A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast‐Enhanced MRI Radiomics

• Published in: Journal of magnetic resonance imaging Vol. 58; no. 6; pp. 1930 - 1941
• Main Authors: Cao, Xinshan, Yang, Haoran, Luo, Xin, Zou, Linxuan, Zhang, Qiang, Li, Qilin, Zhang, Juntao, Li, Xiangfeng, Shi, Yan, Jin, Chenwang
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2023; Wiley Subscription Services, Inc
• Subjects: Clustering; Cox nomogram; DCE‐MRI; Field strength; Hepatocellular carcinoma; Liver cancer; Magnetic resonance imaging; Medical imaging; Medical prognosis; Metastases; Nomograms; Parenchyma; preoperative survival; Radiomics; Reliability analysis; Risk assessment; Statistical analysis; Statistical tests; Survival; Tumors; radiomics; Cox nomogram; DCE-MRI; preoperative survival
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.28725

Background The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. Purpose To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast‐enhanced MRI (DCE‐MRI), along with clinical findings. Study Type Retrospective. Subjects 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. Field Strength/Sequence Fat saturated T2‐weighted, Fat saturated T1‐weighted, and DCE‐MRI performed at 1.5 T and 3.0 T. Assessment Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical–radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24‐month survival for HCC. Statistical Tests The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan–Meier analysis. The discrimination performance of each model was quantified by the C‐index. A P value <0.05 was considered statistically significant. Results The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C‐index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. Conclusion The combined radiomic model provides superior ability to discern the possibility of recurrence‐free survival in HCC over the total radiomic and the clinical–radiological models. Evidence Level 4. Technical Efficacy Stage 2.