Integrating multimodal data for precise subtyping and prognostication in patients with pulmonary mucinous adenocarcinoma.
Authors
Affiliations (4)
Affiliations (4)
- Cell and Molecular Biology Laboratory, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China.
- Lung Cancer Research Centre, Zhoushan Hospital, Zhoushan, China.
- Department of Cardio-Thoracic Surgery, Zhoushan Hospital, Zhoushan, China.
- Department of Respiratory and Critical Care, Zhoushan Hospital, Zhoushan, China.
Abstract
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. This retrospective study enrolled 175 primary PMA cases with details of demographic, histopathological, computed tomography (CT) imaging, and epithelial growth factor receptor (<i>EGFR</i>) mutation data. Least absolute shrinkage and selection operator (LASSO) regularized logistic regression model was utilized for histological classification, and Cox proportional hazards model was employed for survival prediction with internal validation. 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 <i>EGFR</i> exon 19 deletion (P=0.002, 0.003, <0.001, <0.001, 0.001, <0.001, 0.002, 0.03, 0.02, 0.001, respectively) compared to mix mucinous and mucin-secreting adenocarcinoma. The ML-derived nomogram achieved discriminative ability, with an area under the curve (AUC) of 0.792 in the training set and 0.687 in the validation set, and exhibited satisfactory calibration for the preoperative diagnosis of pure mucinous adenocarcinoma. Multivariate Cox modeling identified higher CEA levels, and <i>EGFR</i> exon 19 deletion as independent prognostic factors in PMA (P=0.04, 0.049, respectively). Moreover, Kaplan-Meier curves revealed significantly different outcomes between low- and high-risk groups stratified by the nomogram score (P<0.001). 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.