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274 results found
Nougaret S, Razakamanantsoa L, Sadowski EA, et al., 2024, O-RADS MRI risk stratification system: pearls and pitfalls, Insights into Imaging, Vol: 15
In 2021, the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Committee developed a risk stratification system and lexicon for assessing adnexal lesions using MRI. Like the BI-RADS classification, O-RADS MRI provides a standardized language for communication between radiologists and clinicians. It is essential for radiologists to be familiar with the O-RADS algorithmic approach to avoid misclassifications. Training, like that offered by International Ovarian Tumor Analysis (IOTA), is essential to ensure accurate and consistent application of the O-RADS MRI system. Tools such as the O-RADS MRI calculator aim to ensure an algorithmic approach. This review highlights the key teaching points, pearls, and pitfalls when using the O-RADS MRI risk stratification system. Critical relevance statement This article highlights the pearls and pitfalls of using the O-RADS MRI scoring system in clinical practice. Key points • Solid tissue is described as displaying post- contrast enhancement. • Endosalpingeal folds, fimbriated end of the tube, smooth wall, or septa are not solid tissue. • Low-risk TIC has no shoulder or plateau. An intermediate-risk TIC has a shoulder and plateau, though the shoulder is less steep compared to outer myometrium. Graphical Abstract: (Figure presented.).
Ross J, Hammouche S, Chen Y, et al., 2024, Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment., Clin Radiol, Vol: 79, Pages: 338-345
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors such as cost-effectiveness and institutional information technology support. When a technology is new and relatively untested in a field, professional confidence is lacking and there is a sense of the need to go above the baseline level of validation and compliance. In this article, we propose an approach that goes beyond standard regulatory compliance for AI apps that are approved for marketing, including independent benchmarking in the lab as well as clinical audit in practice, with the aims of increasing trust and preventing harm.
Dubash S, Barwick TD, Kozlowski K, et al., 2024, Somatostatin receptor imaging with [18F]FET-bAG-TOCAPET/CT and [68Ga]Ga-DOTA-peptide PET/CT in patientswith neuroendocrine tumors: a prospective, phase 2comparative study, The Journal of Nuclear Medicine, Vol: 65, Pages: 416-422, ISSN: 0161-5505
There is a clinical need for 18F-labeled somatostatin analogs for the imaging of neuroendocrine tumors (NET), given the limitations of using [68Ga]Ga-DOTA-peptides, particularly with regard to widespread accessibility. We have shown that [18F]fluoroethyl-triazole-[Tyr3]-octreotate ([18F]FET-βAG-TOCA) has favorable dosimetry and biodistribution. As a step toward clinical implementation, we conducted a prospective, noninferiority study of [18F]FET-βAG-TOCA PET/CT compared with [68Ga]Ga-DOTA- peptide PET/CT in patients with NET. Methods: Forty-five patients with histologically confirmed NET, grades 1 and 2, underwent PET/CT imaging with both [18F]FET-βAG-TOCA and [68Ga]Ga-peptide performed within a 6-mo window (median, 77 d; range, 6–180 d). Whole-body PET/CT was conducted 50 min after injection of 165 MBq of [18F]FET-βAG-TOCA. Tracer uptake was evaluated by comparing SUVmax and tumor-to-background ratios at both lesion and regional levels by 2 unblinded, experienced readers. A randomized, blinded reading of both scans was also then undertaken by 3 experienced readers, and consensus was assessed at a regional level. The ability of both tracers to visualize liver metastases was also assessed. Results: A total of 285 lesions were detected on both imaging modalities. An additional 13 tumor deposits were seen in 8 patients on [18F]FET-βAG-TOCA PET/CT, and [68Ga]Ga-DOTA-peptide PET/CT detected an additional 7 lesions in 5 patients. Excellent correlation in SUVmax was observed between both tracers (r = 0.91; P < 0.001). No difference was observed between median SUVmax across regions, except in the liver, where the median tumor-to-background ratio of [18F]FET-βAG-TOCA was significantly lower than that of [68Ga]Ga-DOTA-peptide (2.5 ± 1.9 vs. 3.5 ± 2.3; P < 0.001). Conclusion: [18F]FET-βAG-TOCA was not inferior to [68Ga]Ga-DOTA-peptide in visualizing NET and may be considered in rout
Dabi Y, Rockall A, Razakamanantsoa L, et al., 2024, O-RADS MRI scoring system has the potential to reduce the frequency of avoidable adnexal surgery., Eur J Obstet Gynecol Reprod Biol, Vol: 294, Pages: 135-142
OBJECTIVE: To assess the potential impact of the O-RADS MRI score on the decision-making process for the management of adnexal masses. METHODS: EURAD database (prospective, European observational, multicenter study) was queried to identify asymptomatic women without history of infertility included between March 1st and March 31st 2018, with available surgical pathology or clinical findings at 2-year clinical follow-up. Blinded to final diagnosis, we stratified patients into five categories according to the O-RADS MRI score (absent i.e. non adnexal, benign, probably benign, indeterminate, probably malignant). Prospective management was compared to theoretical management according to the score established as following: those with presumed benign masses (scored O-RADS MRI 2 or 3) (follow-up recommended) and those with presumed malignant masses (scored O-RADS MRI 4 or 5) (surgery recommended). RESULTS: The accuracy of the score for assessing the origin of the mass was of 97.2 % (564/580, CI95% 0.96-0.98) and was of 92.0 % (484/526) for categorizing lesions with a negative predictive value of 98.1 % (415/423, CI95% 0.96-0.99). Theoretical management using the score would have spared surgery in 229 patients (87.1 %, 229/263) with benign lesions and malignancy would have been missed in 6 borderline and 2 invasive cases. In patients with a presumed benign mass using O-RADS MRI score, recommending surgery for lesions >= 100 mm would miss only 4/77 (4.8 %) malignant adnexal tumors instead of 8 (50 % decrease). CONCLUSION: The use of O-RADS MRI scoring system could drastically reduce the number of asymptomatic patients undergoing avoidable surgery.
