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Summary: Background: As differentiating between lipomas and atypical lipomatous tumors (ALTs) based on imaging is challenging and requires biopsies, radiomics has been proposed to aid the diagnosis. This study aimed to externally and prospectively validate a radiomics model differentiating between lipomas and ALTs on MRI in three large, multi-center cohorts, and extend it with automatic and minimally interactive segmentation methods to increase clinical feasibility. Methods: Three study cohorts were formed, two for external validation containing data from medical centers in the United States (US) collected from 2008 until 2018 and the United Kingdom (UK) collected from 2011 until 2017, and one for prospective validation consisting of data collected from 2020 until 2021 in the Netherlands. Patient characteristics, MDM2 amplification status, and MRI scans were collected. An automatic segmentation method was developed to segment all tumors on T1-weighted MRI scans of the validation cohorts. Segmentations were subsequently quality scored. In case of insufficient quality, an interactive segmentation method was used. Radiomics performance was evaluated for all cohorts and compared to two radiologists. Findings: The validation cohorts included 150 (54% ALT), 208 (37% ALT), and 86 patients (28% ALT) from the US, UK and NL. Of the 444 cases, 78% were automatically segmented. For 22%, interactive segmentation was necessary due to insufficient quality, with only 3% of all patients requiring manual adjustment. External validation resulted in an AUC of 0.74 (95% CI: 0.66, 0.82) in US data and 0.86 (0.80, 0.92) in UK data. Prospective validation resulted in an AUC of 0.89 (0.83, 0.96). The radiomics model performed similar to the two radiologists (US: 0.79 and 0.76, UK: 0.86 and 0.86, NL: 0.82 and 0.85). Interpretation: The radiomics model extended with automatic and minimally interactive segmentation methods accurately differentiated between lipomas and ALTs in two large, multi-center external cohorts, and in prospective validation, performing similar to expert radiologists, possibly limiting the need for invasive diagnostics. Funding: Hanarth fonds.

Background. Endometrial stromal sarcomas (ESS) are a subtype of gynaecological sarcomas characterized by the overexpression of hormone receptors. Hormone treatment is widely used in ESS but primary or acquired resistance is common. The mammalian target of rapamycin (mTOR) pathway has been suggested to play a key role in the mechanisms of hormone resistance. Recent studies in breast and prostate cancer demonstrate that this resistance can be reversed with the addition of an mTOR inhibitor. This phenomenon has never been reported in ESS. Methods. We report the outcome of one patient with pretreated, progressing low grade metastatic ESS treated with medroxyprogesterone acetate in combination with the mTOR inhibitor sirolimus. Results. Partial response was achieved following the addition of sirolimus to the hormone treatment. Response has been maintained for more than 2 years with minimal toxicity and treatment is ongoing. Conclusion. This case suggests that the resistance to the hormone manipulation in ESS can be reversed by the addition of an mTOR pathway inhibitor. This observation is highly encouraging and deserves further investigation.

We report a case of a 33-year-old female presenting with right sided abdominal pain, a right iliac fossa mass and right hydronephrosis. The mass was subsequently shown to represent an isolated desmoid tumour compromising the adjacent ureter. The patient had no predisposing factors for this pathology. This is an uncommon occurrence and we discuss the imaging features and differential diagnosis. [ABSTRACT FROM AUTHOR]

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., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Aim: To assess the diagnostic accuracy and inter-reader agreement of a simulated abbreviated gadoxetate liver magnetic resonance imaging (MRI) protocol together with contrast-enhanced computed tomography (CE-CT) against a standard gadoxetate MRI for the detection of colorectal liver metastases at baseline., Materials and Methods: Three readers independently evaluated two sets of images per patient, recording number and location of metastases and benign lesions. Set 1 comprised T1w, T2w, DWI, multiphase CE-T1w, and hepatobiliary phase (HBP) images (standard). Set 2 included T2w, HBP, DWI (from Set 1) and CE-CT (simulated abbreviated). Diagnostic performance was compared using McNemar's test. The level of agreement between sets 1 and 2 was determined with Cohen kappa. For agreement in the number of benign lesions and metastases, we applied intraclass correlation coefficient (ICC)., Results: Seventy-five patients (245 metastases, 122 benign lesions) were evaluated. There was no significant difference in diagnostic accuracy between set 1 and 2 for each reader (mean P = 0.74). The total number of metastases and benign lesions showed high agreement between reading set 1 and 2 (κ = 0.81, 0.78). The total number of metastases showed substantial agreement between readers for set 1 and 2 (ICC = 0.99, 0.99). Good agreement was seen for metastatic segmental involvement (κ = 0.84-0.99)., Conclusion: At baseline, using a simulated abbreviated liver MRI together with CE-CT showed excellent agreement with standard MRI protocol for liver metastasis detection., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

