PURPOSE:This systematic review and meta-analysis compared the diagnostic performance of MRI, [ 18 F]FDG-PET/CT, and [ 18 F]FDG-PET/MRI in detecting focal bone lesions and bone marrow infiltration in the initial staging of patients with multiple myeloma (MM) who underwent both MRI and [ 18 F]FDG-PET/CT or [ 18 F]FDG-PET/MRI studies. PATIENTS AND METHODS:A systematic literature search was conducted across PubMed, Embase, and Cochrane databases, including studies comparing the performance of MRI (WB-MRI or spine/pelvis MRI), [ 18 F]FDG-PET/CT, and/or [ 18 F]FDG-PET/MRI in the same patients for MM initial staging. Pooled sensitivities and concordance between imaging modalities were analyzed using R (package META-R). Heterogeneity and bias were assessed with the QUADAS-C tool. RESULTS:Twenty studies (published between 2007 and 2025) using the international MM diagnostic criteria as a reference standard met the inclusion criteria. Of these, 13 (n=742) compared per-patient sensitivity of [ 18 F]FDG-PET/CT and MRI, and 4 (n=224) compared MRI and [ 18 F]FDG-PET/MRI. Pooled sensitivities were 0.807 (95% CI: 0.74-0.86) for [ 18 F]FDG-PET/CT (with significant heterogeneity) versus 0.914 (95% CI: 0.88-0.94) for MRI (0.906 for spine/pelvis MRI and 0.920 for WB-MRI) ( P <0.001 for meta-regression analysis). Using contingency tables, 83% (599/721) of included patients had concordant [ 18 F]FDG-PET/CT and MRI results, while 14% (101/721) patients had negative [ 18 F]FDG-PET/CT and positive MRI with significant differences between the 2 techniques for the paired sample analysis ( P <0.001). The pooled sensitivity of the 4 studies including [ 18 F]FDG-PET/MRI was 0.944 (95% CI: 0.88-0.98). Consensus definitions for specificity in MM imaging should be standardized across studies. CONCLUSIONS:This systematic review and comparative meta-analysis demonstrates superior sensitivity of WB-MRI compared with [ 18 F]FDG-PET/CT for initial staging of MM patients. Future international guidelines might prioritize MRI and [ 18 F]FDG-PET/MRI for staging of MM patients. REGISTRATION:PROSPERO CRD42024564937.

OBJECTIVES:Clinical translation of advanced MRI techniques can be hindered by the challenges of performing standardized multicentre imaging trials. This work aims to develop and demonstrate an automated tool for monitoring imaging protocol deviations, enabling corrective action to be taken. METHODS:A Python-based tool, integrated into the imaging repository XNAT, was developed to compare DICOM series with an agreed imaging protocol, highlighting missing series and parameter deviations. This was demonstrated through retrospective analysis of a prospectively acquired dataset from a ten-site whole-body (WB) MRI study of patients with multiple myeloma. The acquired data were compared to the relevant radiological guidelines and to the site-specific imaging protocols agreed for the study. RESULTS:The rate of technical software failure was 0% across 174 examinations from 10 sites. The clinical guidelines were followed in 87.9% of examinations and compliance with the site-specific imaging protocol was greater than 75.0% for all parameters. Common deviations included number of averages for diffusion-weighted imaging (DWI) and repetition time for DWI and Dixon: 85.2%, 81.7%, and 75.1%, respectively. There was a statistically significant correlation between protocol compliance and overall exam radiological image quality. CONCLUSIONS:Repository-integrated software is presented for automated monitoring of imaging protocol compliance to support standardization in multicentre studies and clinical translation. ADVANCES IN KNOWLEDGE:This study presents a novel open-source repository-integrated software tool for automatically monitoring compliance with the expected imaging protocol. Standardized acquisition protocols are crucial in multicentre imaging studies and this tool has the potential to enhance research outcomes and support clinical translation.