Doran SJ, Barfoot T, Wedlake L, et al., 2024, Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data., Insights Imaging, Vol: 15, ISSN: 1869-4101
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 l
Boverhof B-J, Redekop WK, Bos D, et al., 2024, Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice., Insights Imaging, Vol: 15, ISSN: 1869-4101
OBJECTIVE: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. METHODS: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. RESULTS: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. CONCLUSION: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. CRITICAL RELEVANCE STATEMENT: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. KEYPOINTS: • Radiology artificial intelligen
Dabi Y, Rockall A, Sadowski E, et al., 2024, O-RADS MRI to classify adnexal tumors: from clinical problem to daily use., Insights Imaging, Vol: 15, ISSN: 1869-4101
Eighteen to 35% of adnexal masses remain non-classified following ultrasonography, leading to unnecessary surgeries and inappropriate management. This finding led to the conclusion that ultrasonography was insufficient to accurately assess adnexal masses and that a standardized MRI criteria could improve these patients' management. The aim of this work is to present the different steps from the identification of the clinical issue to the daily use of a score and its inclusion in the latest international guidelines. The different steps were the following: (1) preliminary work to formalize the issue, (2) physiopathological analysis and finding dynamic parameters relevant to increase MRI performances, (3) construction and internal validation of a score to predict the nature of the lesion, (4) external multicentric validation (the EURAD study) of the score named O-RADS MRI, and (5) communication and education work to spread its use and inclusion in guidelines. Future steps will include studies at patients' levels and a cost-efficiency analysis. Critical relevance statement We present translating radiological research into a clinical application based on a step-by-step structured and systematic approach methodology to validate MR imaging for the characterization of adnexal mass with the ultimate step of incorporation in the latest worldwide guidelines of the O-RADS MRI reporting system that allows to distinguish benign from malignant ovarian masses with a sensitivity and specificity higher than 90%. Key points • The initial diagnostic test accuracy studies show the limitation of a preoperative assessment of adnexal masses using solely ultrasonography.• The technical developments (DCE/DWI) were investigated with the value of dynamic MRI to accurately predict the nature of benign or malignant lesions to improve management.• The first developing score named ADNEX MR Score was constructed using multiple easily assessed criteria on MRI to classify indeterm
Smits M, Rockall A, Constantinescu SN, et al., 2024, Translating radiological research into practice-from discovery to clinical impact., Insights Imaging, Vol: 15, ISSN: 1869-4101
At the European Society of Radiology (ESR), we strive to provide evidence for radiological practices that improve patient outcomes and have a societal impact. Successful translation of radiological research into clinical practice requires multiple factors including tailored methodology, a multidisciplinary approach aiming beyond technical validation, and a focus on unmet clinical needs. Low levels of evidence are a threat to radiology, resulting in low visibility and credibility. Here, we provide the background and rationale for the thematic series Translating radiological research into practice-from discovery to clinical impact, inviting authors to describe their processes of achieving clinically impactful radiological research. We describe the challenges unique to radiological research. Additionally, a survey was sent to non-radiological clinical societies. The majority of respondents (6/11) were in the field of gastrointestinal/abdominal medicine. The implementation of CT/MRI techniques for disease characterisation, detection and staging of cancer, and treatment planning and radiological interventions were mentioned as the most important radiological developments in the past years. The perception was that patients are substantially unaware of the impact of these developments. Unmet clinical needs were mostly early diagnosis and staging of cancer, microstructural/functional assessment of tissues and organs, and implant assessment. All but one respondent considered radiology important for research in their discipline, but five indicated that radiology is currently not involved in their research. Radiology research holds the potential for being transformative to medical practice. It is our responsibility to take the lead in studies including radiology and strive towards the highest levels of evidence.Critical relevance statement For radiological research to make a clinical and societal impact, radiologists should take the lead in radiological studies, go beyond the
Santhirasekaram A, Winkler M, Rockall A, et al., 2024, Hierarchical Compositionality in Hyperbolic Space for Robust Medical Image Segmentation, Pages: 52-62, ISSN: 0302-9743
Deep learning based medical image segmentation models need to be robust to domain shifts and image distortion for the safe translation of these models into clinical practice. The most popular methods for improving robustness are centred around data augmentation and adversarial training. Many image segmentation tasks exhibit regular structures with only limited variability. We aim to exploit this notion by learning a set of base components in the latent space whose composition can account for the entire structural variability of a specific segmentation task. We enforce a hierarchical prior in the composition of the base components and consider the natural geometry in which to build our hierarchy. Specifically, we embed the base components on a hyperbolic manifold which we claim leads to a more natural composition. We demonstrate that our method improves model robustness under various perturbations and in the task of single domain generalisation.
