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

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. Non-small cell lung cancer (NSCLC) requires accurate assessment of histologic subtype, TNM stage, and prognosis for clinical risk stratification. Here, the authors present a multimodal, multi-task deep learning scoring system (MM-DLS) that integrates pretreatment PET/CT imaging and clinical variables to non-invasively predict histologic subtype, TNM stage, and survival risk in patients with NSCLC.