This systematic review and meta-analysis compared the diagnostic performance of MRI, [18F]FDG-PET/CT, and [18F]FDG-PET/MRI in detecting focal bone lesions and bone marrow infiltration in the initial staging of patients with multiple myeloma (MM) who underwent both MRI and [18F]FDG-PET/CT or [18F]FDG-PET/MRI studies. A systematic literature search was conducted across PubMed, Embase, and Cochrane databases, including studies comparing the performance of MRI (WB-MRI or spine/pelvis MRI), [18F]FDG-PET/CT, and/or [18F]FDG-PET/MRI in the same patients for MM initial staging. Pooled sensitivities and concordance between imaging modalities were analyzed using R (package META-R). Heterogeneity and bias were assessed with the QUADAS-C tool. Twenty studies (published between 2007 and 2025) using the international MM diagnostic criteria as a reference standard met the inclusion criteria. Of these, 13 (n=742) compared per-patient sensitivity of [18F]FDG-PET/CT and MRI, and 4 (n=224) compared MRI and [18F]FDG-PET/MRI. Pooled sensitivities were 0.807 (95% CI: 0.74–0.86) for [18F]FDG-PET/CT (with significant heterogeneity) versus 0.914 (95% CI: 0.88–0.94) for MRI (0.906 for spine/pelvis MRI and 0.920 for WB-MRI) (P<0.001 for meta-regression analysis). Using contingency tables, 83% (599/721) of included patients had concordant [18F]FDG-PET/CT and MRI results, while 14% (101/721) patients had negative [18F]FDG-PET/CT and positive MRI with significant differences between the 2 techniques for the paired sample analysis (P<0.001). The pooled sensitivity of the 4 studies including [18F]FDG-PET/MRI was 0.944 (95% CI: 0.88–0.98). Consensus definitions for specificity in MM imaging should be standardized across studies. This systematic review and comparative meta-analysis demonstrates superior sensitivity of WB-MRI compared with [18F]FDG-PET/CT for initial staging of MM patients. Future international guidelines might prioritize MRI and [18F]FDG-PET/MRI for staging of MM patients. PROSPERO CRD42024564937.

Generative AI has the potential to significantly mitigate data privacy and security risks by enabling the sharing of synthetic images containing features of cancer diseases across multiple cancer care centres. In this work, we focused on training a conditional denoising diffusion probabilistic model (DDPM) to generates synthetic images of Maximum Intensity Projection (MIP) of Diffusion-weighted Images (DWI) in different rotational views. Our model attains 40.30 of FID score. Furthermore, the visual dissimilarity between synthetic samples and their closest raw data per Mean Square Error indicates the absence of mode collapse. An expert radiologist achieved an accuracy rate of 52

To compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI). Fifty patients undergoing gadoxetate-enhanced MRI were recruited. Two T1-weighted Dixon sequences were utilized: a 17-s breath-hold acquisition and an accelerated 12-s breath-hold acquisition (reduced phase resolution), both reconstructed using neural network (NN) and iterative denoising (ID), NN-alone, ID-alone, and the standard method. Contrast-to-noise ratio (CNR) was assessed quantitatively for all series (ANOVA). Two blinded radiologists independently analyzed three image sets: 17-s acquisition reconstructed with NN and ID (17-s NN + ID), 12-s acquisition reconstructed with NN and ID (12-s NN + ID), and 17-s acquisition with standard reconstruction (17-s standard). Overall image quality, qualitative CNR, lesion edge sharpness, vessel edge sharpness, and respiratory motion artifacts were scored (4-point Likert scale) and compared (Friedman test). Lesion detection was compared between 12-s NN + ID and 17-s standard reconstructions (Wilcoxon signed-rank test). Quantitative liver-to-portal vein CNR was significantly higher for 17-s NN + ID than 17-s standard or 17-s NN-alone images (p = 0.001). Scores for overall image quality, qualitative CNR, vessel edge sharpness, and lesion edge sharpness were significantly higher for 17-s NN + ID and 12-s NN + ID than standard reconstruction (p < 0.001); there was no significant difference between 17-s and 12-s NN + ID. There was no significant difference in respiratory motion artifacts and number of lesions or diameter of the smallest detected lesion using 12-s NN + ID or 17-s standard reconstruction. AI-augmented reconstructions can improve image quality while reducing breath-hold duration in T1-weighted hepatobiliary-phase gadoxetate-enhanced MRI, without compromising lesion detection. AI-augmented reconstruction of T1-weighted MRI improves image quality and lesion detection in hepatobiliary phase liver imaging, reducing breath-hold duration without compromising clinical lesion detection.

