Planetary Health and Radiology: Why We Should Care and What We Can Do.

Climate change adversely affects the well-being of humans and the entire planet. A planetary health framework recognizes that sustaining a healthy planet is essential to achieving individual, community, and global health. Radiology contributes to the climate crisis by generating greenhouse gas (GHG) emissions during the production and use of medical imaging equipment and supplies. To promote planetary health, strategies that mitigate and adapt to climate change in radiology are needed. Mitigation strategies to reduce GHG emissions include switching to renewable energy sources, refurbishing rather than replacing imaging scanners, and powering down unused scanners. Radiology departments must also build resiliency to the now unavoidable impacts of the climate crisis. Adaptation strategies include education, upgrading building infrastructure, and developing departmental sustainability dashboards to track progress in achieving sustainability goals. Shifting practices to catalyze these necessary changes in radiology requires a coordinated approach. This includes partnering with key stakeholders, providing effective communication, and prioritizing high-impact interventions. This article reviews the intersection of planetary health and radiology. Its goals are to emphasize why we should care about sustainability, showcase actions we can take to mitigate our impact, and prepare us to adapt to the effects of climate change. © RSNA, 2024 Supplemental material is available for this article. See also the article by Ibrahim et al in this issue. See also the article by Lenkinski and Rofsky in this issue.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Robust prostate disease classification using transformers with discrete representations.

PURPOSE: Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI. METHOD: We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images. RESULTS: We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space. CONCLUSION: We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.

A geometric approach to robust medical image segmentation.

Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances. We posit that the restricted anatomical differences between subjects could be harnessed to refine the latent space into a set of shape components. The learned set then aims to encompass the relevant anatomical shape variation found within the patient population. We explore this by utilising multiple MRI sequences to learn texture invariant and shape equivariant features which are used to construct a shape dictionary using vector quantisation. We investigate shape equivariance to a number of different types of groups. We hypothesise and prove that the greater the group order, i.e., the denser the constraint, the better becomes the model robustness. We achieve shape equivariance either with a contrastive based approach or by imposing equivariant constraints on the convolutional kernels. The resulting shape equivariant dictionary is then sampled to compose the segmentation output. Our method achieves state-of-the-art performance for the task of single domain generalisation for prostate and cardiac MRI segmentation. Code is available at https://github.com/AinkaranSanthi/A_Geometric_Perspective_For_Robust_Segmentation.

Translating radiological research into practice-from discovery to clinical impact.

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 assessment of technical feasibility and diagnostic accuracy, and-in a multidisciplinary approach-address clinical unmet needs.Key points• Multiple factors are essential for radiological research to make a clinical and societal impact.• Radiological research needs to go beyond diagnostic accuracy and address unmet clinical needs.• Radiologists should take the lead in radiological studies with a multidisciplinary approach.

Apparent diffusion coefficient analysis of solid tissue helps distinguish borderline from invasive malignant adnexal masses rated O-RADS MRI 4.

PURPOSE: The purpose of this study was to evaluate the contribution of apparent diffusion coefficient (ADC) analysis of the solid tissue of adnexal masses to optimize tumor characterization and possibly refine the risk stratification of the O-RADS MRI 4 category. MATERIALS AND METHODS: The EURAD cohort was retrospectively analyzed to select all patients with an adnexal mass with solid tissue and feasible ADC measurements. Two radiologists independently measured the ADC values of solid tissue, excluding necrotic areas, surrounding structures, and magnetic susceptibility artifacts. Significant differences in diffusion quantitative parameters in the overall population and according to the morphological aspect of solid tissue were analyzed to identify its impact on ADC reliability. Receiver operating characteristics curve (ROC) was used to determine the optimum cutoff of the ADC for distinguishing invasive from non-invasive tumors in the O-RADS MRI score 4 population. RESULTS: The final study population included 180 women with a mean age of 57 ± 15.5 (standard deviation) years; age range: 19-95 years) with 93 benign, 23 borderline, and 137 malignant masses. The median ADC values of solid tissue was greater in borderline masses (1.310 × 10-3 mm2/s (Q1, Q3: 1.152, 1.560 × 10-3 mm2/s) than in benign masses (1.035 × 10-3 mm2/s; Q1, Q3: 0.900, 1.560 × 10-3 mm2/s) (P= 0.002) and in benign tumors compared by comparison with invasive masses (0.850 × 10-3 mm2/s; Q1, Q3: 0.750, 0.990 × 10-3 mm2/s) (P < 0.001). Solid tissue corresponded to irregular septa or papillary projection in 18.6% (47/253), to a mural nodule or a mixed mass in 46.2% (117/253), and to a purely solid mass in 35.2% (89/253) of adnexal masses. In mixed masses or masses with mural nodule subgroup, invasive masses had a significantly lower ADC (0.830 × 10-3 mm2/s (Q1, Q3: 0.738, 0.960) than borderline (1.385; Q1, Q3: 1.300, 1.930) (P= 0.0012) and benign masses (P= 0.04). An ADC cutoff of 1.08 × 10-3 mm2/s yielded 71.4% sensitivity and 100% specificity for identifying invasive lesions in the mixed or mural nodule subgroup with an AUC of 0.92 (95% confidence interval: 0.76-0.99). CONCLUSION: ADC analysis of solid tissue of adnexal masses could help distinguish invasive masses within the O-RADS MRI 4 category, especially in mixed masses or those with mural nodule.

O-RADS MRI to classify adnexal tumors: from clinical problem to daily use.

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 indeterminate adnexal lesions following ultrasonography.• The multicentric adnexal study externally validated the score creating the O-RADS MR score and leading to its inclusion for daily use in international guidelines.

O-RADS MRI risk stratification system: pearls and pitfalls.

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.

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice.

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 intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.

O-RADS MRI scoring system has the potential to reduce the frequency of avoidable adnexal surgery.

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.

Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.

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".

Somatostatin Receptor Imaging with [18F]FET-ßAG-TOCA PET/CT and [68Ga]Ga-DOTA-Peptide PET/CT in Patients with Neuroendocrine Tumors: A Prospective, Phase 2 Comparative Study.

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-ßAG-TOCA) has favorable dosimetry and biodistribution. As a step toward clinical implementation, we conducted a prospective, noninferiority study of [18F]FET-ßAG-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-ßAG-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-ßAG-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-ßAG-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-ßAG-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-ßAG-TOCA was not inferior to [68Ga]Ga-DOTA-peptide in visualizing NET and may be considered in routine clinical practice given the longer half-life and availability of the cyclotron-produced fluorine radioisotope.