多序列 MRI 影像组学模型术前预测透明细胞肾细胞癌 WHO/ISUP 分级：一项多中心研究。

Multi-sequence MRI-based radiomics model to preoperatively predict the WHO/ISUP grade of clear Cell Renal Cell Carcinoma: a two-center study.

机构信息

Department of Radiology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, Shanxi Province, 030001, P.R. China.

Department of College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi Province, 030001, P.R. China.

出版信息

BMC Cancer. 2024 Sep 27;24(1):1176. doi: 10.1186/s12885-024-12930-2.

DOI:10.1186/s12885-024-12930-2

PMID:39333970

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11438199/

Abstract

OBJECTIVES

To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC).

METHODS

This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant.

RESULTS

Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04-0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849-0.856) across different folds.

CONCLUSION

The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC.

摘要

目的

基于两个中心的多序列 MRI 开发影像组学模型，用于术前预测肾透明细胞癌（ccRCC）的 WHO/ISUP 分级。

方法

本回顾性研究纳入了来自两个中心的 334 例 ccRCC 患者。通过单因素和多因素分析确定有显著意义的临床因素。MRI 序列包括动态对比增强 MRI、轴位脂肪抑制 T2 加权成像、弥散加权成像和同相位/反相位图像。使用特征选择方法和逻辑回归（LR）构建临床和影像组学模型，并使用 Rad-score 和有显著意义的临床因素开发联合模型。此外，使用 7 种分类器构建联合模型，并使用不同的 LR 进行折叠构建联合模型，以评估其性能。使用受试者工作特征（ROC）曲线、曲线下面积（AUC）和决策曲线分析（DCA）评估模型。Delong 检验比较 ROC 性能，p<0.050 为差异有统计学意义。

结果

多因素分析确定肿瘤内血管是高级别 ccRCC 的独立预测因素。在外部验证集中，影像组学模型（AUC=0.834）的表现优于临床模型（AUC=0.762），联合模型的 AUC 最高（0.855），显著优于临床模型（p=0.003）。DCA 显示联合模型在 0.04-0.54 风险阈值范围内的净获益高于临床模型。此外，使用 LR 构建的联合模型的分类器优先级高于其他分类器。此外，LR 对联合模型的 10 折交叉验证显示在不同的折之间 AUC 值（0.849-0.856）具有一致性。