Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
School of Nuclear Science and Technology, University of South China, Hengyang, China.
Sci Rep. 2023 Nov 8;13(1):19409. doi: 10.1038/s41598-023-46621-y.
This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann-Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models-the clinical model, the radiomics model, and the combined clinic and radiomics model-were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.
本研究旨在评估基于磁共振成像（MRI）的 Delta 放射组学特征从 Ax LAVA+C 系列中提取出来，以识别接受新辅助放化疗后手术的宫颈癌患者中中高危因素的可行性。总共 157 名患者被分为两组：无中高危因素组（n=75）和有一个中危因素的组（阴性组）。有任何高危因素或一个以上中危因素的组（阳性组，n=82）。使用 Ax-LAVA+C MRI 序列提取放射组学特征。数据按 8:2 的比例分为训练集（n=126）和测试集（n=31）。使用 Mann-Whitney U 检验和最小绝对收缩和选择算子（LASSO）检验选择训练集数据特征。然后分析最佳放射组学特征，以建立术前预测放射组学模型，预测宫颈癌中高危因素。本研究利用随机森林算法建立了三种模型——临床模型、放射组学模型和联合临床放射组学模型。利用受试者工作特征（ROC）曲线、决策曲线分析（DCA）、准确性、敏感性和特异性评估了每个模型的预测效果和临床获益。本研究建立了三种模型，以预测接受新辅助放疗后接受手术的患者术后病理相关的中高危变量。在训练集和测试集中，临床模型、放射组学模型和联合临床放射组学模型评估的 AUC 值分别为 0.76 和 0.70、0.88 和 0.86、0.91 和 0.89。使用机器学习算法分析 Delta Ax LAVA+C MRI 放射组学特征有助于预测接受新辅助治疗的宫颈癌患者的中高危因素。
