增强基于CT的基于CT的肿瘤内和周围放射线学列图预测了浸润性肺腺癌的高级模式
Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma
影响因子:3.90000
分区:医学2区 / 核医学2区
发表日期:2025 Jan
作者:
Jiaheng Xu, Ling Liu, Yang Ji, Tiancai Yan, Zhenzhou Shi, Hong Pan, Shuting Wang, Kang Yu, Chunhui Qin, Tong Zhang
摘要
提取肿瘤内和脑周围放射素学特征以及临床因素结合了临床因素，以预测肺（IAC）的浸润性腺癌的高级模式（微毛细血管和固体）。对463例病理证实的患者进行了回顾性研究。患者以7：3的比率随机分为训练队列（n = 324）和测试队列（n = 139）。从四个区域中的每个区域中的每个区域中提取了总共2154个基于CT的放射素特征：含有3 mm，6 mm和9 mm和9 mm和9 mm的肿瘤区域的肿瘤肿瘤体积（GTV）和总肿瘤肿瘤体积（GPTV3，GPTV6，GPTV9，GPTV9）。与GTV（0.840），GPTV6（0.843）（0.843）和GPTV9（0.734）模型相比，GPTV3放射组模型在测试组中显示出更好的预测性能，在测试组中显示出更好的预测性能。在临床模型中，将肿瘤密度和杂音符号的存在确定为独立的预测因子。将这些独立预测因子与GPTV3-RADSCORE相结合的命名图被证明在临床上有用。GPTV3放射线学模型在预测IAC的高级模式（HGP）中优于GTV，GPTV6和GPTV9放射线模型。此外，基于GPTV3放射线学特征和临床独立预测因子的列图可以进一步提高预测效率。
Abstract
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.