Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes. We included five significant clinical risk factors (age, preoperative serum tumour markers AFP and B-HCG, histotype and BMI) to build the clinical model. After segmenting 273 retroperitoneal lymph nodes, we then combined the clinical risk factors and lymph node radiomics features to establish combined predictive models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, the decision curve analysis (DCA) was used to evaluate the clinical usefulness. The Random Forest combined clinical lymph node radiomics model with the highest AUC of 0.95 (±0.03 SD; 95% CI) was considered the candidate model with decision curve analysis, demonstrating its usefulness for preoperative prediction in the clinical setting. Our study has identified reliable and predictive machine learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment.
准确预测睾丸癌患者的淋巴结转移（LNM）对治疗决策和预后评估具有重要意义。本研究旨在开发并验证用于睾丸癌患者术前个体化预测LNM的临床影像组学模型。我们纳入了91例经临床病理证实为早期睾丸癌的患者，其病变局限于睾丸。我们选取了五个重要的临床风险因素（年龄、术前血清肿瘤标志物AFP和B-HCG、组织学类型及BMI）构建临床模型。在对273个腹膜后淋巴结进行分割后，我们结合临床风险因素与淋巴结影像组学特征，采用随机森林（RF）、轻量梯度提升机（LGBM）、支持向量机分类器（SVC）和K近邻（KNN）算法建立了联合预测模型。通过受试者工作特征（ROC）曲线下面积（AUC）评估模型性能，并采用决策曲线分析（DCA）评估临床实用性。随机森林联合临床淋巴结影像组学模型具有最高的AUC值（0.95±0.03 SD；95% CI），经决策曲线分析验证为候选模型，证明其在临床术前预测中具有实用价值。本研究确定了可用于早期睾丸癌淋巴结转移预测的可靠机器学习技术。基于整合临床风险因素的影像组学，识别最有效的机器学习预测分析方法，可拓展影像组学在精准肿瘤学和癌症治疗中的适用性。
肿瘤(癌症)患者之家