A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes..

Background and purpose: Magnetic resonance imaging integrated linear accelerator (MR-Linac) platforms enable acquisition of diffusion weighted imaging (DWI) during treatment providing potential information about treatment response. Obtaining DWI on these platforms is technically different from diagnostic magnetic resonance imaging (MRI) scanners. The aim of this project was to determine feasibility of obtaining DWI and calculating apparent diffusion coefficient (ADC) parameters longitudinally in rectal cancer patients on the MR-Linac. Materials and methods: Nine patients undergoing treatment on MR-Linac had DWI acquired using b-values 0, 30, 150, 500 s/mm2. Gross tumour volume (GTV) and normal tissue was delineated on DWI throughout treatment and median ADC was calculated using an in-house tool (pyOsirix ®). Results: Seven out of nine patients were included in the analysis; all demonstrated downstaging at follow-up. A total of 63 out of 70 DWI were analysed (7 excluded due to poor image quality). An increasing trend of ADC median for GTV (1.15 × 10-3 mm2/s interquartile range (IQ): 1.05-1.17 vs 1.59 × 10-3 mm2/s IQ: 1.37 - 1.64; p = 0.0156), correlating to treatment response. In comparison ADC median for normal tissue remained the same between first and last fraction (1.61 × 10-3 mm2/s IQ: 1.56-1.71 vs 1.67 × 10-3 mm2/s IQ: 1.37-2.00; p = 0.9375). Conclusions: DWI assessment in rectal cancer patients on MR-Linac is feasible. Initial results provide foundations for further studies to determine DWI use for treatment adaptation in rectal cancer..

PURPOSE: To use deep learning to calculate the uncertainty in apparent diffusion coefficient (σADC) voxel-wise measurements to clinically impact the monitoring of treatment response and improve the quality of ADC maps. MATERIALS AND METHODS: We use a uniquely designed diffusion-weighted imaging (DWI) acquisition protocol that provides gold-standard measurements of σADC to train a deep learning model on two separate cohorts: 16 patients with prostate cancer and 28 patients with mesothelioma. Our network was trained with a novel cost function, which incorporates a perception metric and a b-value regularisation term, on ADC maps calculated by combinations of 2 or 3 b-values (e.g. 50/600/900, 50/900, 50/600, 600/900 s/mm2). We compare the accuracy of the deep-learning based approach for estimation of σADC with gold-standard measurements. RESULTS: The model accurately predicted the σADC for every b-value combination in both cohorts. Mean values of σADC within areas of active disease deviated from those measured by the gold-standard by 4.3% (range, 2.87-6.13%) for the prostate and 3.7% (range, 3.06-4.54%) for the mesothelioma cohort. We also showed that the model can easily be adapted for a different DWI protocol and field-of-view with only a few images (as little as a single patient) using transfer learning. CONCLUSION: Deep learning produces maps of σADC from standard clinical diffusion-weighted images (DWI) when 2 or more b-values are available..

BACKGROUND: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. METHODS: 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. FINDINGS: 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. INTERPRETATION: The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. FUNDING: This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316)..

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Background: Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT). Materials and Methods: Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability. Results: For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively). Conclusions: The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change..

OBJECTIVES: Investigate the laboratory, imaging and procedural factors that are associated with a tumour-positive and/or NGS-feasible CT-guided sclerotic bone lesion biopsy result in cancer patients. METHODS: In total, 113 CT-guided bone biopsies performed in cancer patients by an interventional radiologist in one institution were retrospectively reviewed. Sixty-five sclerotic bone biopsies were eventually included and routine blood parameters and tumour marker levels were recorded. Non-contrast (NC) biopsy CTs (65), contrast-enhanced CTs (24), and PET/CTs (22) performed within four weeks of biopsy were reviewed; lesion location, diameter, lesion-to-cortex distance, and NC-CT appearance (dense-sclerosis versus mild-sclerosis) were noted. Mean NC-CT, CE-CT HU, and PET SUVmax were derived from biopsy tract and lesion segmentations. Needle diameter, tract length, and number of samples were noted. Comparisons between tumour-positive/negative and next-generation sequencing (NGS)-feasible/non-feasible biopsies determined significant (p < 0.05) laboratory, imaging, and procedural parameter differences. RESULTS: Seventy-four percent of biopsies were tumour-positive. NGS was feasible in 22/30 prostate cancer patients (73%). Neither laboratory blood parameters, PET/CT availability, size, nor lesion-to-cortex distance affected diagnostic yield or NGS feasibility (p > 0.298). Eighty-seven percent of mildly sclerotic bone (mean 244 HU) biopsies were positive compared with 56% in dense sclerosis (622 HU, p = 0.005) and NC-CT lesion HU was significantly lower in positive biopsies (p = 0.003). A 610 HU threshold yielded 89% PPV for tumour-positive biopsies and a 370 HU threshold 94% PPV for NGS-feasible biopsies. FDG-PET and procedural parameters were non-significant factors (each p > 0.055). CONCLUSION: In cancer patients with sclerotic bone disease, targeting areas of predominantly mild sclerosis in lower CT-attenuation lesions can improve tumour tissue yield and NGS feasibility. KEY POINTS: • Areas of predominantly mild sclerosis should be preferred to areas of predominantly dense sclerosis for CT-guided bone biopsies in cancer patients. • Among sclerotic bone lesions in prostate cancer patients, lesions with a mean HU below 370 should be preferred as biopsy targets to improve NGS feasibility. • Laboratory parameters and procedure related factors may have little implications for CT-guided sclerotic bone biopsy success..