The purpose of this study was to determine if dual-energy CT (DECT) vital iodine tumor burden (ViTB), a direct assessment of tumor vascularity, allows reliable response assessment in patients with GIST compared to established CT criteria such as RECIST1.1 and modified Choi (mChoi). From 03/2014 to 12/2019, 138 patients (64 years [32-94 years]) with biopsy proven GIST were entered in this prospective, multi-center trial. All patients were treated with tyrosine kinase inhibitors (TKI) and underwent pre-treatment and follow-up DECT examinations for a minimum of 24 months. Response assessment was performed according to RECIST1.1, mChoi, vascular tumor burden (VTB) and DECT ViTB. A change in therapy management could be because of imaging (RECIST1.1 or mChoi) and/or clinical progression. The DECT ViTB criteria had the highest discrimination ability for progression-free survival (PFS) of all criteria in both first line and second line and thereafter treatment, and was significantly superior to RECIST1.1 and mChoi (p < .034). Both, the mChoi and DECT ViTB criteria demonstrated a significantly early median time-to-progression (both delta 2.5 months; both p < .036). Multivariable analysis revealed 6 variables associated with shorter overall survival: secondary mutation (HR = 4.62), polymetastatic disease (HR = 3.02), metastatic second line and thereafter treatment (HR = 2.33), shorter PFS determined by the DECT ViTB criteria (HR = 1.72), multiple organ metastases (HR = 1.51) and lower age (HR = 1.04). DECT ViTB is a reliable response criteria and provides additional value for assessing TKI treatment in GIST patients. A significant superior response discrimination ability for median PFS was observed, including non-responders at first follow-up and patients developing resistance while on therapy., (© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Rationale and Objectives: Intratumoral heterogeneity means single site tumor biopsy might not be representative. Here we develop and optimize an MRI-informed robotic multiregional 'smart' biopsy technique in retroperitoneal and pelvic sarcomas (RPS). We also explore the relationship between imaging and histological biomarkers., Materials and Methods: Participants with suspected RPS underwent multiparametric (mp)MRI within a week prior to biopsy. Three target regions with differing MRI characteristics were contoured. Robotic or freehand multiregional biopsy was performed, collecting samples from each target region in separate specimen pots. CT/MRI fusion extracted quantitative imaging biomarkers for correlation with histological biomarkers at precise biopsy sites. The primary endpoint was feasibility and safety. Spearman's correlation explored the relationship between imaging and histological biomarkers., Results: Twelve participants (7 women), median age 58.6 years interquartile range [IQR]: 52 - 75 years underwent biopsy. All procedures were technically successful with same-day discharge. The within-tumor range of Apparent Diffusion Coefficeint correlated very strongly with the within-tumor range of Ki-67 proliferation index; Spearman's ρ = 0.91 (95% CI 0.68 to 0.98). Ranges represent intratumoral heterogeneity uniquely obtained by multiregional biopsy., Conclusion: Multiregional robotic MRI-informed, CT-guided biopsy is feasible and safe in RPS. Sampling three distinct regions within tumors provides a more comprehensive and accurate representation of tumor biology than standard biopsy. The close relationship between imaging and histological heterogeneity biomarkers has broader implications for pancancer biopsy techniques, imaging characterization, and personalized treatment selection., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Edward W Johnston reports financial support was provided by Royal College of Radiologists. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

This Good Practice Paper provides recommendations for the use of advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma. It describes how advanced imaging contributes to optimal healthcare resource utilisation by in newly diagnosed and relapsed myeloma, and provides a perspective on future directions of myeloma imaging, including machine learning assisted reporting., (© 2024 British Society for Haematology and John Wiley & Sons Ltd.)

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., (Creative Commons Attribution license.)

Purpose: To explore accessibility challenges encountered by smartphone users with cervical spinal cord injuries (C1-C8). To investigate the suitability of current technology and make recommendations to help future technology meet user needs., Methods: The study uses a mixed-method approach combining an inductive thematic analysis of nine semi-structured interviews with a quantitative analysis of thirty-nine questionnaires., Results: The analysis generated four themes: 'the drive for independence and self-efficacy' ; 'trying to make it work' ; 'getting the right technology for me' ; 'using the phone as and when I want to' . These themes highlighted how unresolved access issues and situational barriers limited independence and created unwanted privacy compromises for effective communication. There was a lack of information or support on available smartphone accessibility features and assistive technology (AT). Smartphone AT was regarded as overpriced, poorly designed and lacking the voices of people with disabilities., Conclusions: The smartphone's potential to improve quality of life, participation, and well-being is limited by accessibility challenges hindering independent and private smartphone use. Future design work should focus on improving accessibility, investigating reasons for AT's poor quality and high cost, and removing barriers to end-user inclusion. To enhance user awareness of available technology, stakeholders should build and maintain an open platform to act as an information source for peer and professional support on assistive technology.

(1) Background: We assessed the test-re-test repeatability of radiomics in metastatic castration-resistant prostate cancer (mCPRC) bone disease on whole-body diffusion-weighted (DWI) and T1-weighted Dixon MRI. (2) Methods: In 10 mCRPC patients, 1.5 T MRI, including DWI and T1-weighted gradient-echo Dixon sequences, was performed twice on the same day. Apparent diffusion coefficient (ADC) and relative fat-fraction-percentage (rFF%) maps were calculated. Per study, up to 10 target bone metastases were manually delineated on DWI and Dixon images. All 106 radiomic features included in the Pyradiomics toolbox were derived for each target volume from the ADC and rFF% maps. To account for inter- and intra-patient measurement repeatability, the log-transformed individual target measurements were fitted to a hierarchical model, represented as a Bayesian network. Repeatability measurements, including the intraclass correlation coefficient (ICC), were derived. Feature ICCs were compared with mean ADC and rFF ICCs. (3) Results: A total of 65 DWI and 47 rFF% targets were analysed. There was no significant bias for any features. Pairwise correlation revealed fifteen ADC and fourteen rFF% feature sub-groups, without specific patterns between feature classes. The median intra-patient ICC was generally higher than the inter-patient ICC. Features that describe extremes in voxel values (minimum, maximum, range, skewness, and kurtosis) showed generally lower ICCs. Several mostly shape-based texture features were identified, which showed high inter- and intra-patient ICCs when compared with the mean ADC or mean rFF%, respectively. (4) Conclusions: Pyradiomics texture features of mCRPC bone metastases varied greatly in inter- and intra-patient repeatability. Several features demonstrated good repeatability, allowing for further exploration as diagnostic parameters in mCRPC bone disease.

