Datasheet1_Development and validation of radiomic signature for predicting overall survival in advanced stage cervical cancer.docx

BackgroundThe role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance.PurposeThe purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features.Materials and MethodsPretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and final step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC) were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation.ResultsThe average prediction accuracy was found to be 0.65 (95% CI: 0.60–0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively.ConclusionOur study shows the promising predictive performance of robust radiomic signature to predict 5-year overall survival in cervical cancer patients.

背景：人工智能与放射组学在癌症预测模型开发中的作用日益凸显。子宫颈癌是全球女性发病率第四高的癌症，占所有癌症类型的6.5%。子宫颈癌患者的治疗效果存在差异，因此对疾病结果的个体化预测至关重要。目的：本研究旨在开发并验证一种基于稳健的CT放射组学和临床特征的数字签名，以预测子宫颈癌患者的5年总生存率。材料与方法：本研究采用了68名在本院接受化疗放疗治疗的患者的前治疗临床特征和CT放射组学特征。放射组学特征通过自主研发的Python脚本和pyradiomic包提取。临床特征通过递归特征消除技术进行选择。放射组学特征选择则通过多步骤过程进行，即步骤1：根据前期研究仅选择稳健的放射组学特征；步骤2：执行层次聚类以消除特征冗余；最终步骤3：进行递归特征消除，以选择最佳特征用于预测模型开发。使用四种机器学习算法（逻辑回归（LR）、随机森林（RF）、支持向量机（SVC）和梯度提升分类器（GBC）），利用临床、放射组学和结合特征开发了24个模型（每种算法六种模型）。模型通过内部验证中的预测分数进行比较。结果：分别使用四种预测算法开发的临床、放射组学和结合模型，平均预测准确率分别为0.65（95% CI：0.60–0.70）、0.72（95% CI：0.63–0.81）和0.77（95% CI：0.72–0.82）。在三个数据集上分别开发的LR、RF、SVC和GBC模型，平均预测准确率分别为0.69（95% CI：0.62–0.76）、0.79（95% CI：0.72–0.86）、0.71（95% CI：0.62–0.80）和0.72（95% CI：0.66–0.78）。结论：本研究表明，基于稳健放射组学特征的预测性能在预测子宫颈癌患者的5年总生存率方面具有广阔的应用前景。