Rockall AG, Li X, Johnson N, et al., 2023, Development and evaluation of machine learning in whole-body magnetic resonance imaging for detecting metastases in patients with lung or colon cancer: a diagnostic test accuracy study, Investigative Radiology, Vol: 58, Pages: 823-831, ISSN: 0020-9996
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 spec
Ponnaiah M, Bhatnagar T, Abdulkader RS, et al., 2023, Authors' response., Indian J Med Res, Vol: 158, Pages: 505-508
Mariampillai J, Rockall A, Manuellian C, et al., 2023, The green and sustainable radiology department., Radiologie (Heidelb), Vol: 63, Pages: 21-26
As manmade climate change threatens the health of the planet, it is important that we understand and address the contribution of healthcare to global emissions. Medical imaging is a significant contributor to overall emissions. This article aims to highlight key issues and examples of sustainable practices, in order to empower radiologists to make a change within their department.
Aboagye E, Lu H, Lou H, et al., 2023, Tumour and local lymphoid tissue interaction determines prognosis in high grade serous ovarian cancer, Cell Reports Medicine, Vol: 4, Pages: 1-24, ISSN: 2666-3791
Tertiary lymphoid structure (TLS) is associated with prognosis in copy number-driven tumours,including high grade serous ovarian cancer (HGSOC), although the function of TLS and its interactionwith copy-number alterations in HGSOC is not fully understood. In the current study, we confirmthat TLS-high HGSOC patients show significantly better progression free survival. We show thatpresence of TLS in HGSOC tumours is associated with B-cell maturation and cytotoxic tumourspecific T-cells activation and proliferation. Additionally, the copy number loss of IL15 and CXCL10may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed.Manuscript Click here to view linked ReferencesLastly, a radiomics-based signature is developed to predict presence of TLS, which independentlypredicts PFS in both HGSOC patients and ICI-treated NSCLC patients. Overall, we reveal that TLScoordinates intratumoural B-cell and T-cell response against HGSOC tumour, while cancer genomeevolves to counteract TLS formation and function.