Mucosal (MM) and acral melanomas (AM) are rare melanoma subtypes of unmet clinical need; 15%-20% harbor KIT mutations potentially targeted by small-molecule inhibitors, but none yet approved in melanoma. This multicenter, single-arm Phase II trial (NICAM) investigates nilotinib safety and activity in KIT mutated metastatic MM and AM. KIT mutations are identified in 39/219 screened patients (18%); of 29/39 treated, 26 are evaluable for primary analysis. Six patients were alive and progression free at 6 months (local radiology review, 25%); 5/26 (19%) had objective response at 12 weeks; median OS was 7.7 months; ddPCR assay correctly identifies KIT alterations in circulating tumor DNA (ctDNA) in 16/17 patients. Nilotinib is active in KIT-mutant AM and MM, comparable to other KIT inhibitors, with demonstrable activity in nonhotspot KIT mutations, supporting broadening of KIT evaluation in AM and MM. Our results endorse further investigations of nilotinib for the treatment of KIT-mutated melanoma. This clinical trial was registered with ISRCTN (ISRCTN39058880) and EudraCT (2009-012945-49)., Competing Interests: Declaration of interests J.L. declares the following: honoraria: Eisai, Novartis, Incyte, Merck, touchIME, touchEXPERTS, Pfizer, Royal College of Physicians, Cambridge Healthcare Research, Royal College of General Practitioners, VJOncology, Agence Unik, BMS, Immatics, Insighter, and GCO; consultancy: iOnctura, Apple Tree, Merck, BMS, Eisai, Debipharm, Incyte, Pfizer, and Novartis; speaker’s fees: Pierre Fabre, BMS, Ipsen, Roche, EUSA Pharma, Novartis, Aptitude, AstraZeneca, GSK, Eisai, Calithera, Ultimovacs, Seagen, Merck, eCancer, Inselgruppe, Pfizer, Goldman Sachs, MSD, Regional British Society of Gastroenterology, and Agence Unik; institutional research support: BMS, MSD, Novartis, Pfizer, Achilles Therapeutics, Roche, Nektar Therapeutics, Covance, Immunocore, Pharmacyclics, and Aveo; grants: Achilles, BMS, MSD, Nektar, Novartis, Pfizer, Roche, Immunocore, Aveo, and Pharmacyclics. R.M. is an expert witness for Pfizer and may benefit financially from commercialized programs. P.L. declares the following: honoraria: Novartis, PierreFabre, Merck, BMS, MSD, NeraCare GmbH, Amgen, Roche, OncologyEducation Canada, and Nektar; consultancy: Merck Sharp & Dohme, Bristol-Myers Squibb, Amgen, Pierre Fabre, Novartis, Nektar, and NeraCare GmbH; speakers’ bureaus: Merck Sharp & Dohme, Novartis, Bristol-Myers Squibb, and Pierre Fabre; institutional research funding: BMS and Pierre Fabre; travel, accommodations, expenses: Merck Sharp & Dohme and Bristol-Myers Squibb. T.R.J.E. reported the following competing interests: honoraria (payable to employing institution): Ascelia, AstraZeneca, Bicycle Therapeutics, BMS, Eisai, Medivir, MSD, Nucana, Roche/Genentech, and Seagen; advisory/consulting (payable to employing institution): Karus Therapeutics; speakers’ bureaus (payable to employing institution): AstraZeneca, BMS, Eisai, Medivir, MSD, Nucana, Roche/Genentech, and United Medical; research funding (payable to employing institution): Adaptimmune, Astellas Pharma, AstraZeneca, Athenex, Avacta, Basilea, Bayer, Beigene, Berg Pharma, Bicycle Therapeutics, BiolineRx, Boehringer Ingelheim, BMS, Celgene, Clovis Oncology, Codiak, CytomX Therapeutics, Eisai, GlaxoSmithKline, Halozyme, Immunocore, iOnctura, Iovance Biotherapeutics, Janssen, Johnson & Johnson, Lilly, Medivir, Merck Serono, MSD, MiNA Therapeutics, Modulate Pharma, Novartis, Nucana, Nurix, Plexxikon, Roche/Genentech, Sanofi/Aventis, Sapience Therapeutics, Seagen, Seattle Genetics, Sierra Pharma, Starpharma, T3P, UCB, Verastem, and Vertex; expert testimony (payable to employing institution): Medivir; support to attend international conferences (personal): BMS, Celgene, Eisai, MSD, Nucana, Pierre Fabre, and Roche; other relationship (payable to employing institution): Genmab. M.R.M. is supported by the NIHR Biomedical Research Center in Oxford and reports grants from Roche, AstraZeneca, GSK, GRAIL (outside the submitted work), grants and other from Immunocore, and other from Novartis, BMS, Pfizer, Merck/MSD, Regeneron, BiolineRx, and Replimune. P.N. reported having received funding for advisory boards and/or speakers’ bureau from the following sources: AstraZeneca, BMS, Esai, Ideaya, Immunocore, Ipsen, Medicenna, MSD, Merck, Novartis, and Pfizer. R.P. reported, in the last 4 years, having received honoraria for attending advisory boards from Pierre Faber, Bayer, Novartis, BMS, Cybrexa, Ellipses, CV6 Therapeutics, Immunocore, Genmab, Astex Therapeutics, Medivir, and Sanofi Aventis; honoraria as an IDMC member for Alligator Biosciences, GSK, Onxeo, SOTIO Biotech AG, and AstraZeneca; having been paid for delivery of educational talks or chairing educational meetings by AstraZeneca, Novartis, Bayer, MSD, and BMS; and received funds to support attendance at conferences from MSD and BMS. J. Bliss reported receiving grants to ICR-CTSU from AstraZeneca, Merck Sharp & Dohme, Puma Biotechnology, Pfizer, Roche, Novartis (previously GlaxoSmithKline), Eli Lilly, Janssen-Cilag, Clovis Oncology, and Cancer Research UK; and nonfinancial support from the National Institute for Health Research. S.V. is a recipient of a research grant from Amgen. S.T. is funded by CRUK (grant no. A29911), the Francis Crick Institute, which receives its core funding from CRUK (FC10988), the UK Medical Research Council (FC10988), the Wellcome Trust (FC10988), the National Institute for Health Research Biomedical Research Centre at the Royal Marsden Hospital and Institute of Cancer Research (grant no. A109), the Royal Marsden Cancer Charity, The Rosetrees Trust (grant no. A2204), Ventana Medical Systems (grant nos. 10467 and 10530), the National Institutes of Health (U01 CA247439), and the Melanoma Research Alliance (Award Ref no 686061). S.T. has received speaking fees from Roche, Astra Zeneca, Novartis, and Ipsen. S.T. has filed the following patents: Indel mutations as a therapeutic target and predictive biomarker PCTGB2018/051892 and PCTGB2018/051893 and P113326GB., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining "real-world" and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation., Methods: Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods., Results: A total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for "composing" whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered., Conclusions: MALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multi-national imaging projects., Critical Relevance Statement: This article showcases innovative data curation methods using a state-of-the-art image repository platform; such tools will be vital for managing the large multi-institutional datasets required to train and validate generalisable ML algorithms and future foundation models in medical imaging., Key Points: • Heterogeneous data in the MALIMAR study required the development of novel curation strategies. • Correction of multiple problems affecting the real-world data was successful, but implications for machine learning are still being evaluated. • Modern image repositories have rich application programming interfaces enabling data enrichment and programmatic QA, making them much more than simple "image marts"., (© 2024. The Author(s).)

