College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China.
Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China.
Cancer Control. 2022 Jan-Dec;29:10732748221089408. doi: 10.1177/10732748221089408.
Pure ground-glass nodules (pGGNs) have been considered inert tumors due to their biological behavior; however, their prognosis is not completely consistent because of differences in internal pathological component. The aim of this study was to explore whether radiomics can be used to identify the invasiveness of pGGNs.
The retrospective study received the relevant ethical approval. After postoperative pathological confirmation, sixty-five patients with lung adenocarcinoma pGGNs (≤30 mm) were enrolled in this study from January 2015 to October 2018. All the cases were randomly divided into training and test groups in a 7:3 ratio. In total, 385 radiomics features were obtained from HRCT images, and then least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the training group to obtain optimal features to distinguish the invasion degree of lesions. The diagnostic efficiency of the radiomics model was estimated by the area under the curve (AUC) of the receiver operating curve (ROC), and verified by the test group.
The optimal features ("GLCMEntropy_angle135_offset1" and "Sphericity") were selected after applying the LASSO regression to develop the proposed radiomics model. This prediction model exhibited good differentiation between pre-invasive and invasive lesions. The AUC for the test group was 0.824 (95%CI: 0.599-1.000), indicating that the radiomics model has some prediction ability.
The HRCT radiomics features can discriminate pre-invasive from invasive lung adenocarcinoma pGGNs. This non-invasive method can provide more information for surgeons before operation, and can also predict the prognosis of patients to some extent.
由于纯磨玻璃结节（pGGNs）的生物学行为，它们被认为是惰性肿瘤；然而，由于内部病理成分的差异，其预后并不完全一致。本研究旨在探讨放射组学是否可用于识别 pGGNs 的侵袭性。
本回顾性研究获得了相关伦理批准。术后病理证实后，2015 年 1 月至 2018 年 10 月期间，共纳入 65 例肺腺癌 pGGNs（≤30mm）患者。所有病例均以 7：3 的比例随机分为训练组和测试组。共从 HRCT 图像中获取 385 个放射组学特征，然后在训练组中应用最小绝对收缩和选择算子（LASSO）逻辑回归获得最佳特征以区分病变的侵袭程度。通过受试者工作特征曲线（ROC）的曲线下面积（AUC）评估放射组学模型的诊断效率，并通过测试组进行验证。
应用 LASSO 回归后，选择出最优特征（“GLCMEntropy_angle135_offset1”和“Sphericity”），建立了放射组学预测模型。该预测模型能够很好地区分术前和侵袭性病变。测试组的 AUC 为 0.824（95%CI：0.599-1.000），表明放射组学模型具有一定的预测能力。
HRCT 放射组学特征可区分肺腺癌 pGGNs 的术前和侵袭性病变。这种非侵入性方法可为术前外科医生提供更多信息，在一定程度上还可以预测患者的预后。
