Integrating multimodal data for precise subtyping and prognostication in patients with pulmonary mucinous adenocarcinoma.

Background: Pulmonary mucinous adenocarcinoma (PMA) represents a rare lung adenocarcinoma subtype, characterized by a lack of comprehensive pathological classification and prognostic factors. In this study, we propose a multimodal machine learning (ML) framework that integrates multiple data modalities to enhance PMA subtype classification and prognostic prediction. Methods: This retrospective study enrolled 175 primary PMA cases with details of demographic, histopathological, computed tomography (CT) imaging, and epithelial growth factor receptor ( Results: Pure mucinous adenocarcinoma presented a higher prevalence of smaller radiographic or pathological tumor size, lower lobe localization, lower rates of lymph node metastasis and spread through air spaces (STAS), early pathological stage, carcinoembryonic antigen (CEA) ≤5 ng/mL, spiculated margin, Ki-67 low expression and lower frequency of Conclusions: Pulmonary pure mucinous adenocarcinoma exhibited lower malignancy compared to the mixed mucinous and mucin-secreting type. The nomogram model developed and validated in this study exhibited promising efficacy in predicting histological subtype preoperatively and survival in patients with PMA, offering guidance for clinicians in diagnosis and prognosis prediction.