Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases.
prospective_cohort · Level II
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- Record sourced from PubMed, PMID 42143150.
- Also identified by DOI 10.1038/s41746-026-02759-3.
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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.