BACKGROUND: Whole-body (WB) MRI, which includes diffusion-weighted imaging (DWI) and T1-w Dixon, permits sensitive detection of marrow disease in addition to qualitative and quantitative measurements of disease and response to treatment of bone marrow. We report on the first study to embed standardised WB-MRI within a prospective, multi-centre myeloma clinical trial (IMAGIMM trial, sub-study of OPTIMUM/MUKnine) to explore the use of WB-MRI to detect minimal residual disease after treatment. METHODS: The standardised MY-RADS WB-MRI protocol was set up on a local 1.5 T scanner. An imaging manual describing the MR protocol, quality assurance/control procedures and data transfer was produced and provided to sites. For non-identical scanners (different vendor or magnet strength), site visits from our physics team were organised to support protocol optimisation. The site qualification process included review of phantom and volunteer data acquired at each site and a teleconference to brief the multidisciplinary team. Image quality of initial patients at each site was assessed. RESULTS: WB-MRI was successfully set up at 12 UK sites involving 3 vendor systems and two field strengths. Four main protocols (1.5 T Siemens, 3 T Siemens, 1.5 T Philips and 3 T GE scanners) were generated. Scanner limitations (hardware and software) and scanning time constraint required protocol modifications for 4 sites. Nevertheless, shared methodology and imaging protocols enabled other centres to obtain images suitable for qualitative and quantitative analysis. CONCLUSIONS: Standardised WB-MRI protocols can be implemented and supported in prospective multi-centre clinical trials. Trial registration NCT03188172 clinicaltrials.gov; registration date 15th June 2017 https://clinicaltrials.gov/ct2/show/study/NCT03188172..

OBJECTIVE: To establish optimised diffusion weightings ('b-values') for acquisition of whole-body diffusion-weighted MRI (WB-DWI) for estimation of the apparent diffusion coefficient (ADC) in patients with metastatic melanoma (MM). Existing recommendations for WB-DWI have not been optimised for the tumour properties in MM; therefore, evaluation of acquisition parameters is essential before embarking on larger studies. METHODS: Retrospective clinical data and phantom experiments were used. Clinical data comprised 125 lesions from 14 examinations in 11 patients with multifocal MM, imaged before and/or after treatment with immunotherapy at a single institution. ADC estimates from these data were applied to a model to estimate the optimum b-value. A large non-diffusing phantom was used to assess eddy current-induced geometric distortion. RESULTS: Considering all tumour sites from pre- and post-treatment examinations together, metastases exhibited a large range of mean ADC values, [0.67-1.49] × 10-3 mm2/s, and the optimum high b-value (bhigh) for ADC estimation was 1100 (10th-90th percentile: 740-1790) s/mm2. At higher b-values, geometric distortion increased, and longer echo times were required, leading to reduced signal. CONCLUSIONS: Theoretical optimisation gave an optimum bhigh of 1100 (10th-90th percentile: 740-1790) s/mm2 for ADC estimation in MM, with the large range of optimum b-values reflecting the wide range of ADC values in these tumours. Geometric distortion and minimum echo time increase at higher b-values and are not included in the theoretical optimisation; bhigh in the range 750-1100 s/mm2 should be adopted to maintain acceptable image quality but performance should be evaluated for a specific scanner. KEY POINTS: • Theoretical optimisation gave an optimum high b-value of 1100 (10th-90th percentile: 740-1790) s/mm2 for ADC estimation in metastatic melanoma. • Considering geometric distortion and minimum echo time (TE), a b-value in the range 750-1100 s/mm2 is recommended. • Sites should evaluate the performance of specific scanners to assess the effect of geometric distortion and minimum TE..

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Purpose To evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study.Materials and methods Separate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability.Results Eighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%.Conclusion Most MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies..

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations..

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Whole-body magnetic resonance imaging (MRI) is now a crucial tool for the assessment of the extent of systemic malignant bone disease and response to treatment, and forms part of national and international recommendations for imaging patients with myeloma or metastatic prostate cancer. Recent developments in scanners have enabled acquisition of good-quality whole-body MRI data within 45 minutes on modern MRI systems from all main manufacturers. This provides complimentary morphological and functional whole-body imaging; however, lack of prior experience and acquisition times required can act as a barrier to adoption in busy radiology departments. This article aims to tackle the former by reviewing the indications and providing guidance for technical delivery and clinical interpretation of whole-body MRI for patients with malignant bone disease..

Purpose: To characterize the voxel-wise uncertainties of Apparent Diffusion Coefficient (ADC) estimation from whole-body diffusion-weighted imaging (WBDWI). This enables the calculation of a new parametric map based on estimates of ADC and ADC uncertainty to improve WBDWI imaging standardization and interpretation: NoIse-Corrected Exponentially-weighted diffusion-weighted MRI (niceDWI). Methods: Three approaches to the joint modeling of voxel-wise ADC and ADC uncertainty (σADC) are evaluated: (i) direct weighted least squares (DWLS), (ii) iterative linear-weighted least-squares (IWLS), and (iii) smoothed IWLS (SIWLS). The statistical properties of these approaches in terms of ADC/σADC accuracy and precision is compared using Monte Carlo simulations. Our proposed post-processing methodology (niceDWI) is evaluated using an ice-water phantom, by comparing the contrast-to-noise ratio (CNR) with conventional exponentially-weighted DWI. We present the clinical feasibility of niceDWI in a pilot cohort of 16 patients with metastatic prostate cancer. Results: The statistical properties of ADC and σADC conformed closely to the theoretical predictions for DWLS, IWLS, and SIWLS fitting routines (a minor bias in parameter estimation is observed with DWLS). Ice-water phantom experiments demonstrated that a range of CNR could be generated using the niceDWI approach, and could improve CNR compared to conventional methods. We successfully implemented the niceDWI technique in our patient cohort, which visually improved the in-plane bias field compared with conventional WBDWI. Conclusions: Measurement of the statistical uncertainty in ADC estimation provides a practical way to standardize WBDWI across different scanners, by providing quantitative image signals that improve its reliability. Our proposed method can overcome inter-scanner and intra-scanner WBDWI signal variations that can confound image interpretation..