Background: Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning., Methods: We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287)., Results: The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10 -3 mm 2 /s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised., Conclusion: Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.

Objectives: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times., Materials and Methods: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated., Results: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML)., Conclusions: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support., Competing Interests: Conflicts of interest and sources of funding: A.G.R. is part of the advisory board of RoClub. This study was funded by the National Institute of Health Research Efficacy and Mechanism Evaluation Programme (trial registration: ISRCTN 23068310). The project is supported by the Imperial College London National Institute for Health Research Biomedical Research Centre, CRUK Imperial Centre, National Institute for Health Research University College London Hospitals Biomedical Research Centre, NIHR Biomedical Research Centre, and the NIHR Clinical Research Facilities and the Royal Marsden Hospital and Institute of Cancer Research. S.T. is an NIHR senior investigator. This research has been conducted using the UK Biobank Resource., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Objectives: To compare relative fat fraction (rFF) of active bone lesions from breast, prostate and myeloma malignancies and normal bone marrow; to assess its inter-reader agreement., Methods: Patients with breast ( n = 26), myeloma ( n = 32) and prostate cancer ( n = 52) were retrospectively evaluated. 110 baseline rFF maps from whole-body MRI were reviewed by two radiologists. Regions of interest for up to four focal active lesions in each patient were drawn on rFF maps, one each at the cervicothoracic spine, lumbosacral spine, pelvis and extremity. The mean and standard deviation of rFF were recorded. The rFF of normal marrow was measured in the pelvis for patients without diffuse bone disease ( n = 88). We compared the rFF of malignant bone lesions and normal marrow using Mann-Whitney test. Interobserver agreement was assessed by interclass correlation coefficient., Results: Malignant bone lesions showed significantly lower median rFF (13.87%) compared with normal marrow (89.76%) with little overlap ( p < 0.0001). There was no significant difference in the median rFF of malignant lesions from breast (14.46%), myeloma (13.12%) and prostate cancer (13.67%) ( p > 0.017, Bonferroni correction) and in the median rFF of bone disease according to their anatomical locations ( p > 0.008, Bonferroni correction). There was excellent interobserver agreement (0.95)., Conclusion: The low rFF of active bone lesions in breast, prostate and myeloma malignancies provides high image contrast relative to normal marrow that may be used to detect bone metastases., Advances in Knowledge: This study shows the importance of rFF towards detecting bone metastases.

T 2 -weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T 2 W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant ( p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T 2 W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Background: 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., Methods: 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., Findings: 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., Interpretation: 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., Funding: 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., Competing Interests: Declaration of interests AA reports funding from the Wellcome Trust paid to their institution. MRO, SD, and CM report funding paid to their institutions from the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London. MRO reports funding from the Royal Marsden Cancer Charity to their institution. SD reports funding to their institution from Cancer Research UK. RH declares a previous role as the President of the Connective Tissue Oncology Society (in 2021). AG reports grants from PharmaMar and Nanobiotic; consulting fees from Novartis, Pfizer, Bayer, Lilly, PharmaMar, SpringWorks, and Boehringer Ingelheim; and honoraria from Deciphera. CPR reports royalties from UpTo Date. DVG reports grants from Elekta, IntraOp, and Orfit to their institution; consulting fees from Sanofi, Takeda, Novartis, and Merck; payment or honoraria from Elekta for lectures and support for attending meetings or travel; payment from Sanofi for expert testimony; and also declares a role in the Belgian College of Oncology and a research partnership with Elekta, Siemens, IntraOp and, MIM software, and a commercial partnership with Orfit, VisionRT, Philips, and Precisionxray. CLP reports payment from AstraZeneca to their institution for lectures; support for attending meetings and travel by Janseen and Ose Immunotherapeutics; and participation on advisory boards for AstraZeneca, Bristol Myers Squibbs and Varian. RJ reports grants from MSD and GSK and consulting fees from Adaptimmune, Astex, Athenex, Bayer, Boehringer Ingelheim, Blueprint, Clinigen, Eisai, Epizyme, Daichii, Deciphera, Immunedesign, Immunicum, Karma Oncology, Lilly, Merck, Mundipharma, PharmaMar, SpringWorks, Synox, Tracon, and UpTo Date. CM reports funding from European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group to their institution; payment or honoraria from TeleMedicine Clinic academy and GE Healthcare; and support for meetings from International Cancer Imaging Society. CM also declares a role as trustee of the British Institution of Radiology and is a Fellow of the International Cancer Imaging Society. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives: To assess the repeatability of quantitative multiparametric whole-body MRI (mpWB-MRI) parameters in advanced prostate cancer (APC) bone metastases., Methods: 1.5T MRI was performed twice on the same day in 10 APC patients. MpWB-MRI-included diffusion weighted imaging (DWI) and T 1 -weighted gradient-echo 2-point Dixon sequences. ADC and relative fat-fraction percentage (rFF%) maps were calculated, respectively. A radiologist delineated up to 10 target bone metastases per study. Means of ADC, b900 signal intensity(SI), normalised b900 SI, rFF% and maximum diameter (MD) for each target lesion and overall parameter averages across all targets per patient were recorded. The total disease volume (tDV in ml) was manually delineated on b900 images and mean global (g)ADC was derived. Bland-Altman analyses were performed with calculation of 95% repeatability coefficients (RC)., Results: Seventy-three individual targets (median MD 26 mm) were included. Lesion mean ADC RC was 12.5%, mean b900 SI RC 137%, normalised mean b900 SI RC 110%, rFF% RC 3.2 and target MD RC 5.5 mm (16.3%). Patient target lesion average mean ADC RC was 6.4%, b900 SI RC 104% and normalised mean b900 SI RC 39.6%. Target average rFF% RC was 1.8, average MD RC 1.3 mm (4.8%). tDV segmentation RC was 6.4% and mean gADC RC 5.3%., Conclusions: APC bone metastases' ADC, rFF% and maximum diameter, tDV and gADC show good repeatability., Advances in Knowledge: APC bone metastases' mean ADC and rFF% measurements of single lesions and global disease volumes are repeatable, supporting their potential role as quantitative biomarkers in metastatic bone disease.

