提取瘤内和瘤周放射组学特征并结合临床因素建立列线图来预测侵袭性肺腺癌 (IAC) 的高级模式（微乳头状和实性）。对 463 例经病理证实的 IAC 患者进行了回顾性研究。患者按 7:3 的比例随机分为训练队列 (n = 324) 和测试队列 (n = 139)。从四个区域中提取了总共 2154 个基于 CT 的放射组学特征：肿瘤总体积 (GTV) 和瘤周肿瘤总体积（GPTV3、GPTV6、GPTV9），其中肿瘤周围区域大小为 3 mm、6 mm 和 9 mm。毫米。基于最佳放射组学模型和临床独立预测因子构建了放射组学列线图。与 GTV (0.840)、GPTV6 (0.843) 和 GPTV9 (0.734) 模型相比，GPTV3 放射组学模型在测试组中表现出更好的预测性能，测试组的AUC值为0.889。在临床模型中，肿瘤密度和毛刺征的存在被确定为独立的预测因素。将这些独立预测因子与 GPTV3-Radscore 相结合的列线图被证明在临床上是有用的。在预测 IAC 的高级模式 (HGP) 方面，GPTV3 放射组学模型优于 GTV、GPTV6 和 GPTV9 放射组学模型。此外，基于 GPTV3 放射组学特征和临床独立预测因子的列线图可以进一步提高预测效率。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

Extraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC).A retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors.The GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843), and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful.The GPTV3 radiomics model was superior to the GTV, GPTV6, and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.