Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China.
Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China.
Sci Rep. 2024 Sep 30;14(1):22659. doi: 10.1038/s41598-024-73331-w.
The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient's images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.
广泛期小细胞肺癌的预后通常较差。本研究构建了一种基于治疗前 CT 放射组学和临床特征的综合模型，以预测广泛期小细胞肺癌患者接受化疗联合免疫治疗后的总生存期。
回顾性收集了 2019 年 12 月至 2021 年 12 月我院收治的 111 例广泛期小细胞肺癌患者的临床资料。最终，有 93 例患者被纳入研究，通过 PACS 系统获得治疗前的 CT 图像。所有患者均随机分为训练集（n=66）和验证集（n=27）。将图像导入 ITK-SNAP 以勾勒出感兴趣的区域，然后使用 Python 软件提取放射组学特征。从每位患者的图像中提取了 1781 个放射组学特征。通过 MRMR 和 LASSO 方法对特征维度进行降维，筛选出具有最大预测价值的放射组学特征。计算放射组学特征的权重系数，并对特征参数和权重系数进行线性组合，计算 Radscore。采用单因素 cox 回归分析筛选出与预后相关的放射学和临床特征，并采用多因素 cox 回归分析建立广泛期小细胞肺癌的预后预测模型。单因素 cox 回归分析筛选出转移程度作为显著的临床预后因素。通过 LASSO-COX 回归分析选择了 7 个具有统计学意义的放射组学特征，并根据放射组学特征的系数计算 Radscore。通过结合 Radscore 与转移病灶数量构建了生存预测模型。根据患者的生存时间将研究人群分为生存期不足 11 个月和生存期大于 11 个月的两组。训练集和验证集的 C 指数分别为 0.722（se=0.044）和 0.68（se=0.074）。组合模型的 Log-rank 检验结果如下：训练集：p<0.0001，验证集：p=0.00042。本研究构建了一种基于放射组学和临床特征的联合模型，可预测接受化疗联合免疫治疗的广泛期小细胞肺癌患者的 OS，有助于指导临床治疗策略。