Noninvasive early indicators of treatment response are crucial to the successful delivery of precision medicine in children with cancer. Neuroblastoma is a common solid tumor of young children that arises from anomalies in neural crest development. Therapeutic approaches aiming to destabilize MYCN protein, such as small-molecule inhibitors of Aurora A and mTOR, are currently being evaluated in early phase clinical trials in children with high-risk MYCN -driven disease, with limited ability to evaluate conventional pharmacodynamic biomarkers of response. T 1 mapping is an MRI scan that measures the proton spin-lattice relaxation time T 1 . Using a multiparametric MRI-pathologic cross-correlative approach and computational pathology methodologies including a machine learning-based algorithm for the automatic detection and classification of neuroblasts, we show here that T 1 mapping is sensitive to the rich histopathologic heterogeneity of neuroblastoma in the Th- MYCN transgenic model. Regions with high native T 1 corresponded to regions dense in proliferative undifferentiated neuroblasts, whereas regions characterized by low T 1 were rich in apoptotic or differentiating neuroblasts. Reductions in tumor-native T 1 represented a sensitive biomarker of response to treatment-induced apoptosis with two MYCN -targeted small-molecule inhibitors, Aurora A kinase inhibitor alisertib (MLN8237) and mTOR inhibitor vistusertib (AZD2014). Overall, we demonstrate the potential of T 1 mapping, a scan readily available on most clinical MRI scanners, to assess response to therapy and guide clinical trials for children with neuroblastoma. The study reinforces the potential role of MRI-based functional imaging in delivering precision medicine to children with neuroblastoma. SIGNIFICANCE: This study shows that MRI-based functional imaging can detect apoptotic responses to MYCN -targeted small-molecule inhibitors in a genetically engineered murine model of MYCN -driven neuroblastoma..

Background Whole body magnetic resonance imaging (MRI) is now incorporated into international guidance for imaging patients with multiple myeloma. The aim of this study was to investigate inter-observer agreement of triple reported baseline whole-body MRI in myeloma and highlight potential pitfalls.Methods Fifty-seven patients with symptomatic myeloma at first presentation or relapse and planned for autologous stem cell transplant were included. All patients completed baseline whole body MRI within 2 weeks prior to starting treatment. Each scan was reported independently by 3 radiologists using a defined scoring system. Differences in observer scores were compared using analysis of variance (ANOVA) and inter-observer agreement assessed using intra class correlation coefficient (ICC).Results There was no significant difference in mean observer scores for whole skeleton and ICC demonstrated excellent inter-observer agreement at 0.91. ICC varied between skeletal regions with spine, pelvis and ribs showing good inter-observer agreement, whereas skull and long bones were moderate. Scans with variation in observer scores were re-examined and cause of discrepancies identified. This information was used to describe potential anatomical pitfalls in reporting .Conclusion Whole-body MRI has excellent inter-observer agreement in reporting symptomatic myeloma at baseline. Inter-observer agreement varied between skeletal regions highlighting specific areas of difficulty..

Background: Multi-parametric MRI provides non-invasive methods for response assessment of soft-tissue sarcoma (STS) from non-surgical treatments. However, evaluation of MRI parameters over the whole tumor volume may not reveal the full extent of post-treatment changes as STS tumors are often highly heterogeneous, including cellular tumor, fat, necrosis, and cystic tissue compartments. In this pilot study, we investigate the use of machine-learning approaches to automatically delineate tissue compartments in STS, and use this approach to monitor post-radiotherapy changes. Methods: Eighteen patients with retroperitoneal sarcoma were imaged using multi-parametric MRI; 8/18 received a follow-up imaging study 2-4 weeks after pre-operative radiotherapy. Eight commonly-used supervised machine-learning techniques were optimized for classifying pixels into one of five tissue sub-types using an exhaustive cross-validation approach and expert-defined regions of interest as a gold standard. Final pixel classification was smoothed using a Markov Random Field (MRF) prior distribution on the final machine-learning models. Findings: 5/8 machine-learning techniques demonstrated high median cross-validation accuracies (82.2%, range 80.5-82.5%) with no significant difference between these five methods. One technique was selected (Naïve-Bayes) due to its relatively short training and class-prediction times (median 0.73 and 0.69 ms, respectively on a 3.5 GHz personal machine). When combined with the MRF-prior, this approach was successfully applied in all eight post-radiotherapy imaging studies and provided visualization and quantification of changes to independent STS sub-regions following radiotherapy for heterogeneous response assessment. Interpretation: Supervised machine-learning approaches to tissue classification in multi-parametric MRI of soft-tissue sarcomas provide quantitative evaluation of heterogeneous tissue changes following radiotherapy..

Acknowledging the increasingly important role of whole-body MRI for directing patient care in myeloma, a multidisciplinary, international, and expert panel of radiologists, medical physicists, and hematologists with specific expertise in whole-body MRI in myeloma convened to discuss the technical performance standards, merits, and limitations of currently available imaging methods. Following guidance from the International Myeloma Working Group and the National Institute for Clinical Excellence in the United Kingdom, the Myeloma Response Assessment and Diagnosis System (or MY-RADS) imaging recommendations are designed to promote standardization and diminish variations in the acquisition, interpretation, and reporting of whole-body MRI in myeloma and allow response assessment. This consensus proposes a core clinical protocol for whole-body MRI and an extended protocol for advanced assessments. Published under a CC BY 4.0 license. Online supplemental material is available for this article..

Childhood neuroblastoma is a hypervascular tumor of neural origin, for which antiangiogenic drugs are currently being evaluated; however, predictive biomarkers of treatment response, crucial for successful delivery of precision therapeutics, are lacking. We describe an MRI-pathologic cross-correlative approach using intrinsic susceptibility (IS) and susceptibility contrast (SC) MRI to noninvasively map the vascular phenotype in neuroblastoma Th-MYCN transgenic mice treated with the vascular endothelial growth factor receptor inhibitor cediranib. We showed that the transverse MRI relaxation rate R 2 * (second -1 ) and fractional blood volume ( f BV, %) were sensitive imaging biomarkers of hemorrhage and vascular density, respectively, and were also predictive biomarkers of response to cediranib. Comparison with MRI and pathology from patients with MYCN-amplified neuroblastoma confirmed the high degree to which the Th-MYCN model vascular phenotype recapitulated that of the clinical phenotype, thereby supporting further evaluation of IS- and SC-MRI in the clinic. This study reinforces the potential role of functional MRI in delivering precision medicine to children with neuroblastoma. SIGNIFICANCE: This study shows that functional MRI predicts response to vascular-targeted therapy in a genetically engineered murine model of neuroblastoma..

Objective Application of whole body diffusion-weighted MRI (WB-DWI) for oncology are rapidly increasing within both research and routine clinical domains. However, WB-DWI as a quantitative imaging biomarker (QIB) has significantly slower adoption. To date, challenges relating to accuracy and reproducibility, essential criteria for a good QIB, have limited widespread clinical translation. In recognition, a UK workgroup was established in 2016 to provide technical consensus guidelines (to maximise accuracy and reproducibility of WB-MRI QIBs) and accelerate the clinical translation of quantitative WB-DWI applications for oncology.Methods A panel of experts convened from cancer centres around the UK with subspecialty expertise in quantitative imaging and/or the use of WB-MRI with DWI. A formal consensus method was used to obtain consensus agreement regarding best practice. Questions were asked about the appropriateness or otherwise on scanner hardware and software, sequence optimisation, acquisition protocols, reporting, and ongoing quality control programs to monitor precision and accuracy and agreement on quality control.Results The consensus panel was able to reach consensus on 73% (255/351) items and based on consensus areas made recommendations to maximise accuracy and reproducibly of quantitative WB-DWI studies performed at 1.5T. The panel were unable to reach consensus on the majority of items related to quantitative WB-DWI performed at 3T.Conclusion This UK Quantitative WB-DWI Technical Workgroup consensus provides guidance on maximising accuracy and reproducibly of quantitative WB-DWI for oncology. The consensus guidance can be used by researchers and clinicians to harmonise WB-DWI protocols which will accelerate clinical translation of WB-DWI-derived QIBs..

OBJECTIVES:The aim of this study was to identify apparent diffusion coefficient (ADC) values for typical haemangiomas in the spine and to compare them with active malignant focal deposits. METHODS:This was a retrospective single-institution study. Whole-body magnetic resonance imaging (MRI) scans of 106 successive patients with active multiple myeloma, metastatic prostate or breast cancer were analysed. ADC values of typical vertebral haemangiomas and malignant focal deposits were recorded. RESULTS:The ADC of haemangiomas (72 ROIs, median ADC 1,085×10-6mm2s-1, interquartile range 927-1,295×10-6mm2s-1) was significantly higher than the ADC of malignant focal deposits (97 ROIs, median ADC 682×10-6mm2s-1, interquartile range 583-781×10-6mm2s-1) with a p-value < 10-6. Receiver operating characteristic (ROC) analysis produced an area under the curve of 0.93. An ADC threshold of 872×10-6mm2s-1 separated haemangiomas from malignant focal deposits with a sensitivity of 84.7 % and specificity of 91.8 %. CONCLUSIONS:ADC values of classical vertebral haemangiomas are significantly higher than malignant focal deposits. The high ADC of vertebral haemangiomas allows them to be distinguished visually and quantitatively from active sites of disease, which show restricted diffusion. KEY POINTS:• Whole-body diffusion-weighted MRI is becoming widely used in myeloma and bone metastases. • ADC values of vertebral haemangiomas are significantly higher than malignant focal deposits. • High ADCs of haemangiomas allows them to be distinguished from active disease..

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Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18 × 10-3 mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2 = 0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring..

Purpose To determine the usefulness of whole-body diffusion-weighted imaging (DWI) to assess the response of bone metastases to treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). Materials and Methods A phase II prospective clinical trial of the poly-(adenosine diphosphate-ribose) polymerase inhibitor olaparib in mCRPC included a prospective magnetic resonance (MR) imaging substudy; the study was approved by the institutional research board, and written informed consent was obtained. Whole-body DWI was performed at baseline and after 12 weeks of olaparib administration by using 1.5-T MR imaging. Areas of abnormal signal intensity on DWI images in keeping with bone metastases were delineated to derive total diffusion volume (tDV); five target lesions were also evaluated. Associations of changes in volume of bone metastases and median apparent diffusion coefficient (ADC) with response to treatment were assessed by using the Mann-Whitney test and logistic regression; correlation with prostate-specific antigen level and circulating tumor cell count were assessed by using Spearman correlation (r). Results Twenty-one patients were included. All six responders to olaparib showed a decrease in tDV, while no decrease was observed in all nonresponders; this difference between responders and nonresponders was significant (P = .001). Increases in median ADC were associated with increased odds of response (odds ratio, 1.08; 95% confidence interval [CI]: 1.00, 1.15; P = .04). A positive association was detected between changes in tDV and best percentage change in prostate-specific antigen level and circulating tumor cell count (r = 0.63 [95% CI: 0.27, 0.83] and r = 0.77 [95% CI: 0.51, 0.90], respectively). When assessing five target lesions, decreases in volume were associated with response (odds ratio for volume increase, 0.89; 95% CI: 0.80, 0.99; P = .037). Conclusion This pilot study showed that decreases in volume and increases in median ADC of bone metastases assessed with whole-body DWI can potentially be used as indicators of response to olaparib in mCRPC. Online supplemental material is available for this article..

