Zhang Maochun, Zhang Qing, Wang Xueying, Peng Xiaoli, Chen Jiao, Yang Hanfeng
Affiliated Hospital, Jinan University, Guangzhou, 510630, China.
Department of Health Management Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
Sci Rep. 2025 May 29;15(1):18862. doi: 10.1038/s41598-025-03170-w.
To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinical staging of cervical cancer. General clinical and ultrasound data of 227 patients with cervical cancer who received transvaginal ultrasonography were retrospectively analyzed. The region of interest (ROI) radiomics profiles of the original image and derived image were retrieved and profile screening was performed. The chosen profiles were employed in radiomics model and Radscore formula construction. Prediction models were developed utilizing several ML algorithms by Python based on an integrated dataset of clinical features and ultrasound radiomics. Model performances were evaluated via AUC. Plot calibration curves and clinical decision curves were used to assess model efficacy. The model developed by support vector machine (SVM) emerged as the superior model. Integrating clinical characteristics with ultrasound radiomics, it showed notable performance metrics in both the training and validation datasets. Specifically, in the training set, the model obtained an AUC of 0.88 (95% Confidence Interval (CI): 0.83-0.93), alongside a 0.84 accuracy, 0.68 sensitivity, and 0.91 specificity. When validated, the model maintained an AUC of 0.77 (95% CI: 0.63-0.88), with 0.77 accuracy, 0.62 sensitivity, and 0.83 specificity. The calibration curve aligned closely with the perfect calibration line. Additionally, based on the clinical decision curve analysis, the model offers clinical utility over wide-ranging threshold possibilities. The clinical- and radiomics-based SVM model provides a noninvasive tool for predicting cervical cancer stage, integrating ultrasound radiomics and key clinical factors (age, abortion history) to improve risk stratification. This approach could guide personalized treatment (surgery vs. chemoradiation) and optimize staging accuracy, particularly in resource-limited settings where advanced imaging is scarce.
为研究将机器学习（ML）与临床特征及超声影像组学相结合构建的模型在宫颈癌临床分期中的预测作用。回顾性分析了227例接受经阴道超声检查的宫颈癌患者的一般临床和超声数据。提取原始图像和衍生图像的感兴趣区域（ROI）影像组学特征并进行特征筛选。将所选特征用于影像组学模型和Radscore公式构建。基于临床特征和超声影像组学的综合数据集，利用Python中的几种ML算法开发预测模型。通过AUC评估模型性能。绘制校准曲线和临床决策曲线以评估模型效能。支持向量机（SVM）开发的模型表现最优。将临床特征与超声影像组学相结合，其在训练集和验证集中均表现出显著的性能指标。具体而言，在训练集中，该模型的AUC为0.88（95%置信区间（CI）：0.83 - 0.93），准确率为0.84，灵敏度为0.68，特异性为0.91。在验证时，该模型的AUC保持在0.77（95% CI：0.63 - 0.88），准确率为0.77，灵敏度为0.62，特异性为0.83。校准曲线与理想校准线紧密对齐。此外，基于临床决策曲线分析，该模型在广泛的阈值可能性范围内具有临床实用性。基于临床和影像组学的SVM模型提供了一种用于预测宫颈癌分期的非侵入性工具，整合了超声影像组学和关键临床因素（年龄、流产史）以改善风险分层。这种方法可指导个性化治疗（手术与放化疗）并优化分期准确性，特别是在资源有限且缺乏先进成像技术的环境中。
