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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.

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.

Background To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models. Methods This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models’ net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC). Results In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only. Conclusions The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy.Scientific Literature databases were used to search studies published from January 2007 to October 2023.All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included.Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score.From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72–0.92 and 0.69–0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias.After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.Critical relevant statementThe use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.Protocol of systematic review registrationPROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342.Key PointsRadiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance.The combined models performed better than radiomics signatures alone for detecting extracapsular extension.Radiomics are not yet reliable for extracapsular detection in PCa patients.

Background The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability. Methods Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds. Results Classification performance was significant ( p  < 0.05, H 0 :AUROC = 0.5) for 11 of 20 targets using either pipeline and for these targets the AUROCs were within ± 0.05 for the two pipelines, except for one target where the Proposed pipeline performance increased by > 0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage. Conclusions Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance. Trial registration NCT03226886 (TRACERx Renal)

Background Diffusion weighted imaging (DWI) with intravoxel incoherent motion (IVIM) modelling can inform on tissue perfusion without exogenous contrast administration. Dynamic-contrast-enhanced (DCE) MRI can also characterise tissue perfusion, but requires a bolus injection of a Gadolinium-based contrast agent. This study compares the use of DCE-MRI and IVIM-DWI methods in assessing response to anti-angiogenic treatment in patients with colorectal liver metastases in a cohort with confirmed treatment response. Methods This prospective imaging study enrolled 25 participants with colorectal liver metastases to receive Regorafenib treatment. A target metastasis > 2 cm in each patient was imaged before and at 15 days after treatment on a 1.5T MR scanner using slice-matched IVIM-DWI and DCE-MRI protocols. MRI data were motion-corrected and tumour volumes of interest drawn on b=900 s/mm 2 diffusion-weighted images were transferred to DCE-MRI data for further analysis. The median value of four IVIM-DWI parameters [diffusion coefficient D (10 −3 mm 2 /s), perfusion fraction f (ml/ml), pseudodiffusion coefficient D* (10 −3 mm 2 /s), and their product fD* (mm 2 /s)] and three DCE-MRI parameters [volume transfer constant K trans (min −1 ), enhancement fraction EF (%), and their product KEF (min −1 )] were recorded at each visit, before and after treatment. Changes in pre- and post-treatment measurements of all MR parameters were assessed using Wilcoxon signed-rank tests ( P <0.05 was considered significant). DCE-MRI and IVIM-DWI parameter correlations were evaluated with Spearman rank tests. Functional MR parameters were also compared against Response Evaluation Criteria In Solid Tumours v.1.1 (RECIST) evaluations. Results Significant treatment-induced reductions of DCE-MRI parameters across the cohort were observed for EF (91.2 to 50.8%, P <0.001), KEF (0.095 to 0.045 min −1 , P <0.001) and K trans (0.109 to 0.078 min −1 , P =0.002). For IVIM-DWI, only D (a non-perfusion parameter) increased significantly post treatment (0.83 to 0.97 × 10 −3 mm 2 /s, P <0.001), while perfusion-related parameters showed no change. No strong correlations were found between DCE-MRI and IVIM-DWI parameters. A moderate correlation was found, after treatment, between K trans and D* ( r =0.60; P =0.002) and fD* ( r =0.67; P <0.001). When compared to RECIST v.1.1 evaluations, KEF and D correctly identified most clinical responders, whilst non-responders were incorrectly identified. Conclusion IVIM-DWI perfusion-related parameters showed limited sensitivity to the anti-angiogenic effects of Regorafenib treatment in colorectal liver metastases and showed low correlation with DCE-MRI parameters, despite profound and significant post-treatment reductions in DCE-MRI measurements. Trial registration NCT03010722 clinicaltrials.gov; registration date 6 th January 2015.

Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27–89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33–77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research.

