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Huang W, Pan Y, Wang H, Jiang L, Liu Y, Wang S, Dai H, Ye R, Yan C, Li Y. Delta-radiomics Analysis Based on Multi-phase Contrast-enhanced MRI to Predict Early Recurrence in Hepatocellular Carcinoma After Percutaneous Thermal Ablation. Acad Radiol 2024:S1076-6332(24)00361-1. [PMID: 38902111 DOI: 10.1016/j.acra.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/22/2024]
Abstract
RATIONALE AND OBJECTIVES It is critical to predict early recurrence (ER) after percutaneous thermal ablation (PTA) for hepatocellular carcinoma (HCC). We aimed to develop and validate a delta-radiomics nomogram based on multi-phase contrast-enhanced magnetic resonance imaging (MRI) to preoperatively predict ER of HCC after PTA. MATERIALS AND METHODS We retrospectively enrolled 164 patients with HCC and divided them into training, temporal validation, and other-scanner validation cohorts (n = 110, 29, and 25, respectively). The volumes of interest of the intratumoral and/or peritumoral regions were delineated on preoperative multi-phase MR images. Original radiomics features were extracted from each phase, and delta-radiomics features were calculated. Logistic regression was used to train the corresponding radiomics models. The clinical and radiological characteristics were evaluated and combined to establish a clinical-radiological model. A fusion model comprising the best radiomics scores and clinical-radiological risk factors was constructed and presented as a nomogram. The performance of each model was evaluated and recurrence-free survival (RFS) was assessed. RESULTS Child-Pugh grade B, high-risk tumor location, and an incomplete/absent tumor capsule were independent predictors of ER. The optimal radiomics model comprised 12 delta-radiomics features with areas under the curve (AUCs) of 0.834, 0.795, and 0.769 in the training, temporal validation, and other-scanner validation cohorts, respectively. The nomogram showed the best predictive performance with AUCs as 0.893, 0.854, and 0.827 in the three datasets. There was a statistically significant difference in RFS between the risk groups calculated using the delta-radiomics model and nomogram. CONCLUSIONS The nomogram combined with the delta-radiomic score and clinical-radiological risk factors could non-invasively predict ER of HCC after PTA.
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Affiliation(s)
- Wanrong Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Yifan Pan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Huifang Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Lu Jiang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Yamei Liu
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Shunli Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Hanting Dai
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Key Laboratory of Radiation Biology of Fujian higher education institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China.
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Shaheen NH, Hardie AD. "Approach to Hepatic Tumor Ablation: Choosing the Right Tool for the Job". Acad Radiol 2024; 31:1302-1303. [PMID: 38472022 DOI: 10.1016/j.acra.2024.02.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 02/21/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Affiliation(s)
- Nicholas H Shaheen
- Medical University of South Carolina, Department of Radiology and Radiological Science, 169 Ashley Avenue, Charleston, SC 29425 (N.H.S., A.D.H.)
| | - Andrew D Hardie
- Medical University of South Carolina, Department of Radiology and Radiological Science, 169 Ashley Avenue, Charleston, SC 29425 (N.H.S., A.D.H.).
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