Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma
Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to predict IC response and OS in LANPC. Pretreatment MRI and WSIs from 404 patients with LANPC were retrospectively collected to construct a multi-task model (MoEMIL) for the simultaneous prediction of…
A model combining pretreatment MRI and pathology slides separated good from poor induction chemotherapy responders and outperformed TNM staging on both response and survival prediction in a test set.
The cohort had no progressive disease cases, so the poor responder group consisted only of stable disease patients, and the data were retrospective; the improvement over the pathomics model for IC response was only borderline.
Evidence
- Peer-reviewedPLOS Digital Health2026-10-05
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Truvace Impact Record TRV-2026-1865, v1: “Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma.” Truvace, 2026-10-18. /record/TRV-2026-1865 (accessed at citation time). sha256 8e5c4187013381bb…
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