Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Authors

Xinyue Liu, Fang Dai, Jiawei Dai, Huoqiang Wang, Qiang Li, Yaokai Wen, Haoyue Guo, Lishu Zhao, Hao Wang, Kandi Xu, Maotao Weng, Siqiong Yao, Hui Lu, Yayi He

Published in

Nature communications. Jun 15, 2026. Epub Jun 15, 2026.

Abstract

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-small cell lung cancer (NSCLC), but remain challenging because of tumor heterogeneity, limited biomarker performance and diagnostic uncertainty. Here we show that a multimodal, multi-task deep learning scoring system (MM-DLS), integrating pretreatment PET/CT images with clinical variables, enables non-invasive prediction of NSCLC subtype, stage and survival risk. We develop and validate MM-DLS in 4,164 patients from multiple centres. In the external validation cohort, MM-DLS achieves area under the receiver operating characteristic curve values of 0.86 for histologic subtype classification and 0.86, 0.86, and 0.88 for stages I-II, III, and IV, respectively. The model also shows consistently strong discrimination for 1-, 3-, and 5-year survival across treatment regimens. These results indicate that MM-DLS provides an integrated framework for subtype prediction, staging, and prognostic stratification in NSCLC.

PMID:
42297835
Bibliographic data and abstract were imported from PubMed on 16 Jun 2026.

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