文章：

基于放射组学的计算机断层扫描尿路成像方法预测上尿路尿路上皮癌的生存与复发

Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma

原文发布日期：10 September 2024

DOI: 10.3390/cancers16183119

类型: Article

开放获取: 是

英文摘要：

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell’s concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan–Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan–Meier analysis showed significant differences in the survival (p< 0.0001) and recurrence (p< 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors.

摘要翻译： 

上尿路尿路上皮癌（UTUC）是一种罕见且侵袭性强的恶性肿瘤，预后较差。准确预测UTUC患者的生存和复发对于有效的风险分层和指导治疗决策至关重要。结合计算机断层扫描尿路造影（CTU）的影像组学特征与临床病理数据构建的模型，可作为预测UTUC生存和复发的一种方法。本研究分析了106例接受根治性肾输尿管切除术的UTUC患者的术前CTU影像及临床数据。从分割后的肿瘤区域提取影像组学特征，并采用最小绝对收缩与选择算子（LASSO）方法筛选最相关的特征。通过结合影像组学特征与临床因素构建多变量Cox模型，用于预测生存和复发。采用Harrell一致性指数（C-index）评估模型性能，并通过Kaplan-Meier分析评估生存分布。多变量Cox模型识别出显著的结果预测因子。联合模型展现出更优的预测准确性（C指数：0.73）和更高的复发预测能力（C指数：0.84）。Kaplan-Meier分析显示，联合数据集在生存概率（p<0.0001）和复发概率（p<0.002）方面存在显著差异。基于CTU的影像组学模型能有效预测UTUC患者的生存和复发，且通过结合影像组学特征与临床因素进一步提升了预后评估效能。

原文链接：

Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma