Liu Miaozhi, Duan Rui, Xu Zhifeng, Fu Zijie, Li Zhiheng, Pan Aizhen, Lin Yan
Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province 515041, PR China.
Department of Radiology, First People's Hospital of Foshan, Foshan, Guangdong Province 528000, PR China.
Eur J Radiol Open. 2024 Jun 27;13:100584. doi: 10.1016/j.ejro.2024.100584. eCollection 2024 Dec.
To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features.
This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023. Models based on CT radiomics and clinical features were constructed for classification of AAH/AIS and MIA, MIA and IAC, as well as lepidic-predominant adenocarcinoma (LPA) and acinar-predominant adenocarcinoma (APA). Receiver operating characteristic (ROC) curve was used to evaluate the model performance. Finally, the nomograms based on the optimal models were established.
The nomogram based on the combined model (AAH/AIS versus MIA) consisting of lobulation, the GGN-vessel relationship, diameter, CT value, consolidation tumor ratio (CTR) and rad-score performed the best (AUC=0.841), while age, CT value, CTR and rad-score were the significant features for distinguishing MIA from IAC, the nomogram based on these features performed the best (AUC=0.878). There were no significant differences in clinical features between LPA and APA, while the radiomics model based on rad-score showed good performance for distinguishing LPA from APA (AUC=0.926).
The nomograms based on radiomics and clinical features could predict the invasiveness of SSNs accurately. Moreover, radiomics models showed good performance in distinguishing LPA from APA.
基于CT影像组学和临床特征构建预测亚实性结节（SSN）侵袭性和病理亚型的最佳模型。
本研究为一项涉及两个中心的回顾性研究。纳入了2019年1月至2023年2月期间共316例患者的353个经证实为非典型腺瘤样增生（AAH）、原位腺癌（AIS）、微浸润腺癌（MIA）和浸润性腺癌（IAC）的SSN。构建基于CT影像组学和临床特征的模型，用于AAH/AIS与MIA、MIA与IAC以及以鳞屑为主型腺癌（LPA）和以腺泡为主型腺癌（APA）的分类。采用受试者操作特征（ROC）曲线评估模型性能。最后，建立基于最佳模型的列线图。
由分叶、磨玻璃结节与血管关系、直径、CT值、实变肿瘤比率（CTR）和影像组学评分组成的联合模型（AAH/AIS与MIA）构建的列线图表现最佳（AUC = 0.841），而年龄、CT值、CTR和影像组学评分是区分MIA与IAC的显著特征，基于这些特征构建的列线图表现最佳（AUC = 0.878）。LPA和APA的临床特征无显著差异，而基于影像组学评分的影像组学模型在区分LPA与APA方面表现良好（AUC = 0.926）。
基于影像组学和临床特征的列线图能够准确预测SSN的侵袭性。此外，影像组学模型在区分LPA与APA方面表现良好。
