Discrimination of ground‐glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study

Abstract

Abstract Background The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground‐glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. Methods We developed and validated a three‐dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre‐training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end‐to‐end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. Results The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). Conclusions Our 3D deep transfer learning model provides a noninvasive, low‐cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

Keywords

• adenocarcinoma
• deep learning
• lung
• prognosis
• X‐ray computed tomography