OBJECTIVES:Transcatheter CT hepatic angiography (CTHA) enhances liver tumour ablation by enabling multiple direct intra-arterial contrast injections during a single procedure. We report the implementation of a single-room CTHA workflow using a mobile fluoroscopic C-arm, focusing on feasibility and safety. METHODS:A prospective service evaluation was conducted following a radiation risk assessment and training at an expert centre. Feasibility-defined as the ability to integrate CTHA into a four-hour ablation session-was assessed alongside technical success, which was defined as acquisition of CTHA images. All CTHA-related adverse events were recorded and graded using the Common Terminology Criteria for Adverse Events (CTCAE). Radiation dose, contrast volume, and tumour visualization were also documented. RESULTS:Twenty patients (14 men, median age 66 years) underwent 21 CTHA procedures (April-September 2024). Feasibility and technical success were 100%, without any instance of CTHA-related complications. Median catheterization time was 10 min16 s, contrast dose was 174 mL (7 acquisitions), and radiation dose-area product was 18.86 Gy·cm2. Ninety-seven percent of tumours (32/33) were visible. All these tumours were completely covered by an ablation zone using image fusion software. CONCLUSIONS:Single-room CTHA using a mobile C-arm is feasible and safe for liver tumour ablation. This technique enhances tumour and ablation zone visibility whilst requiring low contrast volumes, enabling multiple acquisitions and real-time margin assessment. Our technique can be readily implemented without expensive infrastructure, holding significant promise in improving liver ablation outcomes and broadening access to advanced interventional oncology techniques. ADVANCES IN KNOWLEDGE:Single-room CTHA can be readily implemented in centres with catheterization experience, without expensive infrastructure. The method holds significant promise in improving liver ablation outcomes and broadening access to state-of-the-art interventional oncology techniques globally.

The assessment of imaging biomarkers is critical for advancing precision medicine and improving disease characterization. Despite the availability of methods to derive disease heterogeneity metrics in imaging studies, a robust framework for evaluating measurement uncertainty remains underdeveloped. To address this gap, we propose a novel Bayesian framework to assess the precision of disease heterogeneity measures in biomarker studies. Our approach extends traditional methods for evaluating biomarker precision by providing greater flexibility in statistical assumptions and enabling the analysis of biomarkers beyond univariate or multivariate normally-distributed variables. Using Hamiltonian Monte Carlo sampling, the framework supports both, for example, normally-distributed and Dirichlet-Multinomial distributed variables, enabling the derivation of posterior distributions for biomarker parameters under diverse model assumptions. Designed to be broadly applicable across various imaging modalities and biomarker types, the framework builds a foundation for generalizing reproducible and objective biomarker evaluation. To demonstrate utility, we apply the framework to whole-body diffusion-weighted MRI (WBDWI) to assess heterogeneous therapeutic responses in metastatic bone disease. Specifically, we analyze data from two patient studies investigating treatments for metastatic castrate-resistant prostate cancer (mCRPC). Our results reveal an approximately 70 tumors across both studies, objectively characterizing differential responses to systemic therapies and validating the clinical relevance of the proposed methodology. This Bayesian framework provides a powerful tool for advancing biomarker research across diverse imaging-based studies while offering valuable insights into specific clinical applications, such as mCRPC treatment response.

BACKGROUND AND OBJECTIVE:Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognised cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal. METHODS:We developed an automated deep-learning pipeline based on a 3D patch-based Residual U-Net architecture that localises and delineates these anatomical structures on WB-DWI. The algorithm was trained using "soft-labels" (non-binary segmentations) derived from a computationally intensive atlas-based approach. For training and validation, we employed a multi-centre WB-DWI dataset comprising 532 scans from patients with Advanced Prostate Cancer (APC) or Multiple Myeloma (MM), with testing on 45 patients. RESULTS:Our weakly-supervised deep learning model achieved an average dice score of 0.67 for whole skeletal delineation, 0.76 when excluding ribcage, 0.83 for internal organs, and 0.86 for spinal canal, with average surface distances below 3 mm. Relative median ADC differences between automated and manual full-body delineations were below 10 %. The model was 12x faster than the atlas-based registration algorithm (25 s vs. 5 min). Two experienced radiologists rated the model's outputs as either "good" or "excellent" on test scans, with inter-reader agreement from fair to substantial (Gwet's AC1=0.27-0.72). CONCLUSION:The model offers fast, reproducible probability maps for localising and delineating body regions on WB-DWI, potentially enabling non-invasive imaging biomarkers quantification to support disease staging and treatment response assessment.

