Jiang Wenting, Qu Tingting, Liu Weiran, Shi Huazheng, Zhang Yali
Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Shanghai Universal Cloud Medical Imaging Diagnostic Center, Shanghai, China.
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241305432. doi: 10.1177/15330338241305432.
To evaluate the diagnostic performance of CT radiomic features extracted from tumor and peritumoral regions in identifying pathological subtypes of T1-stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs). A retrospective analysis was conducted on the data of T1-stage lung adenocarcinoma patients who underwent surgical resection and whose preoperative CT scans revealed pGGNs from June 2020 to June 2023 in our hospital. 3D Slicer was used to extract radiomic features of the intratumoral (VOI entire) and peritumoral regions (VOI +2 mm), and Rad-scores were calculated from the coefficients of features obtained after dimensionality reduction with LASSO regression. A total of 131 patients with T1-stage lung adenocarcinoma presenting as pGGNs were included in this study; of these, 84 were pathologically diagnosed with the lepidic-predominant (LPA) subtype, and 47 were diagnosed with non-LPA. The diagnostic performance of the VOI entire and VOI +2 mm features for the pathological subtype of pGGN was superior to that of conventional features, with the VOI +2 mm features showing the best performance: the area under the curve, sensitivity, specificity, and accuracy in the training set were 0.883, 0.964, 0.667, and 0.761, respectively. Intra- and especially peritumoral-based radiomic features have high diagnostic performance for the pathological subtype of T1-stage lung adenocarcinoma presenting as pGGNs.
评估从肿瘤及瘤周区域提取的CT影像组学特征在鉴别表现为纯磨玻璃结节（pGGN）的T1期肺腺癌病理亚型中的诊断性能。对2020年6月至2023年6月在我院接受手术切除且术前CT扫描显示为pGGN的T1期肺腺癌患者的数据进行回顾性分析。使用3D Slicer提取瘤内（VOI整体）和瘤周区域（VOI +2 mm）的影像组学特征，并通过LASSO回归降维后得到的特征系数计算Rad分数。本研究共纳入131例表现为pGGN的T1期肺腺癌患者；其中，84例经病理诊断为以鳞屑为主型（LPA）亚型，47例诊断为非LPA。VOI整体和VOI +2 mm特征对pGGN病理亚型的诊断性能优于传统特征，VOI +2 mm特征表现最佳：训练集中的曲线下面积、敏感性、特异性和准确性分别为0.883、0.964、0.667和0.761。基于肿瘤内部尤其是瘤周的影像组学特征对表现为pGGN的T1期肺腺癌病理亚型具有较高的诊断性能。
