CT-based radiomic prognostic vector (RPV) predicts survival and stromal histology in high-grade serous ovarian cancer: an external validation study.

Objectives: In women with high-grade serous ovarian cancer (HGSOC), a CT-based radiomic prognostic vector (RPV) predicted stromal phenotype and survival after primary surgery. The study's purpose was to fully externally validate RPV and its biological correlate.

Materials And Methods: In this retrospective study, ovarian masses on CT scans of HGSOC patients, who underwent primary cytoreductive surgery in an ESGO-certified Center between 2002 and 2017, were segmented for external RPV score calculation and then correlated with overall survival (OS) and progression-free survival (PFS).

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.

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.

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.

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.

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.

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.

Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment.

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.

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.

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

There is a clinical need for F-labeled somatostatin analogs for the imaging of neuroendocrine tumors (NET), given the limitations of using [Ga]Ga-DOTA-peptides, particularly with regard to widespread accessibility. We have shown that [F]fluoroethyl-triazole-[Tyr]-octreotate ([F]FET-βAG-TOCA) has favorable dosimetry and biodistribution. As a step toward clinical implementation, we conducted a prospective, noninferiority study of [F]FET-βAG-TOCA PET/CT compared with [Ga]Ga-DOTA- peptide PET/CT in patients with NET.

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.

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.

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.

The green and sustainable radiology department.

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.

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.

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.

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.

Purpose: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images.

Methods: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed.

Adnexal Lesion Imaging: Past, Present, and Future.

Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation.