预测早期癌症睾丸淋巴结转移的CT放射组学和临床特征模型

Onco Pub Date : 2023-04-10 DOI:10.3390/onco3020006
C. Lisson, Sabitha Manoj, Daniel Wolf, Jasper Schrader, S. Schmidt, Meinrad Beer, Michael Goetz, F. Zengerling, C. Lisson
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引用次数: 2

摘要

准确预测早期睾丸生殖细胞肿瘤(TGCT)的腹膜后淋巴结转移(LNM),有可能显著降低这组年轻患者治疗过度或治疗不足以及治疗相关的发病率,这是重要的生存要求。我们研究了计算机断层扫描(CT)放射组学模型在早期TGCT中整合临床预测因素对LNM个性化预测的作用。本回顾性研究纳入了91例经手术证实的睾丸生殖细胞肿瘤和增强CT患者。使用专用的放射组学软件对273个腹膜后淋巴结进行分割并提取特征。在特征选择后,开发了基于放射组学的机器学习模型来预测LN转移。该程序的稳健性通过10倍交叉验证进行控制。使用多变量逻辑回归模型,我们开发了三个预测模型:仅放射组学模型、仅临床模型和放射组学-临床组合模型。使用受试者工作特性曲线下面积(AUC)评估模型的性能。最后,进行决策曲线分析,以评估预测模型的临床实用性。仅用于预测淋巴结转移的放射组学模型比仅用于临床的模型具有更大的辨别力,在我们的研究队列中,AUC为0.87(±0.04;95%置信区间)对0.75(±0.08;95%可信区间)。整合临床风险因素和选定放射组学特征的组合模型优于仅临床和仅放射组学预测模型,并显示出良好的辨别力,曲线下面积为0.89(±0.03;95%置信区间)。决策曲线分析证明了我们提出的联合模型的临床实用性。所提出的基于CT的放射组学-临床组合模型为睾丸生殖细胞肿瘤中的LN转移预测提供了一种令人兴奋的非侵入性工具。需要多中心验证才能为其临床应用提供高质量的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer
Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCTs) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualised prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: a radiomics-only model, a clinical-only model, and a combined radiomics–clinical model. The models’ performances were evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.87 (±0.04; 95% CI) vs. 0.75 (±0.08; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models, and showed good discrimination with an area under the curve of 0.89 (±0.03; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics–clinical model represents an exciting non-invasive tool for individualised LN metastasis prediction in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application.
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