Peritumoral and intratumoral radiomics for predicting visceral pleural invasion in lung adenocarcinoma based on preoperative computed tomography (CT)

作者：Y-Q Zuo, Dongmei Gao, J-J. Cui, Yixia Yin, Zhao Gao, Po‐Hao Feng, Zhimin Geng, Xuan Yang · 发表于：Clinical Radiology · 年份：2024 · DOI：10.1016/j.crad.2024.10.010 · 被引用次数：13 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Advanced Radiotherapy Techniques

AIM: To evaluate the prediction of peritumoral and intratumoral radiomics for visceral pleural invasion (VPI) in lung adenocarcinoma cancer (LAC) based on preoperative computed tomography (CT) radiomics. MATERIALS AND METHODS: In total, 350 patients with LAC confirmed by surgery pathology were enrolled in The Second Hospital of Hebei Medical University, including 281 VPI negative patients and 69 VPI positive patients, were divided into the training cohort (n = 280) and validation cohort (n=70) at random with a ratio of 8:2. We extracted the radiomics features from the 3 region of interest (ROI), including gross tumor volume (GTV), the gross peritumoral tumor volume (GPTV) and the gross volume of the tumor rim (included the outer 4 mm of the tumor and 4mm of the tumor adjacent lung tissue on either side of the tumor contour boundary, GTR).The maximal redundancy minimal relevance (mMRM) algorithm and the least absolute shrinkage and selection operator (LASSO) was performed to reduce feature dimensionality and the radiomics score (Rad score) of the best radiomics model was combined with CT morphological characteristics with statistical significance in the univariable analysis to construct the combined model. The performance of the models was evaluated based on receiver operating characteristics (ROC) curve, calibration, and clinical usefulness. DeLong's test was used to assess differences in area under curve (AUC) between different models. RESULTS: There were no statistically si...