The integration of magnetic resonance imaging into radiation therapy (RT) treatment necessitates automated segmentation algorithms for fast and accurate adaptive interventions, particularly in magnetic resonance imaging-integrated linear accelerator (MR-linac or MRL) treatment systems. However, the scarcity of data hampers the training of these models. This study aimed to address this shortcoming by developing a synthetic MRL-assisted deep learning framework to establish a robust baseline for organ at risk segmentation on MRL images and enable domain adaptation for automatic delineations during adaptive RT treatments. We used a retrospective data set, comprising 158 patients diagnosed with various gynecologic cancers who underwent computed tomography scanning for RT planning and 25 patients with T2-weighted MRL scans for model fine-tuning, adaptation, and evaluation. A patch-based cycle-consistent generative adversarial network was developed to synthesize MRL images from computed tomography data. Subsequently, a domain-adaptive segmentation network was trained to segment the 6 organs at risk on acquired MRL images. In addition, we employed per-fraction adaptation to enhance anatomical conformity guided by prior treatment fractions of individual patients. A quantitative evaluation and blinded human reader assessment were conducted to establish contour acceptance rates. The synthetic MRL-assisted model improved organ at risk segmentation accuracy on MRL images, with fraction-adapted contours displaying high anatomical fidelity. Two radiation oncologists reported contour acceptance rates of 100% and 98% for treatment planning after adaptation. This novel framework holds promise to bridge the semantic gap between computed tomography and magnetic resonance imaging databases, potentially facilitating adaptive RT treatments and reducing treatment times as well as clinician burden. The utility of this framework can extend beyond gynecologic and pelvic cancers.

This study aimed to assess the accuracy of fat fraction estimation with clinically available Dixon sequences in normal-appearing marrow and bone metastases in the pelvis of metastatic prostate cancer patients. A prospective single-centre study was conducted with metastatic prostate cancer patients and healthy volunteers. Linearity and bias of fat fraction estimates from clinically available Dixon sequences were assessed against a 6-point PDw gradient echo (q-Dixon) sequence measuring the reference standard proton density fat fraction. Lesion fat fraction estimates were cross-compared using the Friedman test. Repeatability in volunteers was evaluated with Bland-Altman plots. Sensitivity of fat fraction estimates using TSE-Dixon sequences to specific absorption rate (SAR) related modifications were evaluated with correlation plots. Thirty-three patients were recruited for this study. Significant (p < 0.05) absolute bias (12.4

Introduction Whole-body MRI (WB-MRI) is increasingly used in clinical practice for detection of malignant bone disease. A high relative contrast ratio (RCR) of malignant bone lesions compared with normal bone can improve disease detection in breast, prostate and myeloma malignancies. However, the RCR of malignant bone lesions on T1w, DWI and relative Fat Fraction (rFF) maps derived from Dixon T1w have not been compared. Methods 110 baseline WB-MRI of patients with suspected malignant bone lesions were reviewed retrospectively. On each scan, up to four active bone lesions were identified, one each at the cervicothoracic spine, lumbosacral spine, pelvis and extremity, and their ROI signal intensity measured on rFF, T1w and DWI b = 900. The signal intensity of background bone was measured by placing an ROI on the nearest normal-appearing bone to each lesion, for each sequence. The mean lesion signal-to-background ratio (taken as RCR) for each lesion was calculated. We compared the RCR of bone lesions on rFF, T1w and DWI (Mann-Whitney test). Results The median rFF RCR of malignant bone lesions was highest compared with normal bone marrow than that of T1w (p < 0.0001) and DWI (p < 0.0001). There was no significant difference in the median rFF RCR of malignant bone lesions from breast cancer, myeloma and prostate cancer (p > 0.017, Bonferroni correction) or according to their anatomical locations (p > 0.012, Bonferroni correction). Conclusions Malignant bone lesion RCR measured by lesion/background signal intensity was higher on rFF than on T1w and DWI b = 900 in patients with prostate, breast and myeloma malignancies, indicating its value for disease detection.

