Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China; Department of Radiology, Stanford University, School of Medicine, Stanford, United States.
Eur J Radiol. 2023 Mar;160:110671. doi: 10.1016/j.ejrad.2022.110671. Epub 2022 Dec 26.
To develop CT-based radiomics models that can efficiently distinguish between multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPMs).
This retrospective study included 127 patients with 254 lung tumors pathologically proved as MPLCs or IPMs between May 2009 and January 2020. Radiomics features of lung tumors were extracted from baseline CT scans. Particularly, we incorporated tumor-focused, refined radiomics by calculating relative radiomics differences from paired tumors of individual patients. We applied the L1-norm regularization and analysis of variance to select informative radiomics features for constructing radiomics model (RM) and refined radiomics model (RRM). The performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The two radiomics models were compared with the clinical-CT model (CCM, including clinical and CT semantic features). We incorporated both radiomics features to construct fusion model1 (FM1). We also, build fusion model2 (FM2) by combing both radiomics, clinical and CT semantic features. The performance of the FM1 and FM2 were further compared with that of the RRM.
On the validation set, the RM achieved an AUC of 0.857. The RRM demonstrated improved performance (validation set AUC, 0.870) than the RM, and showed significant differences compared with the CCM (validation set AUC, 0.782). Fusion models further led prediction performance (validation set AUC, FM1:0.885; FM2:0.889). There were no significant differences among the performance of the FM1, the FM2 and the RRM.
The CT-based radiomics models presented good performance on the discrimination between MPLCs and IPMs, demonstrating the potential for early diagnosis and treatment guidance for MPLCs and IPMs.
开发基于 CT 的放射组学模型，以有效区分多原发肺癌（MPLC）和肺内转移（IPM）。
本回顾性研究纳入了 2009 年 5 月至 2020 年 1 月期间经病理证实为 MPLC 或 IPM 的 127 例 254 个肺部肿瘤患者。从基线 CT 扫描中提取肺部肿瘤的放射组学特征。特别地，我们通过计算个体患者配对肿瘤的相对放射组学差异，纳入了以肿瘤为焦点的精细放射组学。我们应用 L1 范数正则化和方差分析来选择信息丰富的放射组学特征，用于构建放射组学模型（RM）和精细放射组学模型（RRM）。采用受试者工作特征曲线下面积（AUC-ROC）评估性能。将这两种放射组学模型与临床 CT 模型（CCM，包括临床和 CT 语义特征）进行比较。我们结合放射组学特征构建融合模型 1（FM1）。我们还通过结合放射组学、临床和 CT 语义特征构建融合模型 2（FM2）。进一步比较了 FM1 和 FM2 的性能与 RRM 的性能。
在验证集上，RM 的 AUC 为 0.857。RRM 的性能得到了改善（验证集 AUC，0.870），优于 RM，与 CCM（验证集 AUC，0.782）相比具有显著差异。融合模型进一步提高了预测性能（验证集 AUC，FM1：0.885；FM2：0.889）。FM1、FM2 和 RRM 的性能之间没有显著差异。
基于 CT 的放射组学模型在区分 MPLC 和 IPM 方面表现出良好的性能，为 MPLC 和 IPM 的早期诊断和治疗指导提供了潜力。
