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The latest developments in Radiology & AI.
10-year mortality predicted by deep learning on chest radiographs
FDA clears AI for multi-disease screening on chest CT (40M/yr reach)
AI flags COPD on routine ECG with AUC 0.82, external validation
AI MRI model boosts early liver cancer detection to 87.9% sensitivity
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FDA Approvals Database
Latest News & Trends
BrainIAC AI Model Excels in Brain MRI Analysis and Disease Prediction
BrainIAC, a new foundation model from Mass General Brigham, outperforms traditional AI approaches in analyzing brain MRI for tasks like brain age estimation and cancer prognosis.
Medicare May Deny Coverage for AI-Based Brain MRI Tools
A Medicare contractor has proposed denying coverage for AI tools used in brain MRI analysis, citing insufficient evidence and data limitations.
AI-Enhanced Structured Reporting Can Speed Up Radiology Turnaround Times
A study found that combining structured reporting templates with AI assistance significantly reduces radiology report turnaround times.
From the Research Hub
An interactive axial feature selection network for medical image classification.
To address the differences and correlations between features, as well as to fully utilize the importance of salient semantics in medical image classification tasks, this paper proposes an Interactive Axial Feature Selection Network (IAFSNet), aimed at improving feature representation, effectively filtering noise during classification, thereby enhancing classification performance. The paper introduces a newly designed Feature Interaction Module (FIM), which learns spatial differences between various features and enhances the interdependence and complementarity between local spatial features and global contextual semantics. Additionally, the paper implements a novel Axial Feature Selection Module (AFSM), which filters salient feature semantics from three perspectives: horizontal, vertical, and spatial. By adjusting thresholds, salient features are emphasized while irrelevant noise is eliminated, allowing these key features to cross-aggregate layer by layer and establish interactions among them, ultimately improving classification accuracy. Experimental results on four benchmark datasets demonstrate that the proposed IAFSNet exhibits excellent classification performance and robustness, significantly outperforming many existing classification methods.
Noninvasive imaging-based assessment of tumor-associated neutrophils for prognosis and immunotherapy response in gastric cancer: a multicenter study.
Tumor-associated neutrophils (TAN) critically promote gastric cancer progression. However, current assessment relies on invasive biopsies that preclude serial monitoring. Noninvasive tools to quantify TAN infiltration are urgently required. To develop and validate a noninvasive, CT-based ensemble machine learning radiomic biomarker for mapping TAN infiltration in gastric cancer, and to assess its utility for prognosis stratification and the prediction of response to anti-PD-1 immunotherapy. In this multicenter study of 2,170 gastric cancer patients across eight cohorts, we developed EnmlbaRB, an ensemble machine-learning-based CT radiomic biomarker. Portal venous-phase scans were processed to extract features, with mRMR-Boruta algorithms identifying 11 radiomic signatures (six peritumoral and five intratumoral signatures). These were integrated via a five-tier heterogeneous stacking architecture supervised by the immunohistochemistry-derived CD66b + TAN status (high/medium/low). The validation spanned six independent cohorts, including 177 anti-PD-1-treated patients. External validation demonstrated robust performance: EnmlbaRB predicted TAN status with an AUC of 0.71 (95%CI: 0.65-0.78) and 80.74% specificity. Critically, TAN-Low patients exhibited significantly superior 5-year overall survival compared to TAN-High across all cohorts (e.g., SYSUCC cohort: 64.12% vs. 46.78%, p < 0.05). In the anti-PD-1 cohorts, the TAN-Low subgroups achieved 1.9-fold higher disease control rates (83.9% vs 44.1%; p < 0.001) and significantly prolonged median progression-free survival (>41.9 vs 6.2 months; HR = 0.162, p < 0.001), establishing clear clinical utility for immunotherapy stratification. This study is the first clinically validated noninvasive solution for mapping the TAN infiltration status in gastric cancer. EnmlbaRB effectively stratified the patients based on survival outcomes and immunotherapy responsiveness. This paradigm empowers clinicians to personalize therapeutic sequencing based on evolving TAN biology, thereby addressing the critical need for adaptive treatment strategies for advanced gastric cancer management.
Predicting Rectal Cancer Patient Survival with Dutch Radiology Reports using Natural Language Processing (NLP): The Role of Pretrained Language Models
The use of Electronic Health Records (EHRs) has increased significantly in recent years. However, a substantial portion of the clinical data remains in unstructured text formats, especially in the context of radiology. This limits the application of EHRs for automated analysis in oncology research. Pretrained language models have been utilized to extract feature embeddings from these reports for downstream clinical applications, such as treatment response and survival prediction. However, a thorough investigation into which pretrained models produce the most effective features for rectal cancer survival prediction has not yet been done. This study explores the performance of five Dutch pretrained language models, including two publicly available models (RobBERT and MedRoBERTa.nl) and three developed in-house for the purpose of this study (RecRoBERT, BRecRoBERT, and BRec2RoBERT) with training on distinct Dutch-only corpora, in predicting overall survival and disease-free survival outcomes in rectal cancer patients. Our results showed that our in-house developed BRecRoBERT, a RoBERTa-based language model trained from scratch on a combination of Dutch breast and rectal cancer corpora, delivered the best predictive performance for both survival tasks, achieving a C-index of 0.65 (0.57, 0.73) for overall survival and 0.71 (0.64, 0.78) for disease-free survival. It outperformed models trained on general Dutch corpora (RobBERT) or Dutch hospital clinical notes (MedRoBERTa.nl). BRecRoBERT demonstrated the potential capability to predict survival in rectal cancer patients using Dutch radiology reports at diagnosis. This study highlights the value of pretrained language models that incorporate domain-specific knowledge for downstream clinical applications. Furthermore, it proves that utilizing data from related domains can improve the quality of feature embeddings for certain clinical tasks, particularly in situations where domain-specific data is scarce.
From the RadAI FDA Approvals Database
Manteia Technologies Co., Ltd.
AccuContour 4.0
AccuContour 4.0 is a software product designed to assist clinicians in radiation therapy by processing radiological images. It helps accurately identify and delineate target areas for treatment, improving precision and patient outcomes in radiation therapy planning.
Devicor Medical Products, Inc.
HydroMARK Plus Breast Biopsy Site Marker (Dragonfly Shape); HydroMARK Plus Breast Biopsy Site Marker (Hummingbird Shape)
The HydroMARK Plus Breast Biopsy Site Marker is a small implantable marker placed at the site of a breast biopsy. It helps clinicians and radiologists accurately identify the biopsy location on imaging, aiding in subsequent diagnosis and treatment planning.
Siemens Healthcare GmbH
AI-Rad Companion Brain MR
AI-Rad Companion Brain MR by Siemens Healthcare is an AI-powered software designed to assist clinicians by automatically analyzing brain MRI scans. It helps improve the efficiency and accuracy of brain imaging interpretation, supporting diagnosis and treatment planning.
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