Objectives MR fingerprinting (MRF) has the potential to quantify treatment response. This study evaluated the repeatability of MRF-derived T 1 and T 2 relaxation times in bone metastasis, bone, and muscle in patients with metastatic prostate cancer. Materials and methods This prospective single-centre study included same-day repeated MRF acquisitions from 20 patients (August 2019–October 2020). Phantom and human data were acquired on a 1.5-T MR scanner using a research MRF sequence outputting T 1 and T 2 maps. Regions of interest (ROIs) across three tissue types (bone metastasis, bone, muscle) were drawn on two separate acquisitions. Repeatability of T 1 and T 2 was assessed using Bland-Altman plots, together with repeatability (r) and intraclass correlation (ICC) coefficients. Mean T 1 and T 2 were reported per tissue type. Results Twenty patients with metastatic prostate cancer (mean age, 70 years ± 8 (standard deviation)) were evaluated and bone metastasis ( n  = 44), normal-appearing bone ( n  = 14), and muscle ( n  = 20) ROIs were delineated. Relative repeatability of T 1 measurements was 6.9% (bone metastasis), 32.6% (bone), 5.8% (muscle) and 21.8%, 32.2%, 16.1% for T 2 measurements. The ICC of T 1 was 0.97 (bone metastasis), 0.94 (bone), 0.96 (muscle); ICC of T 2 was 0.94 (bone metastasis), 0.94 (bone), 0.91 (muscle). T 1 values in bone metastasis were higher than in bone ( p  < 0.001). T 2 values showed no difference between bone metastasis and bone ( p  = 0.5), but could separate active versus treated metastasis ( p  < 0.001). Conclusion MRF allows repeatable T 1 and T 2 measurements in bone metastasis, bone, and muscle in patients with primary prostate cancer. Such measurements may help quantify the treatment response of bone metastasis. Key Points Question MR fingerprinting has the potential to characterise bone metastasis and its response to treatment. Findings Repeatability of MRF-based T 1 measurements in bone metastasis and muscle was better than for T 2 . Clinical relevance MR fingerprinting allows repeatable T 1 and T 2 quantitative measurements in bone metastasis, bone, and muscle in patients with primary prostate cancer, which makes it potentially applicable for disease characterisation and assessment of treatment response.

Objectives Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) acquisition and advanced processing can accelerate acquisition time and improve MR image quality. This study evaluated the image quality and apparent diffusion coefficient (ADC) measurements of free-breathing DWI acquired from patients with liver metastases using a prototype SMS-DWI acquisition (with/without an advanced processing option) and conventional DWI. Methods Four DWI schemes were compared in a pilot 5-patient cohort; three DWI schemes were further assessed in a 24-patient cohort. Two readers scored image quality of all b -value images and ADC maps across the three methods. ADC measurements were performed, for all three methods, in left and right liver parenchyma, spleen, and liver metastases. The Friedman non-parametric test (post-hoc Wilcoxon test with Bonferroni correction) was used to compare image quality scoring; t -test was used for ADC comparisons. Results SMS-DWI was faster (by 24%) than conventional DWI. Both readers scored the SMS-DWI with advanced processing as having the best image quality for highest b -value images (b750) and ADC maps; Cohen’s kappa inter-reader agreement was 0.6 for b750 image and 0.56 for ADC maps. The prototype SMS-DWI sequence with advanced processing allowed a better visualization of the left lobe of the liver. ADC measured in liver parenchyma, spleen, and liver metastases using the SMS-DWI with advanced processing option showed lower values than those derived from the SMS-DWI method alone ( t -test, p  < 0.0001; p  < 0.0001; p  = 0.002). Conclusions Free-breathing SMS-DWI with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients. Clinical relevance statement Free-breathing simultaneous multi-slice- diffusion-weighted imaging (DWI) with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients. Key Points • Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) can accelerate acquisition time and improve image quality. • Apparent diffusion coefficients (ADC) measured in liver parenchyma, spleen, and liver metastases using the simultaneous multi-slice DWI with advanced processing were significantly lower than those derived from the simultaneous multi-slice DWI method alone. • Simultaneous multi-slice DWI sequence with inline advanced processing was faster and demonstrated better image quality in liver patients.

Objectives Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours. Methods Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm -2 ). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively. Results Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models. Conclusions Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours. Key Points • Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours . • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours . • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types . • Micro-structural features are likely to affect parameters in non-mono-exponential models differently .