LAUSR

Simple Summary This retrospective study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features and build a model to preoperatively distinguish LGN from LAC in SPSNs. The experiment showed that, compared with other source domains (such as ImageNet and LIDC), the transfer learning signature based on lung whole slide images as the source domain could extract more robust features (Wasserstein distance: 1.7108). Finally, a cross-domain transfer learning radiomics model combining transfer learning signatures based on lung whole slide images as the source domain, clinical factors and subjective CT findings was constructed. According to the validation cohort results of five centres (AUC range: 0.9074–0.9442), the cross-domain transfer learning radiomics model that combined multimodal data could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Abstract Purpose: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). Methods: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. Results: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228–0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074–0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. Conclusions: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.