Background: The Myeloma Response Assessment and Diagnosis System (MY-RADS) guidelines establish a standardised acquisition and analysis pipeline for whole-body MRI (WB-MRI) in patients with myeloma. This is the first study to assess image quality in a multi-centre prospective trial using MY-RADS., Methods: The cohort consisted of 121 examinations acquired across ten sites with a range of prior WB-MRI experience, three scanner manufacturers and two field strengths. Image quality was evaluated qualitatively by a radiologist and quantitatively using a semi-automated pipeline to quantify common artefacts and image quality issues. The intra- and inter-rater repeatability of qualitative and quantitative scoring was also assessed., Results: Qualitative radiological scoring found that the image quality was generally good, with 94% of examinations rated as good or excellent and only one examination rated as non-diagnostic. There was a significant correlation between radiological and quantitative scoring for most measures, and intra- and inter-rater repeatability were generally good. When the quality of an overall examination was low, this was often due to low quality diffusion-weighted imaging (DWI), where signal to noise ratio (SNR), anterior thoracic signal loss and brain geometric distortion were found as significant predictors of examination quality., Conclusions: It is possible to successfully deliver a multi-centre WB-MRI study using the MY-RADS protocol involving scanners with a range of manufacturers, models and field strengths. Quantitative measures of image quality were developed and shown to be significantly correlated with radiological assessment. The SNR of DW images was identified as a significant factor affecting overall examination quality., Trial Registration: ClinicalTrials.gov, NCT03188172 , Registered on 15 June 2017., Critical Relevance Statement: Good overall image quality, assessed both qualitatively and quantitatively, can be achieved in a multi-centre whole-body MRI study using the MY-RADS guidelines., Key Points: • A prospective multi-centre WB-MRI study using MY-RADS can be successfully delivered. • Quantitative image quality metrics were developed and correlated with radiological assessment. • SNR in DWI was identified as a significant predictor of quality, allowing for rapid quality adjustment., (© 2023. European Society of Radiology (ESR).)

Aim: To evaluate the incidence of pseudoprogression in patients with metastatic or inoperable uterine leiomyosarcoma (LMS) treated with first-line single-agent doxorubicin., Methods: The Royal Marsden NHS Foundation Trust Sarcoma Unit database was searched to identify all patients with metastatic or inoperable LMS treated with first-line doxorubicin from January 2006 to January 2022. Patients with available computed tomography scans performed at baseline and during doxorubicin therapy were included. Response evaluation criteria in solid tumours v1.1 and Choi criteria were applied. Any increase in the sum of the longest diameter that decreased on the subsequent scan was labelled as pseudoprogression., Results: The total number of patients evaluated was 52. In total, 19% (n = 10) of patients treated with doxorubicin showed pseudoprogression. However, pseudoprogression at the time of the second scan was not associated with time to doxorubicin failure. Choi criteria identified 30% (n = 3) of pseudoprogressors as responding., Conclusion: Despite the use of doxorubicin as first-line therapy for soft-tissue sarcomas for over 40 years, pseudoprogression has not been described. This retrospective study shows that pseudoprogression occurs in 19% of patients with metastatic/inoperable uterine LMS treated with first-line doxorubicin. Choi criteria were not consistently able to differentiate pseudoprogression from true progression. It is imperative that oncologists and radiologists are aware of this as symptomatically stable/improving patients may benefit from continued treatment despite initial radiological growth in tumour size., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools . This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions., Competing Interests: CM receives funding from the Royal Marsden Cancer Charity and NIHR Biomedical Research Centre at The Royal Marsden and Institute of Cancer Research. Richard Lee reports funding indirectly related to this work including Royal Marsden Cancer charity, CRUK, Innovate UK (co-funded by Roche), RM Partners Cancer Alliance, NIHR, NHS England, and personal private practice. MST is a scientific advisor to Mindpeak and Sonrai Analytics, and has received honoraria recently from BMS, Roche, MSD and Incyte. None of these disclosures are related to this work., (© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

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)., (© 2023. International Cancer Imaging Society (ICIS).)

