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Integrating baseline MR imaging biomarkers into BCLC and CLIP improves overall survival prediction of patients with hepatocellular carcinoma (HCC)

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单位: [1]Johns Hopkins Univ, Sch Med, Russell H Morgan Dept Radiol & Radiol Sci, 600 N Wolfe St,MRI 143, Baltimore, MD 21287 USA [2]Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Radiol, Wuhan, Peoples R China
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关键词: Carcinoma hepatocellular Magnetic resonance imaging Survival analysis Neoplasm staging Chemoembolization therapeutic

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Objectives We aimed to evaluate the independent predictive role of baseline imaging biomarkers for overall survival (OS) and transplant-free survival (TFS) in patients with HCC and assess the incremental value of these biomarkers to current staging systems. Methods In this retrospective IRB approved study, the clinical, laboratory, and imaging parameters of 304 HCC patients were collected. Cox regression model was utilized to identify the potential predictors of survival. Recursive partitioning test was utilized to identify the optimal ADC cutoff for stratifying patients' OS. Patients were stratified based on Barcelona Clinic Liver Cancer (BCLC) and Cancer of the Liver Italian Program (CLIP). Binary ADC value (above vs. below the cutoff) and tumor margin (well- vs. ill-defined) were integrated into BCLC and CLIP. OS and TFS was compared for patients based on standard criteria with and without imaging biomarkers. Results At baseline, patients with low tumor ADC and well-defined tumor margin (favorable imaging biomarkers) had longer survival, as compared to those with high ADC and ill-defined tumor margin (unfavorable imaging biomarkers) (median OS of 43 months vs. 7 months, respectively) (p < 0.001). Tumor ADC and tumor margin remained strong independent predictors of survival after adjustment for demographics, BCLC and CLIP staging, and tumor burden. Incorporating ADC and tumor margin improved performance of OS prediction by 9% in BCLC group and 6% in CLIP group. Conclusion Incorporating ADC and tumor margin to current staging systems for HCC significantly improve prediction of OS and TFS of these criteria.

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出版当年[2020]版:
大类 | 2 区 医学
小类 | 2 区 核医学
最新[2025]版:
大类 | 2 区 医学
小类 | 2 区 核医学
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出版当年[2019]版:
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

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第一作者单位: [1]Johns Hopkins Univ, Sch Med, Russell H Morgan Dept Radiol & Radiol Sci, 600 N Wolfe St,MRI 143, Baltimore, MD 21287 USA
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