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article..

Purpose To determine the test-retest repeatability of Apparent Diffusion Coefficient (ADC) measurements across institutions and MRI vendors, plus investigate the effect of post-processing methodology on measurement precision.Methods Thirty malignant lung lesions >2 cm in size (23 patients) were scanned on two occasions, using echo-planar-Diffusion-Weighted (DW)-MRI to derive whole-tumour ADC (b = 100, 500 and 800smm^-2). Scanning was performed at 4 institutions (3 MRI vendors). Whole-tumour volumes-of-interest were copied from first visit onto second visit images and from one post-processing platform to an open-source platform, to assess ADC repeatability and cross-platform reproducibility.Results Whole-tumour ADC values ranged from 0.66-1.94x10^-3mm²s^-1 (mean = 1.14). Within-patient coefficient-of-variation (wCV) was 7.1% (95% CI 5.7-9.6%), limits-of-agreement (LoA) -18.0 to 21.9%. Lesions >3 cm had improved repeatability: wCV 3.9% (95% CI 2.9-5.9%); and LoA -10.2 to 11.4%. Variability for lesions <3 cm was 2.46 times higher. ADC reproducibility across different post-processing platforms was excellent: Pearson's R² = 0.99; CoV 2.8% (95% CI 2.3-3.4%); and LoA -7.4 to 8.0%.Conclusion A free-breathing DW-MRI protocol for imaging malignant lung tumours achieved satisfactory within-patient repeatability and was robust to changes in post-processing software, justifying its use in multi-centre trials. For response evaluation in individual patients, a change in ADC >21.9% will reflect treatment-related change.Key points • In lung cancer, free-breathing DWI-MRI produces acceptable images with evaluable ADC measurement. • ADC repeatability coefficient-of-variation is 7.1% for lung tumours >2 cm. • ADC repeatability coefficient-of-variation is 3.9% for lung tumours >3 cm. • ADC measurement precision is unaffected by the post-processing software used. • In multicentre trials, 22% increase in ADC indicates positive treatment response..

Objective The purpose of this article is to review current image acquisition and interpretation for whole-body MRI, clinical applications, and the emerging roles in oncologic imaging, especially in the assessment of bone marrow diseases.Conclusion Whole-body MRI is an emerging technique used for early diagnosis, staging, and assessment of therapeutic response in oncology. The improved accessibility and advances in technology, including widely available sequences (Dixon and DWI), have accelerated its deployment and acceptance in clinical practice..

Quantitative whole-body diffusion-weighted MRI (WB-DWI) is now possible using semi-automatic segmentation techniques. The method enables whole-body estimates of global Apparent Diffusion Coefficient (gADC) and total Diffusion Volume (tDV), both of which have demonstrated considerable utility for assessing treatment response in patients with bone metastases from primary prostate and breast cancers. Here we investigate the agreement (inter-observer repeatability) between two radiologists in their definition of Volumes Of Interest (VOIs) and subsequent assessment of tDV and gADC on an exploratory patient cohort of nine. Furthermore, each radiologist was asked to repeat his or her measurements on the same patient data sets one month later to identify the intra-observer repeatability of the technique. Using a Markov Chain Monte Carlo (MCMC) estimation method provided full posterior probabilities of repeatability measures along with maximum a-posteriori values and 95% confidence intervals. Our estimates of the inter-observer Intraclass Correlation Coefficient (ICCinter) for log-tDV and median gADC were 1.00 (0.97-1.00) and 0.99 (0.89-0.99) respectively, indicating excellent observer agreement for these metrics. Mean gADC values were found to have ICCinter = 0.97 (0.81-0.99) indicating a slight sensitivity to outliers in the derived distributions of gADC. Of the higher order gADC statistics, skewness was demonstrated to have good inter-user agreement with ICCinter = 0.99 (0.86-1.00), whereas gADC variance and kurtosis performed relatively poorly: 0.89 (0.39-0.97) and 0.96 (0.69-0.99) respectively. Estimates of intra-observer repeatability (ICCintra) demonstrated similar results: 0.99 (0.95-1.00) for log-tDV, 0.98 (0.89-0.99) and 0.97 (0.83-0.99) for median and mean gADC respectively, 0.64 (0.25-0.88) for gADC variance, 0.85 (0.57-0.95) for gADC skewness and 0.85 (0.57-0.95) for gADC kurtosis. Further investigation of two anomalous patient cases revealed that a very small proportion of voxels with outlying gADC values lead to instability in higher order gADC statistics. We therefore conclude that estimates of median/mean gADC and tumour volume demonstrate excellent inter- and intra-observer repeatability whilst higher order statistics of gADC should be used with caution when ascribing significance to clinical changes..

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Purpose To determine the correlation between the volume of bone metastasis as assessed with diffusion-weighted (DW) imaging and established prognostic factors in metastatic castration-resistant prostate cancer (mCRPC) and the association with overall survival (OS). Materials and Methods This retrospective study was approved by the institutional review board; informed consent was obtained from all patients. The authors analyzed whole-body DW images obtained between June 2010 and February 2013 in 53 patients with mCRPC at the time of starting a new line of anticancer therapy. Bone metastases were identified and delineated on whole-body DW images in 43 eligible patients. Total tumor diffusion volume (tDV) was correlated with the bone scan index (BSI) and other prognostic factors by using the Pearson correlation coefficient (r). Survival analysis was performed with Kaplan-Meier analysis and Cox regression. Results The median tDV was 503.1 mL (range, 5.6-2242 mL), and the median OS was 12.9 months (95% confidence interval [CI]: 8.7, 16.1 months). There was a significant correlation between tDV and established prognostic factors, including hemoglobin level (r = -0.521, P < .001), prostate-specific antigen level (r = 0.556, P < .001), lactate dehydrogenase level (r = 0.534, P < .001), alkaline phosphatase level (r = 0.572, P < .001), circulating tumor cell count (r = 0.613, P = .004), and BSI (r = 0.565, P = .001). A higher tDV also showed a significant association with poorer OS (hazard ratio, 1.74; 95% CI: 1.02, 2.96; P = .035). Conclusion Metastatic bone disease from mCRPC can be evaluated and quantified with whole-body DW imaging. Whole-body DW imaging-generated tDV showed correlation with established prognostic biomarkers and is associated with OS in mCRPC. (©) RSNA, 2016 Online supplemental material is available for this article..

