Clinical‐Radiologic Morphology‐Radiomics Model on Gadobenate Dimeglumine‐Enhanced MRI for Identification of Highly Aggressive Hepatocellular Carcinoma: Temporal Validation and Multiscanner Validation

肝细胞癌 列线图 接收机工作特性 逻辑回归 医学 钆酸 曲线下面积 队列 阶段(地层学) 磁共振成像 核医学 人口 放射科 肿瘤科 内科学 古生物学 环境卫生 生物 钆DTPA
作者
Wanjing Zheng,Xiaodan Chen,Meilian Xiong,Yu Zhang,Yang Song,Dairong Cao
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:60 (6): 2643-2654 被引量:1
标识
DOI:10.1002/jmri.29293
摘要

Background Highly aggressive hepatocellular carcinoma (HCC) is characterized by high tumor recurrence and poor outcomes, but its definition and imaging characteristics have not been clearly described. Purpose To develop and validate a fusion model on gadobenate dimeglumine‐enhanced MRI for identifying highly aggressive HCC. Study Type Retrospective. Population 341 patients (M/F = 294/47) with surgically resected HCC, divided into a training cohort (n = 177), temporal validation cohort ( n = 77), and multiscanner validation cohort ( n = 87). Field Strength/Sequence 3T, dynamic contrast‐enhanced MRI with T1‐weighted volumetric interpolated breath‐hold examination gradient‐echo sequences, especially arterial phase (AP) and hepatobiliary phase (HBP, 80–100 min). Assessment Clinical factors and diagnosis assessment based on radiologic morphology characteristics associated with highly aggressive HCCs were evaluated. The radiomics signatures were extracted from AP and HBP. Multivariable logistic regression was performed to construct clinical‐radiologic morphology (CR) model and clinical‐radiologic morphology‐radiomics (CRR) model. A nomogram based on the optimal model was established. Early recurrence‐free survival (RFS) was evaluated in actual groups and risk groups calculated by the nomogram. Statistical Tests The performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curves analysis, and decision curves. Early RFS was evaluated by using Kaplan–Meier analysis. A P value <0.05 was considered statistically significant. Results The CRR model incorporating corona enhancement, cloud‐like hyperintensity on HBP, and radiomics signatures showed the highest diagnostic performance. The area under the curves (AUCs) of CRR were significantly higher than those of the CR model (AUC = 0.883 vs. 0.815, respectively, for the training cohort), 0.874 vs. 0.769 for temporal validation, and 0.892 vs. 0.792 for multiscanner validation. In both actual and risk groups, highly and low aggressive HCCs showed statistically significant differences in early recurrence. Data Conclusion The clinical‐radiologic morphology‐radiomics model on gadobenate dimeglumine‐enhanced MRI has potential to identify highly aggressive HCCs and non‐invasively obtain prognostic information. Level of Evidence 4 Technical Efficacy Stage 2
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