Purpose: The multicenter OPTIMUM (MUKnine) phase II trial investigated daratumumab, low-dose cyclophosphamide, lenalidomide, bortezomib, and dexamethasone (Dara-CVRd) before and after autologous stem-cell transplant (ASCT) in newly diagnosed patients with molecularly defined ultra-high-risk (UHiR) multiple myeloma (NDMM) or plasma cell leukemia (PCL). To provide clinical context, progression-free survival (PFS) and overall survival (OS) were referenced to contemporaneous outcomes seen in patients with UHiR NDMM treated in the recent Myeloma XI (MyeXI) trial., Methods: Transplant-eligible all-comers NDMM patients were profiled for UHiR disease, defined by presence of ≥2 genetic risk markers t(4;14)/t(14;16)/t(14;20), del(1p), gain(1q), and del(17p), and/or SKY92 gene expression risk signature. Patients with UHiR MM/PCL were offered treatment with Dara-CVRd induction, V-augmented ASCT, extended Dara-VR(d) consolidation, and Dara-R maintenance. UHiR patients treated in MyeXI with carfilzomib, lenalidomide, dexamethasone, and cyclophosphamide, or lenalidomide, dexamethasone, and cyclophosphamide, ASCT, and R maintenance or observation were identified by mirrored molecular screening. OPTIMUM PFS at 18 months (PFS18m) was compared against MyeXI using a Bayesian framework, and patients were followed up to the end of consolidation for PFS and OS., Results: Of 412 screened NDMM OPTIMUM patients, 103 were identified as UHiR or PCL and subsequently treated on trial with Dara-CVRd; 117 MyeXI patients identified as UHiR formed the external comparator arm, with comparable clinical and molecular characteristics to OPTIMUM. Comparison of PFS18m per Bayesian framework resulted in a 99.5% chance of OPTIMUM being superior to MyeXI. At 30 months' follow-up, PFS was 77% for OPTIMUM versus 39.8% for MyeXI, and OS 83.5% versus 73.5%, respectively. Extended post-ASCT Dara-VRd consolidation therapy was highly deliverable, with limited toxicity., Conclusion: Our results suggest that Dara-CVRd induction and extended post-ASCT Dara-VRd consolidation markedly improve PFS for UHiR NDMM patients over conventional management, supporting further evaluation of this strategy.

Understanding the evolutionary pathways to metastasis and resistance to immune-checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here, we present the most comprehensive intrapatient metastatic melanoma dataset assembled to date as part of the Posthumous Evaluation of Advanced Cancer Environment (PEACE) research autopsy program, including 222 exome sequencing, 493 panel-sequenced, 161 RNA sequencing, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen-presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI nonresponders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one patient. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma., Significance: Despite treatment advances, melanoma remains a deadly disease at stage IV. Through research autopsy and dense sampling of metastases combined with extensive multiomic profiling, our study elucidates the many mechanisms that melanomas use to evade treatment and the immune system, whether through mutations, widespread copy-number alterations, or extrachromosomal DNA. See related commentary by Shain, p. 1294. This article is highlighted in the In This Issue feature, p. 1275., (©2023 The Authors; Published by the American Association for Cancer Research.)

Background: Pelvic soft tissue sarcomas are rare. Potentially curative resection remains challenging due to anatomical constraints of true pelvis and tumour spread through various anatomical hiatus. We sought to review the oncological outcomes of surgically managed cases at our centre and determine whether outcomes differ for patients with localised (limited to pelvis) versus extensive disease (with extra-pelvic extension)., Methods: Sixty-seven patients who underwent surgical resection with curative intent at the centre for primary, non-metastatic, WHO intermediate to high-grade soft tissue sarcoma of the true pelvis from January 2012 through January 2020 were analysed. Establishment of the extent of disease was made by review of pre-treatment imaging and surgical notes. Oncologic endpoints examined were resection margin, recurrence rate, disease-free and overall survival., Results: Rates of complete oncological resection and disease control were similar for tumours with localised or extensive disease. On logistic regression analysis, tumour grade, and a negative resection margin (R0) correlated with the risk of recurrence (p=<0.05). On further multinomial analysis, R0 resection was associated with improved local control, but not metastatic relapse (p = 0.003). 5-year local recurrence-free and distant metastasis-free survival were 61.3% and 67.1%, respectively. Five and 10-year overall survival were 64% and 36%, respectively. Age >50 years and high tumour grade were associated with a worse outcome (p < 0.05)., Conclusions: When potentially curative surgery is performed for pelvic sarcoma, disease-extent does not influence oncologic outcomes. While a complete oncologic resection determines the risk of local recurrence, tumour grade and metastatic relapse remain primary prognostic determinants for overall survival., Competing Interests: Declaration of competing interest None., (Crown Copyright © 2023. Published by Elsevier Ltd. All rights reserved.)