Purpose To introduce T 2 -adjusted computed DWI (T 2 -cDWI), a method that provides synthetic images at arbitrary b-values and echo times (TEs) that improve tissue contrast by removing or increasing T 2 contrast in diffusion-weighted images. Materials and methods In addition to the standard DWI acquisition protocol T 2 -weighted echo-planar images at multiple (≥2) echo times were acquired. This allows voxelwise estimation of apparent diffusion coefficient (ADC) and T 2 values, permitting synthetic images to be generated at any chosen b-value and echo time. An analytical model is derived for the noise properties in T 2 -cDWI, and validated using a diffusion test-object. Furthermore, we present T 2 -cDWI in two example clinical case studies: (i) a patient with mesothelioma demonstrating multiple disease tissue compartments and (ii) a patient with primary ovarian cancer demonstrating solid and cystic disease compartments. Results Measured image noise in T 2 -cDWI from phantom experiments conformed to the analytical model and demonstrated that T 2 -cDWI at high computed b-value/TE combinations achieves lower noise compared with conventional DWI. In patients, T 2 -cDWI with low b-value and long TE enhanced fluid signal while suppressing solid tumour components. Conversely, large b-values and short TEs overcome T 2 shine-through effects and increase the contrast between tumour and fluid compared with conventional high-b-value DW images. Conclusion T 2 -cDWI is a promising clinical tool for improving image signal-to-noise, image contrast, and tumour detection through suppression of T 2 shine-through effects..

A novel post-processing methodology able to assess whole-body tumor heterogeneity in patients with metastatic disease is proposed. The method is demonstrated on paired pre- and post-treatment data sets obtained from an initial cohort of six patients with metastatic disease from primary prostate or ovarian cancers. Whole-body diffusion-weighted imaging and T1-weighted contrast-enhanced imaging data were acquired covering the chest, abdomen, and pelvis. Joint histograms of Apparent Diffusion Coefficient and Fractional Enhancement values were calculated within volumes of interest and were modeled as a Gaussian mixture of two classes. Probability maps and volumetric estimates of the magnetic resonance data-derived classes providing visualization of pre- and post-treatment data are shown in three patient examples. This technique provided spatially heterogeneous characterization of regions following treatment as defined by the combined analysis of apparent diffusion coefficient and fractional enhancement. A new whole-body magnetic resonance data analysis has been demonstrated enabling visualization of intra-patient response heterogeneity in patients with metastatic cancer. Changes in the parameters of each subpopulation derived from this technique (apparent diffusion coefficient and fractional enhancement) reflect changes in the tissue properties of each subpopulation following treatment. Furthermore, the volume change of each population can be quantified. Such techniques may be essential for personalized anti-cancer therapy where there is a need to detect early drug-resistance and monitor heterogeneous response. .

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We describe our semi-automatic segmentation of whole-body diffusion-weighted MRI (WBDWI) using a Markov random field (MRF) model to derive tumor total diffusion volume (tDV) and associated global apparent diffusion coefficient (gADC); and demonstrate the feasibility of using these indices for assessing tumor burden and response to treatment in patients with bone metastases. WBDWI was performed on eleven patients diagnosed with bone metastases from breast and prostate cancers before and after anti-cancer therapies. Semi-automatic segmentation incorporating a MRF model was performed in all patients below the C4 vertebra by an experienced radiologist with over eight years of clinical experience in body DWI. Changes in tDV and gADC distributions were compared with overall response determined by all imaging, tumor markers and clinical findings at serial follow up. The segmentation technique was possible in all patients although erroneous volumes of interest were generated in one patient because of poor fat suppression in the pelvis, requiring manual correction. Responding patients showed a larger increase in gADC (median change = +0.18, range = -0.07 to +0.78 × 10(-3) mm2/s) after treatment compared to non-responding patients (median change = -0.02, range = -0.10 to +0.05 × 10(-3) mm2/s, p = 0.05, Mann-Whitney test), whereas non-responding patients showed a significantly larger increase in tDV (median change = +26%, range = +3 to +284%) compared to responding patients (median change = -50%, range = -85 to +27%, p = 0.02, Mann-Whitney test). Semi-automatic segmentation of WBDWI is feasible for metastatic bone disease in this pilot cohort of 11 patients, and could be used to quantify tumor total diffusion volume and median global ADC for assessing response to treatment..

