An Expert Committee-Driven Adaptive Framework for Multicenter Pulmonary Nodule Classification Using AI-Derived Quantitative CT Features.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing University, Nanjing, China.
- Department of Radiology, The People's Hospital of Bozhou, Bozhou, China.
- Department of Magnetic Resonance Imaging, Heze Hospital of Shandong Provincial Hospital (Heze Municipal Hospital), Heze, China.
- Department of Radiology, Mengcheng County No.1, People's Hospital , Mengcheng, China.
- Department of Radiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing University, Nanjing, China. [email protected].
- Department of Radiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing University, Nanjing, China. [email protected].
- Department of Radiology, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing University, Nanjing, China. [email protected].
Abstract
Accurate preoperative classification of pulmonary nodules (PNs) is important for clinical decision-making and avoiding overtreatment. However, marked heterogeneity among pathological subtypes limits the performance of conventional single-model classification approaches. This study aimed to develop and validate an expert committee-driven adaptive framework based on AI-derived quantitative CT features for differentiating four clinically relevant PN subtypes: atypical adenomatous hyperplasia and adenocarcinoma in situ (AAH + AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IAC), and inflammatory nodules (IN). This retrospective study included 491 pathologically confirmed PNs from a single center as the internal cohort. Quantitative CT features were automatically extracted using an AI-assisted platform. A heterogeneous pool of candidate classifiers was constructed, and an expert committee-driven arbitration mechanism was implemented to enable subtype-specific optimal model selection based on validation performance. Diagnostic performance was primarily assessed using the area under the receiver operating characteristic curve (AUC). Two independent multicenter cohorts were used for external validation. In internal validation, the proposed framework achieved AUCs of 0.918 for AAH + AIS, 0.860 for MIA, 0.913 for IAC, and 0.913 for IN. External validation demonstrated consistent performance across centers, with improved sensitivity for heterogeneous subtypes, particularly MIA and IN, compared with conventional single-model approaches. An expert committee-driven adaptive framework enables subtype-specific model selection for pulmonary nodule classification and demonstrates stable diagnostic performance across multicenter cohorts. This approach supports the potential clinical utility of AI-derived quantitative CT features for noninvasive preoperative assessment of pulmonary nodules.