Background: Owing to the rarity and heterogeneity in biology and presentation, there are multiple areas in the diagnosis, treatment and follow-up of soft tissue sarcoma (STS), with no, low-level or conflicting evidence., Methods: During the first Consensus Conference on the State of Science in Sarcoma (CSSS), we used a modified Delphi process to identify areas of controversy in the field of sarcoma, to name topics with limited evidence-based data in which a scientific and knowledge gap may remain and a consensus statement will help to guide patient management. We determined scientific questions which need to be addressed in the future in order to generate evidence and to inform physicians and caregivers in daily clinical practice in order to improve the outcomes of patients with sarcoma. We conducted a vote on STS key questions and controversies prior to the CSSS meeting, which took place in May 2022., Results: Sixty-two European sarcoma experts participated in the survey. Sixteen strong consensus (≥95%) items were identified by the experts, as well as 30 items with a ≥75% consensus on diagnostic and therapeutic questions. Ultimately, many controversy topics remained without consensus., Conclusions: In this manuscript, we summarise the voting results and the discussion during the CSSS meeting. Future scientific questions, priorities for clinical trials, registries, quality assurance, and action by stakeholders are proposed. Platforms and partnerships can support innovative approaches to improve management and clinical research in STS., Competing Interests: Conflict of interest statement The authors declare following conflicts of interest: CR: Consulting or Advisory Role (Payment to Institution): Bayer (Schweiz) AG, Bristol-Myers Squibb, Ipsen, MSD Oncology, Pfizer; Consulting or Advisory Role (Payment to me): Merck (Schweiz) AG; Travel Expenses (Payment to me): PharmaMar; Research Funding (Payment to Institution): Astellas Pharma AG. DA: has received institutional research support from Implantcast GmbH and invited speaker fees from PharmaMar.WGK: served as advisor to Bristol Myers Squibb, unrelated. JYB: grants or research support from AstraZeneca, Bayer, Bristol Myers Squibb, Deciphera, Glaxo SmithKline, Merck Sharp & Dohme, Novartis, PharmaMar, and Roche; has an advisory or board member role in Innate Pharma; and receives honorarium from Bayer, Deciphera, Novartis, and Roche. ID: no COI. PD: Honoraria for consultancy or advisory role: Ayala Pharmaceuticals, Deciphera. RH: no COI. JJ: has received travel expenses or honoraria for participation in advisory boards or invited speaker fees from PharmaMar, Pfizer and Belpharma. LHL: Honoraria and compensation for advisory boards: Boehringer Ingelheim, Novartis, Lilly, Eisai, EL Medconsult, Roche; Travel and research grants: Dr. Sennewald Medizintechnik; Travel grants: PharmaMar; Stockownerships: Thermosome (co-founder) CM: no COI. ABM: Trustee Sarcoma UK, no other conflict of interest. TB: reports personal fees from PharmaMar and Deciphera (lecture fee, advisory board) ABM: Trustee Sarcoma UK, no other conflict of interest. RLJ: Receipt of grants/research support: MSD, GSK. Receipt of consultation fees: Adaptimmune, Astex, Athenex, Bayer, Boehringer Ingelheim, Blueprint, Clinigen, Eisai, Epizyme, Daichii, Deciphera, Immunedesign, Immunicum, Karma Oncology, Lilly, Merck, Mundipharma, Pharmamar, Springworks. SynOx, Tracon, Up to Date. IJ: No conflicts of interest. PR: has received honoraria for participation in advisory boards for Bayer, Novartis, Roche, Deciphera, Mundibiopharma, PharmaMar, Blueprint Medicines, GSK, Boehringer Ingelheim and invited speaker fees from Clinigen, Deciphera, PharmaMar, Boehringer Ingelheim. JS: Participation in advisory boards or invited speaker fees: PharmaMar, Bayer, Roche, Lilly, Amgen; Travel expenses: PharmaMar, Roche, Lilly, Amgen, Bristol Myers Squibb; Research funding: PharmaMar, Roche, Eisai. WvdG: research grant funding from Eli Lilly, advisory board participation Springworks, Bayer, PTC Therapeutics – all to the institute. WJvH: received institutional honoraria for participation in advisory board for Belpharma, invited speaker fees from Amgen andreports expert testimony for Sanofi and MSD and personal travel grant from Novartis and institutional research grant from BMS. EW: has received honoraria for participation in advisory boards or invited speaker fees from Bayer, Roche, Deciphera, Boehringer Ingelheim, Bristol Myers Squibb, PharmaMar. SH: no COI., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 mm 2 /s, and the optimum high b-value (b high ) for ADC estimation was 1100 (10th-90th percentile: 740-1790) s/mm 2 . At higher b-values, geometric distortion increased, and longer echo times were required, leading to reduced signal., Conclusions: Theoretical optimisation gave an optimum b high of 1100 (10th-90th percentile: 740-1790) s/mm 2 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; b high in the range 750-1100 s/mm 2 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/mm 2 for ADC estimation in metastatic melanoma. • Considering geometric distortion and minimum echo time (TE), a b-value in the range 750-1100 s/mm 2 is recommended. • Sites should evaluate the performance of specific scanners to assess the effect of geometric distortion and minimum TE., (© 2022. The Author(s).)

Background: Magnetic resonance imaging (MRI) can be used to target tumour components in biopsy procedures, while the ability to precisely correlate histology and MRI signal is crucial for imaging biomarker validation. Robotic MRI/computed tomography (CT) fusion biopsy offers the potential for this without in-gantry biopsy, although requires development., Methods: Test-retest T1 and T2 relaxation times, attenuation (Hounsfield units, HU), and biopsy core quality were prospectively assessed (January-December 2021) in a range of gelatin, agar, and mixed gelatin/agar solutions of differing concentrations on days 1 and 8 after manufacture. Suitable materials were chosen, and four biopsy phantoms were constructed with twelve spherical 1-3-cm diameter targets visible on MRI, but not on CT. A technical pipeline was developed, and intraoperator and interoperator reliability was tested in four operators performing a total of 96 biopsies. Statistical analysis included T1, T2, and HU repeatability using Bland-Altman analysis, Dice similarity coefficient (DSC), and intraoperator and interoperator reliability., Results: T1, T2, and HU repeatability had 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%, respectively. The phantom was highly reproducible, with DSC of 0.93 versus 0.92 for scanning the same or two different phantoms, respectively. Hit rate was 100% (96/96 targets), and all operators performed robotic biopsies using a single volumetric acquisition. The fastest procedure time was 32 min for all 12 targets., Conclusions: A reproducible biopsy phantom was developed, validated, and used to test robotic MRI/CT-fusion biopsy. The technique was highly accurate, reliable, and achievable in clinically acceptable timescales meaning it is suitable for clinical application., (© 2022. The Author(s) under exclusive licence to European Society of Radiology.)