OBJECTIVE: To determine the measurement reproducibility of perfusion fraction f, pseudodiffusion coefficient D and diffusion coefficient D in colorectal liver metastases and normal liver. METHODS: Fourteen patients with known colorectal liver metastases were examined twice using respiratory-triggered echo-planar DW-MRI with eight b values (0 to 900 s/mm(2)) 1 h apart. Regions of interests were drawn around target metastasis and normal liver in each patient to derive ADC (all b values), ADC(high) (b values ≥ 100 s/mm(2)) and intravoxel incoherent motion (IVIM) parameters f, D and D by least squares data fitting. Short-term measurement reproducibility of median ADC, ADC(high), f, D and D values were derived from Bland-Altman analysis. RESULTS: The measurement reproducibility for ADC, ADC(high) and D was worst in colorectal liver metastases (-21 % to +25 %) compared with liver parenchyma (-6 % to +8 %). Poor measurement reproducibility was observed for the perfusion-sensitive parameters of f (-75 % to +241 %) and D (-89 % to +2,120 %) in metastases, and to a lesser extent the f (-24 % to +25 %) and D (-31 % to +59 %) of liver. CONCLUSIONS: Estimates of f and D derived from the widely used least squares IVIM fitting showed poor measurement reproducibility. Efforts should be made to improve the measurement reproducibility of perfusion-sensitive IVIM parameters..

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OBJECTIVE: We examine the clinical impetus for whole-body diffusion-weighted MRI and discuss how to implement the technique with clinical MRI systems. We include practical tips and tricks to optimize image quality and reduce artifacts. The interpretative pitfalls are enumerated, and potential challenges are highlighted. CONCLUSION: Whole-body diffusion-weighted MRI can be used for tumor staging and assessment of treatment response. Meticulous technique and knowledge of potential interpretive pitfalls will help to avoid mistakes and establish this modality in radiologic practice..

OBJECTIVES: Silicone breast prostheses prove technically challenging when performing diffusion-weighted MR imaging in the breasts. We describe a combined fat and chemical suppression scheme to achieve dual suppression of fat and silicone, thereby improving the quality of diffusion-weighted images in women with breast implants. METHODS: MR imaging was performed at 3.0 and 1.5 T in women with silicone breast implants using short-tau inversion recovery (STIR) fat-suppressed echo-planar (EPI) diffusion-weighted MR imaging (DWI) on its own and combined with the slice-select gradient-reversal (SSGR) technique. Imaging was performed using dedicated breast imaging coils. RESULTS: Complete suppression of the fat and silicone signal was possible at 3.0 T using EPI DWI with STIR and SSGR, evaluated with dedicated breast coils. However, a residual silicone signal was still perceptible at 1.5 T using this combined approach. Nevertheless, a further reduction in silicone signal at 1.5 T could be achieved by employing thinner slice partitions and the addition of the chemical-selective fat-suppression (CHESS) technique. CONCLUSIONS: DWI using combined STIR and SSGR chemical suppression techniques is feasible to eliminate or reduce silicone signal from prosthetic breast implants. KEY POINTS: Breast magnetic resonance imaging (MRI) is frequently needed following breast implants. Unsuppressed signal from silicone creates artefacts on diffusion-weighted MR sequences. Dual fat/chemical suppression can eliminate signal from fat and silicone. STIR with slice selective gradient reversal can suppress fat and silicone signal..

PURPOSE: To describe computed diffusion weighted (DW) magnetic resonance (MR) imaging as a method for obtaining high-b-value images from DW MR imaging performed at lower b values and to investigate the feasibility of the technique to improve lesion detection in oncologic cases. MATERIALS AND METHODS: The study was approved by the institutional and research committee, and written informed consent was obtained from all patients. DW MR imaging was performed on a CuSO(4) phantom at 1.5 T with a range of b values and compared with computed DW MR imaging images synthesized from lower b values (0 and 600 sec/mm(2)). The signal-to-noise ratio (SNR) was compared, and agreement between the SNR of computed DW MR imaging and theoretical estimation assessed. Computed DW MR imaging was evaluated in 10 oncologic patients who underwent whole-body DW MR imaging with b values of 0 and 900 sec/mm(2). Computed DW MR images at computed b values of 1500 and 2000 sec/mm(2) were generated. The image quality and background suppression of acquired and computed images were rated by a radiologist using a four-point scale. The diagnostic performance for malignant lesion detection using these images was evaluated and compared by using the McNemar Test. RESULTS: The SNR of computed DW MR imaging of the phantom conformed closely to theoretical predictions. Computed DW MR imaging resulted in a higher SNR compared with acquired DW MR imaging, especially at b values greater than 840 sec/mm(2). In patients, images with a computed b value of 2000 sec/mm(2) produced good image quality and high background suppression (mean scores of 2.8 and 4.0, respectively). Evaluation of images with a computed b value of 2000 sec/mm(2) resulted in higher overall diagnostic sensitivity (96.0%) and specificity (96.6%) compared with images with an acquired b value of 900 sec/mm(2) (sensitivity, 89.4%; specificity, 87.5%; P < .01). CONCLUSION: Computed DW MR imaging in the body allows higher-b-value images to be obtained with a good SNR. Clinical computed DW MR imaging is feasible and may improve disease detection. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101919/-/DC1..

OBJECTIVE: To compare geometric distortion, signal-to-noise ratio (SNR), apparent diffusion coefficient (ADC), efficacy of fat suppression and presence of artefact between monopolar (Stejskal and Tanner) and bipolar (twice-refocused, eddy-current-compensating) diffusion-weighted imaging (DWI) sequences in the abdomen and pelvis. MATERIALS AND METHODS: A semiquantitative distortion index (DI) was derived from the subtraction images with b = 0 and 1,000 s/mm(2) in a phantom and compared between the two sequences. Seven subjects were imaged with both sequences using four b values (0, 600, 900 and 1,050 s/mm(2)) and SNR, ADC for different organs and fat-to-muscle signal ratio (FMR) were compared. Image quality was evaluated by two radiologists on a 5-point scale. RESULTS: DI was improved in the bipolar sequence, indicating less geometric distortion. SNR was significantly lower for all tissues and b values in the bipolar images compared with the monopolar (p < 0.05), whereas FMR was not statistically different. ADC in liver, kidney and sacrum was higher in the bipolar scheme compared to the monopolar (p < 0.03), whereas in muscle it was lower (p = 0.018). Image quality scores were higher for the bipolar sequence (p ≤ 0.025). CONCLUSION: Artefact reduction makes the bipolar DWI sequence preferable in abdominopelvic applications, although the trade-off in SNR may compromise ADC measurements in muscle..

