Feng Bao, Chen Xiangmeng, Chen Yehang, Yu Tianyou, Duan Xiaobei, Liu Kunfeng, Li Kunwei, Liu Zaiyi, Lin Huan, Li Sheng, Chen Xiaodong, Ke Yuting, Li Zhi, Cui Enming, Long Wansheng, Liu Xueguo
Department of Radiology, Jiangmen Central Hospital, Jiangmen 529000, China.
School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China.
Cancers (Basel). 2023 Jan 31;15(3):892. doi: 10.3390/cancers15030892.
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).
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.
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.
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.
本研究旨在寻找跨域迁移学习中合适的源域数据，以提取稳健的图像特征。然后，构建一个模型，用于在孤立性肺实性结节（SPSN）中术前区分肺肉芽肿性结节（LGN）和肺腺癌（LAC）。
回顾性收集来自五个中心的841例SPSN患者的数据。首先，采用自适应跨域迁移学习在不同源域数据下构建迁移学习特征（TLS）并进行对比分析。在跨域迁移学习中，使用Wasserstein距离评估源域和目标域数据之间的相似性。其次，构建一个结合表现最佳的TLS、临床因素和主观CT表现的跨域迁移学习放射组学模型（TLRM）。最后，通过多中心验证队列对该模型的性能进行验证。
相对于其他源域数据，以肺全切片图像作为源域数据的TLS（TLS-LW）在所有验证队列中表现最佳（AUC范围：0.8228 - 0.8984）。同时，TLS-LW的Wasserstein距离为1.7108，为最小值。最后，使用TLS-LW、年龄、毛刺征和分叶状形态构建TLRM。在所有验证队列中，AUC范围为0.9074 - 0.9442。决策曲线分析和综合鉴别改善表明，与其他模型相比，TLRM具有更好的性能。
TLRM可协助医生在术前区分SPSN中的LGN和LAC。此外，与其他图像相比，跨域迁移学习在以肺全切片图像作为源域数据时能够提取稳健的图像特征，且效果更佳。