AI-based MRI reconstruction techniques improve efficiency by reducing acquisition times whilst maintaining or improving image quality. Recent recommendations from professional bodies suggest centres should perform quality assessments on AI tools. However, monitoring long-term performance presents challenges, due to model drift or system updates. Radiologist-based assessments are resource-intensive and may be subjective, highlighting the need for efficient quality control (QC) measures. This study explores using image quality metrics (IQMs) to assess AI-based reconstructions. 58 patients undergoing standard-of-care rectal MRI were imaged using AI-based and conventional T2-weighted sequences. Paired and unpaired IQMs were calculated. Sensitivity of IQMs to detect retrospective perturbations in AI-based reconstructions was assessed using control charts, and statistical comparisons between the four MR systems in the evaluation were performed. Two radiologists evaluated the image quality of the perturbed images, giving an indication of their clinical relevance. Paired IQMs demonstrated sensitivity to changes in AI-reconstruction settings, identifying deviations outside ± 2 standard deviations of the reference dataset. Unpaired metrics showed less sensitivity. Paired IQMs showed no difference in performance between 1.5 T and 3 T systems (p > 0.99), whilst minor but significant (p < 0.0379) differences were noted for unpaired IQMs. IQMs are effective for QC of AI-based MR reconstructions, offering resource-efficient alternatives to repeated radiologist evaluations. Future work should expand this to other imaging applications and assess additional measures.

Minimal residual disease (MRD) testing has underpinned the evaluation and expansion of therapeutic options for patients with multiple myeloma (MM). Imaging is essential for evaluating residual disease status, overcoming sampling errors inherent with other MRD modalities. The accuracy of whole-body MRI (WB-MRI) has led to its incorporation into MM diagnostic imaging guidelines. We report here on the prospective iTIMM trial (image-guided theranostics in MM; NCT02403102), designed to evaluate imaging residual disease using contemporary, functional WB-MRI as per MY-RADS protocol. In iTIMM, 70 MM patients planned to undergo autologous stem cell transplantation ASCT in newly diagnosed MM or at first relapse, underwent WB-MRI before start of induction and at day 100 post-ASCT. Patients with residual disease post-ASCT (RAC2 or higher) had shorter progression-free survival (median 24 months, 95% confidence interval (CI): 19-41 vs. 42 months, 95% CI: 37-not evaluable (NE), log-rank p = 0.013; hazard ratio (HR) 2.09 (95% CI: 1.15-3.78) and overall survival (median 47 months, 95% CI: 30.9-NE vs. NE (95% CI: NE-NE), p = 0.002, HR = 5.45 (95% CI: 1.67-17.87) than those without (RAC1). Imaging response also refined the prognostic association of bone marrow MRD and serological response. Our results support WB-MRI implementation for evaluation of residual disease alongside conventional laboratory-based assessments.

Radiological response evaluation metrics such as RECIST 1.1 inform critical endpoints in oncology trials. The UK was the 6th highest recruiter into oncology trials worldwide between 1999 and 2022, with almost 9000 oncology trials registered during the same period. However, the provision of tumour measurements for oncology trials is often ad hoc and patchy across the NHS. The aim of this work was to understand the barriers to providing an effective imaging tumour measurement service, gain insight into service delivery models and consider the successes and challenges from the perspective of both service providers and end users. An electronic survey was distributed to those who provide tumour measurement response review for clinical trials (service providers) and those that request and use such measurements in trial activities (service users). Responses from 35 sites demonstrated substantial variation in service provision across the UK. Despite workforce pressures, service is largely delivered through radiologists with a minority utilising radiographer role extension. Only 20% of the service providers had dedicated training and 29% received robust financial reimbursement. Service variation is likely a consequence of limited training, education and infrastructure to support robust service, compounded by increasing radiology workload and workforce pressures.

OBJECTIVES:To investigate signal intensity of colorectal cancer liver metastases (CRLM) at hepatobiliary phase (HBP) gadoxetate-enhanced MRI at 2 time points pre- (TP1) and post- chemotherapy (TP2) and association with disease-free survival (DFS) in patients undergoing curative liver resection. METHODS:Retrospective study was conducted. Single largest tumours were outlined and HBP T1 signal intensity was measured and normalized to skeletal muscle at TP1 and TP2. Enhancement thresholds were defined and risk groups at each TP and Kaplan-Meier survival curves were compared using the log-rank test. Univariate and multivariate association of enhancement and 8 clinical features with risk of recurrence were calculated using Cox proportional hazards. RESULTS:82 patients (48 male, mean age 59 years) underwent 135 imaging studies, 58 at TP1, 77 at TP2, and 53 patients at TP1 + 2. Of 82 patients, 58 recurred with a median time to recurrence of 11.7 months. Enhancement of ≥135 and ≥15 at TP1 and TP2, respectively, were predictive of reduced risk of recurrence (P < .05), although not when corrected for multiple testing (P = .33 and .20, respectively). Enhancement was not associated with recurrence in multivariate model including 8 clinical features (P > .05). Change in enhancement between TP was not associated with risk of recurrence; however, tumours that consistently exhibited low enhancement were 9 times more likely to recur. CONCLUSIONS:Increased CRLM enhancement in the HBP following gadoxetic acid at 2 TPs is associated with improved DFS in patients undergoing liver resection. This initial observation warrants further investigation of serial enhancement measurements as prognostic biomarkers. ADVANCES IN KNOWLEDGE:Dual-time point signal assessment may be informative for clinical outcomes in CRLM undergoing resection.