The purpose was to determine the reproducibility of apparent diffusion coefficient (ADC) measurements in a two-centre phase I clinical trial; and to track ADC changes in response to the sequential administration of the vascular disrupting agent, combretastatin A4 phosphate (CA4P), and the anti-angiogenic drug, bevacizumab. Sixteen patients with solid tumours received CA4P and bevacizumab treatment. Echo-planar diffusion-weighted MRI was performed using six b values (b = 0-750 s/mm(2)) before (x2), and at 3 and 72 h after a first dose of CA4P. Bevacizumab was given 4 h after a second dose of CA4P, and imaging performed 3 h post CA4P and 72 h after bevacizumab treatment. The coefficient of repeatability (r) of ADC total (all b values), ADC high (b = 100-750) and ADC low (b = 0-100) was calculated by Bland-Altman analysis. The ADC total and ADC high showed good measurement reproducibility (r% = 13.3, 14.1). There was poor reproducibility of the perfusion-sensitive ADC low (r% = 62.5). Significant increases in the median ADC total and ADC high occurred at 3 h after the second dose of CA4P (p < 0.05). ADC measurements were highly reproducible in a two-centre clinical trial setting and appear promising for evaluating the effects of drugs that target tumour vasculature..

Whole-body MRI (WB-MRI) has evolved since its first introduction in the 1970s as an imaging technique to detect and survey disease across multiple sites and organ systems in the body. The development of diffusion-weighted MRI (DWI) has added a new dimension to the implementation of WB-MRI on modern scanners, offering excellent lesion-to-background contrast, while achieving acceptable spatial resolution to detect focal lesions 5 to 10 mm in size. MRI hardware and software advances have reduced acquisition times, with studies taking 40-50 min to complete.The rising awareness of medical radiation exposure coupled with the advantages of MRI has resulted in increased utilization of WB-MRI in oncology, paediatrics, rheumatological and musculoskeletal conditions and more recently in population screening. There is recognition that WB-MRI can be used to track disease evolution and monitor response heterogeneity in patients with cancer. There are also opportunities to combine WB-MRI with molecular imaging on PET-MRI systems to harness the strengths of hybrid imaging. The advent of artificial intelligence and machine learning will shorten image acquisition times and image analyses, making the technique more competitive against other imaging technologies..

Objective To assess intra- and inter-reader variability of apparent diffusion coefficient (ADC) and fat fraction (FF) measurement in focal myeloma bone lesions and the influence of lesion size.Methods 22 myeloma patients with focal active disease on whole body MRI were included. Two readers outlined a small (5-10 mm) and large lesion (>10 mm) in each subject on derived ADC and FF maps; one reader performed this twice. Intra- and inter-reader agreement for small and large lesion groups were calculated for derived statistics from each map using within-subject standard deviation, coefficient of variation, interclass correlation coefficient measures, and visualized with Bland-Altman plots.Results For mean ADC, intra- and inter-reader repeatability demonstrated equivalently low coefficient of variation (3.0-3.6%) and excellent interclass correlation coefficient (0.975-0.982) for both small and large lesions. For mean FF, intra- and inter-reader repeatability was significantly poorer for small lesions compared to large lesions (intra-reader within-subject standard variation estimate is 2.7 times higher for small lesions than large lesions ( p = 0.0071), and for inter-reader variations is 3.8 times higher ( p = 0.0070)).Conclusion There is excellent intra- and inter-reader agreement for mean ADC estimates, even for lesions as small as 5 mm. For FF measurements, there is a significant increase in coefficient of variation for smaller lesions, suggesting lesions >10 mm should be selected for lesion FF measurement.Advances in knowledge ADC measurements of focal myeloma have excellent intra- and inter-reader agreement. FF measurements are more susceptible to lesion size as intra- and inter-reader agreement is significantly impaired in lesions less than 10 mm..

Purpose To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA 1 ]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times. Materials and methods Both retrospective and prospective patient groups were used to develop a deep learning-based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA 1 and NOA 9 images (acquisition period, 2015-2017) were retrospectively included. An additional 22 prospective patients with advanced prostate cancer, myeloma, and advanced breast cancer were used for model testing (2019), and the radiologic quality of DNIF-processed NOA 1 (NOA 1-DNIF ) images were compared with NOA 1 images and clinical NOA 16 images by using a three-point Likert scale (good, average, or poor; statistical significance was calculated by using a Wilcoxon signed ranked test). The model was also retrained and tested in 28 patients with malignant pleural mesothelioma (MPM) who underwent lung MRI (2015-2017) to demonstrate feasibility in other body regions. Results The model visually improved the quality of NOA 1 images in all test patients, with the majority of NOA 1-DNIF and NOA 16 images being graded as either "average" or "good" across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA 1-DNIF images of bone disease deviated from those within NOA 9 images by an average of 1.9% (range, 1.1%-2.6%). The model was also successfully applied in the context of MPM; the mean ADCs from NOA 1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA 12 ) by 3.7% (range, 0.2%-10.6%). Conclusion Clinical-standard images were generated from subsampled images by using a DNIF. Keywords: Image Postprocessing, MR-Diffusion-weighted Imaging, Neural Networks, Oncology, Whole-Body Imaging, Supervised Learning, MR-Functional Imaging, Metastases, Prostate, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license..

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Background Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T 1 -weighted MRI from a pre-existing repository of clinical planning CTs.Methods MRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic T 1 weighted MRI scans.Results In our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues (e.g. bladder, bowel). Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test cases.Conclusion Pelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models..