Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, 605425Chongqing University Cancer Hospital, Chongqing, China.
Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221119748. doi: 10.1177/15330338221119748.
To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets ( <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased ( < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules.
评估基于低剂量计算机断层扫描 (LDCT) 的放射组学模型在诊断肺磨玻璃结节良恶性中的临床价值。回顾性分析了 2018 年 1 月至 2021 年 3 月期间接受 LDCT 扫描并识别出肺磨玻璃结节的 274 例患者。所有患者均有完整的临床和病理资料。采用随机抽样法，按照 7:3 的比例将病例随机分为训练集 191 例和验证集 83 例。基于预测源，我们在训练集中建立了临床、放射组学和联合预测模型。为训练集和验证集生成受试者工作特征 (ROC) 曲线，根据曲线下面积 (AUC) 比较不同模型对良性和恶性结节的预测能力，并选择预测能力最佳的模型。绘制校准曲线以检验模型在验证集中的拟合优度。在 274 例患者（84 例男性和 190 例女性）中，156 例为恶性，118 例为良性结节。单因素分析显示，两组数据中良性结节与肺腺癌的结节位置存在统计学差异（均<.001 和.021）。在训练集中，当结节直径>8 mm 时，结节恶性的概率增加（<.001）。结果表明，联合模型的预测能力高于其他两种模型。联合模型可以区分训练集中的良性和恶性肺结节（AUC：0.711；95%CI：0.634-0.787；ACC：0.696；敏感性：0.617；特异性：0.816；PPV：0.835；NPV：0.585）。此外，该模型还可以预测验证集中的良性和恶性结节（AUC：0.695；95%CI：0.574-0.816；ACC：9.747；敏感性：0.694；特异性：0.824；PPV：0.850；NPV：0.651）。校准曲线的 值为 0.775，表明在验证集中，联合模型预测值与实际观测值之间无差异，拟合效果良好。结合临床信息和放射组学参数的预测模型具有良好的区分肺磨玻璃结节良恶性的能力。