Objective. Quantitative assessment of treatment response in advanced prostate cancer (APC) with bone metastases remains an unmet clinical need. Whole-body diffusion-weighted MRI (WB-DWI) provides two response biomarkers: total diffusion volume (TDV) and global apparent diffusion coefficient (gADC). However, tracking post-treatment changes of TDV and gADC from manually delineated lesions is cumbersome and increases inter-reader variability. We developed a software to automate this process.Approach. Core technologies include: (i) a weakly-supervised Residual U-Net model generating a skeleton probability map to isolate bone; (ii) a statistical framework for WB-DWI intensity normalisation, obtaining a signal-normalisedb= 900 s mm-2(b900) image; and (iii) a shallow convolutional neural network that processes outputs from (i) and (ii) to generate a mask of suspected bone lesions, characterised by higher b900 signal intensity due to restricted water diffusion. This mask is applied to the gADC map to extract TDV and gADC statistics. We tested the tool using expert-defined metastatic bone disease delineations on 66 datasets, assessed repeatability of imaging biomarkers (N= 10), and compared software-based response assessment with aconstruct reference standard, defined as multidisciplinary consensus based on ⩾12 months of imaging, clinical, and laboratory follow-up (N= 118).Main results. Average dice score between manual and automated delineations was 0.6 for lesions within pelvis and spine, with an average surface distance of 2 mm. Relative differences for log-transformed TDV (log-TDV) and median gADC were 8.8% and 5%, respectively. Repeatability analysis showed coefficients of variation of 4.6% for log-TDV and 3.5% for median gADC, with intraclass correlation coefficients of 0.94 or higher. The software achieved 80.5% accuracy, 84.3% sensitivity, and 85.7% specificity in assessing response to treatment. Average computation time was 90 s per scan.Significance. Our software enables reproducible TDV and gADC quantification from WB-DWI scans for monitoring metastatic bone disease response, thus providing potentially useful measurements for clinical decision-making in APC patients.

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.

Objective. Image quality in whole-body MRI (WB-MRI) may be degraded by faulty radiofrequency (RF) coil elements or mispositioning of the coil arrays. Phantom-based quality control (QC) is used to identify broken RF coil elements but the frequency of these acquisitions is limited by scanner and staff availability. This work aimed to develop a scan-specific QC acquisition and processing pipeline to detect broken RF coil elements, which is sufficiently rapid to be added to the clinical WB-MRI protocol. The purpose of this is to improve the quality of WB-MRI by reducing the number of patient examinations conducted with suboptimal equipment. Approach. A rapid acquisition (14 s additional acquisition time per imaging station) was developed that identifies broken RF coil elements by acquiring images from each individual coil element and using the integral body coil. This acquisition was added to one centre’s clinical WB-MRI protocol for one year (892 examinations) to evaluate the effect of this scan-specific QC. To demonstrate applicability in multi-centre imaging trials, the technique was also implemented on scanners from three manufacturers. Main results . Over the course of the study RF coil elements were flagged as potentially broken on five occasions, with the faults confirmed in four of those cases. The method had a precision of 80% and a recall of 100% for detecting faulty RF coil elements. The coil array positioning measurements were consistent across scanners and have been used to define the expected variation in signal. Significance . The technique demonstrated here can identify faulty RF coil elements and positioning errors and is a practical addition to the clinical WB-MRI protocol. This approach was fully implemented on systems from two manufacturers and partially implemented on a third. It has potential to reduce the number of clinical examinations conducted with suboptimal hardware and improve image quality across multi-centre studies.

Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (a) provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) discuss strategies to overcome these challenges and mistakes; and (d) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.