Sadowski EA, Thomassin-Naggara I, Rockall A, et al., 2023, O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee (vol 303, pg 35, 2022), RADIOLOGY, Vol: 308, ISSN: 0033-8419
Taylor SA, Darekar A, Goh V, et al., 2023, NIHR Imaging Group. Who are we and what do we do?, CLINICAL RADIOLOGY, Vol: 78, ISSN: 0009-9260
Li X, Marcus D, Russell J, et al., 2023, An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer, Journal of Magnetic Resonance Imaging, Vol: 57, Pages: 1922-1933, ISSN: 1053-1807
Background:Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.Purpose:To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study Type:Retrospective.Population:Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence:1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence.Assessment:Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical Tests:A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.Results:Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respective
Raison N, Servian P, Patel A, et al., 2023, Is tumour volume an independent predictor of outcome after radical prostatectomy for high-risk prostate cancer?, PROSTATE CANCER AND PROSTATIC DISEASES, Vol: 26, Pages: 282-286, ISSN: 1365-7852
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Vithayathil M, Vaidyanathan A, Ocal O, et al., 2023, Application of deep learning auto-segmentation and unsupervised machine learning in developing a radiomic prognostic score to predict disease recurrence post radiofrequency ablation for hepatocellular carcinoma, Publisher: ELSEVIER, Pages: S576-S576, ISSN: 0168-8278
Sadowski EA, Rockall A, Thomassin-Naggara I, et al., 2023, Adnexal Lesion Imaging: Past, Present, and Future, RADIOLOGY, Vol: 307, ISSN: 0033-8419
Thomassin-Naggara I, Razakamanantsoa L, Rockall A, 2023, O-RADS MRI: where are we and where we are going?, EUROPEAN RADIOLOGY, ISSN: 0938-7994
Li X, Aboagye E, Michele D, et al., 2023, Prediction of deep myometrial infiltration, clinical risk category, histological type, and lymphovascular space invasion in women with endometrial cancer based on clinical and T2-weighted MRI radiomic features, Cancers, Vol: 15, ISSN: 2072-6694
Deep myometrial infiltration, clinical risk score, histological type, and lymphovascular space invasion are important clinical variables that have significant management implications for endometrial cancer patients. Determination of these factors using pure T2-weighted MRI is time-consuming, and the accuracy of this relies on the experience of the clinicians. Combining clinical information and radiomic features from MRI, we developed machine learning classification models to predict these clinical variables. Based on a training dataset, an automatic selection classification model with an optimized hyperparameters method was adopted to find the optimal classifiers. The accuracy of the model predictions was evaluated using an independent external testing dataset. The results suggest that an integrated model (combining clinical and radiomic features) achieved a reasonable accuracy for endometrial cancer clinical variable prediction. The application of these models in clinical practice could potentially lead to cost reductions and personalized treatment.
Santhirasekaram A, Kori A, Winkler M, et al., 2023, Robust Hierarchical Symbolic Explanations in Hyperbolic Space for Image Classification, Computer Vision and Pattern Recognition
Lu H, Wengert G, Lou H, et al., 2023, Tumour and local lymphoid tissue interaction determines prognosis in high grade serous ovarian cancer, 114th Annual Meeting of the American Association for Cancer Research (AACR), Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472
Rockall AG, Shelmerdine SC, Chen M, 2023, AI and ML in radiology: Making progress, CLINICAL RADIOLOGY, Vol: 78, Pages: 81-82, ISSN: 0009-9260
Rockall AG, Jalaguier-Coudray A, Thomassin-Naggara I, 2023, MR imaging of the Adnexa: Technique and Imaging Acquisition., Magn Reson Imaging Clin N Am, Vol: 31, Pages: 149-161
MR imaging has a high diagnostic accuracy and reproducibility to classify adnexal masses as benign or malignant, using a risk stratification scoring system, the Ovarian-Adnexal Reporting and Data System (O-RADS) MR imaging score. The first step in achieving high accuracy is to ensure high technical quality of the MR scan. The sequences needed are clearly described in this article, with tips for handling difficult cases. This information will assist in obtaining the best possible images, to allow for accurate use of the O-RADS MR imaging risk score.
Silkens MEWM, Ross J, Hall M, et al., 2023, The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications, CLINICAL RADIOLOGY, Vol: 78, Pages: 107-114, ISSN: 0009-9260
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Santhirasekaram A, Pinto K, Winkler M, et al., 2023, A Sheaf Theoretic Perspective for Robust Prostate Segmentation, Pages: 249-259, ISSN: 0302-9743
Deep learning based methods have become the most popular approach for prostate segmentation in MRI. However, domain variations due to the complex acquisition process result in textural differences as well as imaging artefacts which significantly affects the robustness of deep learning models for prostate segmentation across multiple sites. We tackle this problem by using multiple MRI sequences to learn a set of low dimensional shape components whose combinatorially large learnt composition is capable of accounting for the entire distribution of segmentation outputs. We draw on the language of cellular sheaf theory to model compositionality driven by local and global topological correctness. In our experiments, our method significantly improves the domain generalisability of anatomical and tumour segmentation of the prostate. Code is available at https://github.com/AinkaranSanthi/A-Sheaf-Theoretic-Perspective-for-Robust-Segmentation.git.
Sadowski EA, Stein EB, Thomassin-Naggara I, et al., 2023, O-RADS MRI After Initial Ultrasound for Adnexal Lesions: <i>AJR</i> Expert Panel Narrative Review, AMERICAN JOURNAL OF ROENTGENOLOGY, Vol: 220, Pages: 6-15, ISSN: 0361-803X
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Santhirasekaram A, Winkler M, Rockall A, et al., 2023, Topology Preserving Compositionality for Robust Medical Image Segmentation, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE COMPUTER SOC, Pages: 543-552, ISSN: 2160-7508
Satchwell L, Wedlake L, Greenlay E, et al., 2022, Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study, BMJ Open, Vol: 12, Pages: 1-9, ISSN: 2044-6055
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 informe
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