Introduction: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods., Methods and Analysis: This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response., Ethics and Dissemination: MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination., Trial Registration Number: NCT03574454., Competing Interests: Competing interests: AR receives honoraria for educational lecture at Garmisch International Symposium, has an unpaid role on the European Society of Radiology Board of Directors and receives travel cost support where necessary. BG receives grants from other entities; EU commission and UKRI London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, is a Scientific advisor for Kheiron Medical Technologies (January 2018–September 2021) and receives stock options as part of standard employment packages from both Kheiron Medical Technologies and HeartFlow. EA has a patent pending for Machine Learning in Alzheimer’s disease and has a role on the scientific advisory board for Radiopharm Theranostics Limited. MK receives grants from both Myeloma UK and Celgene/BMS, and consulting fees or payments from AbbVie, BMS/Celgene, Janssen, GSK, Karyopharm, Takeda and Seagen. CM & DK receive additional funding as a co-investigator on a radiology NIHR study and is part of the joint venture Celescan with the Royal Marsden, The Institute of Cancer Research and Sopra Steria. TB receives additional funding from CRUK grant funding (NCITA) and NIHR (HTA) and receives honoraria from Bayer., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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/mm 2 ). 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., Competing Interests: Declaration of competing interest Konstantinos Zormpas-Petridis and Matthew D Blackledge have submitted a patent to the Hellenic Industrial Property Organisation directly regarding the work described in this article. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Invasive lobular breast carcinomas (ILC) account for approximately 15% of breast cancer diagnoses. They can be difficult to diagnose both clinically and radiologically, due to their infiltrative growth pattern. The pattern of metastasis of ILC is unusual, with spread to the serosal surfaces (pleura and peritoneum), retroperitoneum and gastrointestinal (GI)/genitourinary (GU) tracts and a higher rate of leptomeningeal spread than IDC. Routine staging and response assessment with computed tomography (CT) can be undertaken quickly and measurements can be reproduced easily, but this is challenging with metastatic ILC as bone-only/bone-predominant patterns are frequently seen and assessment of the disease status is limited in these scenarios. Functional imaging such as whole-body MRI (WBMRI) allows the assessment of bone and soft tissue disease by providing functional information related to differences in cellular density between malignant and benign tissues. A number of recent studies have shown that WBMRI can detect additional sites of disease in metastatic breast cancer (MBC), resulting in a change in systemic anti-cancer therapy. Although WBMRI and fluorodeoxyglucose-positron-emission tomography-computed tomography (FDG-PET/CT) have a comparable performance in the assessment of MBC, WBMRI can be particularly valuable as a proportion of ILC are non-FDG-avid, resulting in the underestimation of the disease extent. In this review, we explore the added value of WBMRI in the evaluation of metastatic ILC and compare it with other imaging modalities such as CT and FDG-PET/CT. We also discuss the spectrum of WBMRI findings of the different metastatic sites of ILC with CT and FDG-PET/CT correlation. KEY POINTS: • ILC has an unusual pattern of spread compared to IDC, with metastases to the peritoneum, retroperitoneum and GI and GU tracts, but the bones and liver are the commonest sites. • WBMRI allows functional assessment of metastatic disease, particularly in bone-only and bone-predominant metastatic cancers such as ILC where evaluation with CT can be challenging and limited. • WBMRI can detect more sites of disease compared with CT, can reveal disease progression earlier and provides the opportunity to change ineffective systemic treatment sooner., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Whole-body (WB) MRI, which includes diffusion-weighted imaging (DWI) and T 1 -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., (© 2022. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Thrussell, Winfield, Orton, Miah, Zaidi, Arthur, Thway, Strauss, Collins, Koh, Oelfke, Huang, O’Connor, Messiou and Blackledge.)

Introduction: The spleen is a lymphoid organ and we hypothesize that clinical benefit to immunotherapy may present with an increase in splenic volume during treatment. The purpose of this study was to investigate whether changes in splenic volume could be observed in those showing clinical benefit versus those not showing clinical benefit to pembrolizumab treatment in non-small cell lung cancer (NSCLC) patients., Materials and Methods: In this study, 70 patients with locally advanced or metastatic NSCLC treated with pembrolizumab; and who underwent baseline CT scan within 2 weeks before treatment and follow-up CT within 3 months after commencing immunotherapy were retrospectively evaluated. The splenic volume on each CT was segmented manually by outlining the splenic contour on every image and the total volume summated. We compared the splenic volume in those achieving a clinical benefit and those not achieving clinical benefit, using non-parametric Wilcoxon signed-rank test. Clinical benefit was defined as stable disease or partial response lasting for greater than 24 weeks. A p-value of <0.05 was considered statistically significant., Results: There were 23 responders and 47 non-responders based on iRECIST criteria and 35 patients with clinical benefit and 35 without clinical benefit. There was no significant difference in the median pre-treatment volume (175 vs 187 cm3, p = 0.34), post-treatment volume (168 vs 167 cm3, p = 0.39) or change in splenic volume (-0.002 vs 0.0002 cm3, p = 0.97) between the two groups. No significant differences were also found between the splenic volume of patients with partial response, stable disease or progressive disease (p>0.017). Moreover, there was no statistically significant difference between progression-free survival and time to disease progression when the splenic volume was categorized as smaller or larger than the median pre-treatment or post-treatment volume (p>0.05)., Conclusion: No significant differences were observed in the splenic volume of those showing clinical benefit versus those without clinical benefit to pembrolizumab treatment in NSCLC patients. CT splenic volume cannot be used as a potentially simple biomarker of response to immunotherapy., Competing Interests: I have read the journal’s policy and one of the authors of this manuscript (Anna Minchom) have the following competing interests: has served on advisory boards for Janssen Pharmaceuticals and Merck Pharmaceuticals, has received honoraria from Chugai Pharmaceuticals, Novartis Oncology, Faron Pharmaceuticals, Bayer Pharmaceuticals, has received expenses from Amgen Pharmaceuticals and LOXO Oncology. The other authors have declared that no competing interests exist. that this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Multiple myeloma and its precursor and variant types represent some of the most common hematologic malignancies in adults. These plasma cell dyscrasias are well-known in modern medicine. There are well-established clinical, laboratory, and pathologic criteria for diagnosis and staging. There is debate about the diagnosis of some of the earliest cases of myeloma described in the literature. We present a critical review of one such case.

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., Competing Interests: RJ is the recipient of grants/research support from Merck Sharp & Dohme and GlaxoSmithKline. RJ is the recipient of consultation fees from Adaptimmune, Athenex, Blueprint Medicines, Clinigen, Eisai, Epizyme, Daichii, Deciphera, Immune Design, Lilly, Merck, PharmaMar and UpToDate. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Arthur, Johnston, Winfield, Blackledge, Jones, Huang and Messiou.)

The last decades saw a great innovation in computer vision. Recently, the field has been fundamental in the development of autonomous navigation systems. Modern assistive technologies, like smart wheelchairs, could employ autonomous navigation to assist users during operation. A prerequisite for such systems is to recognise the navigable space in real-time. The current research features an off-the-shelf powered wheelchair customised into an intelligent robot, which perceives the environment using Point Cloud Semantic Segmentation (PCSS). The implemented algorithm is used to distinguish between two conditions, traversable and non-traversable space, in real-time, using the aforementioned conditions as the two labelled classes. The accuracy of traversable space detection resulted as 99.64% while the accuracy of non-traversable space detection was 91.79%. The performance of the suggested method was invariant to changes in wheelchair velocity indicating that the latency of the suggested algorithm is within the tolerable limits for real-time operation.