ArticleNPJ digital medicine2026
Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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19 authors.
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Abstract
Gliomas and brain metastases (BMs) on MRI pose significant diagnostic challenges for radiologists. This study aims to develop a multi-task model and a computer-aided diagnosis (CAD) system for the detection and diagnosis of gliomas and BMs. This study enrolled 3909 participants from seven centers, and developed a brain tumor segmentation and classification network (BTSC-Net) and BTSC-CAD with visualization of tumor masks. For detection, BTSC-Net achieved a Dice coefficient of 0.888 and 0.872 on the internal and external test sets, respectively. For diagnosis, BTSC-Net achieved AUCs of 0.941 and 0.933 on the internal and external test sets, respectively. With BTSC-CAD assistance, junior radiologists achieved mean AUC improvements of 4.8% (P < 0.05) for detection and 17.3% (P < 0.001) for diagnosis, along with an average reduction of 64.75 s in reading time. BTSC-CAD significantly improved radiologists' diagnostic accuracy and efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.