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Article: Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery
Title | Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery |
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Authors | |
Keywords | Artificial intelligence Computed tomography Deep learning Hepatic surgery Hepatocellular carcinoma |
Issue Date | 2-Dec-2024 |
Publisher | Wiley |
Citation | Hepatology, 2024 How to Cite? |
Abstract | Background and Aims:HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances Approach and Results:Recurr-NET, a multimodal multiphasic residual-network random survival forest deep-learning model incorporating preoperative CT and clinical parameters, was developed to predict HCC recurrence. Preoperative triphasic CT scans were retrieved from patients with resected histology-confirmed HCC from 4 centers in Hong Kong (internal cohort). The internal cohort was randomly divided in an 8:2 ratio into training and internal validation. External testing was performed in an independent cohort from Taiwan. Among 1231 patients (age 62.4y, 83.1% male, 86.8% viral hepatitis, and median follow-up 65.1mo), cumulative HCC recurrence rates at years 2 and 5 were 41.8% and 56.4%, respectively. Recurr-NET achieved excellent accuracy in predicting recurrence from years 1 to 5 (internal cohort AUROC 0.770–0.857; external AUROC 0.758–0.798), significantly outperforming MVI (internal AUROC 0.518–0.590; external AUROC 0.557–0.615) and multiple clinical risk scores (ERASL-PRE, ERASL-POST, DFT, and Shim scores) (internal AUROC 0.523–0.587, external AUROC: 0.524–0.620), respectively (all p < 0.001). Recurr-NET was superior to MVI in stratifying recurrence risks at year 2 (internal: 72.5% vs. 50.0% in MVI; external: 65.3% vs. 46.6% in MVI) and year 5 (internal: 86.4% vs. 62.5% in MVI; external: 81.4% vs. 63.8% in MVI) (all p < 0.001). Recurr-NET was also superior to MVI in stratifying liver-related and all-cause mortality (all p < 0.001). The performance of Recurr-NET remained robust in subgroup analyses. Conclusions:Recurr-NET accurately predicted HCC recurrence, outperforming MVI and clinical prediction scores, highlighting its potential in preoperative prognostication. |
Persistent Identifier | http://hdl.handle.net/10722/353927 |
ISSN | 2023 Impact Factor: 12.9 2023 SCImago Journal Rankings: 5.011 |
DC Field | Value | Language |
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dc.contributor.author | Hui, Rex Wan-Hin | - |
dc.contributor.author | Chiu, Keith Wan-Hang | - |
dc.contributor.author | Lee, I-Cheng | - |
dc.contributor.author | Wang, Chenlu | - |
dc.contributor.author | Cheng, Ho-Ming | - |
dc.contributor.author | Lu, Jianliang | - |
dc.contributor.author | Mao, Xianhua | - |
dc.contributor.author | Yu, Sarah | - |
dc.contributor.author | Lam, Lok-Ka | - |
dc.contributor.author | Mak, Lung-Yi | - |
dc.contributor.author | Cheung, Tan-To | - |
dc.contributor.author | Chia, Nam-Hung | - |
dc.contributor.author | Cheung, Chin-Cheung | - |
dc.contributor.author | Kan, Wai-Kuen | - |
dc.contributor.author | Wong, Tiffany Cho-Lam | - |
dc.contributor.author | Chan, Albert Chi-Yan | - |
dc.contributor.author | Huang, Yi-Hsiang | - |
dc.contributor.author | Yuen, Man-Fung | - |
dc.contributor.author | Yu, Philip Leung-Ho | - |
dc.contributor.author | Seto, Wai-Kay | - |
dc.date.accessioned | 2025-01-29T00:35:16Z | - |
dc.date.available | 2025-01-29T00:35:16Z | - |
dc.date.issued | 2024-12-02 | - |
dc.identifier.citation | Hepatology, 2024 | - |
dc.identifier.issn | 0270-9139 | - |
dc.identifier.uri | http://hdl.handle.net/10722/353927 | - |
dc.description.abstract | <h3>Background and Aims: </h3><p>HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances</p><h3>Approach and Results: </h3><p>Recurr-NET, a multimodal multiphasic residual-network random survival forest deep-learning model incorporating preoperative CT and clinical parameters, was developed to predict HCC recurrence. Preoperative triphasic CT scans were retrieved from patients with resected histology-confirmed HCC from 4 centers in Hong Kong (internal cohort). The internal cohort was randomly divided in an 8:2 ratio into training and internal validation. External testing was performed in an independent cohort from Taiwan.</p><p>Among 1231 patients (age 62.4y, 83.1% male, 86.8% viral hepatitis, and median follow-up 65.1mo), cumulative HCC recurrence rates at years 2 and 5 were 41.8% and 56.4%, respectively. Recurr-NET achieved excellent accuracy in predicting recurrence from years 1 to 5 (internal cohort AUROC 0.770–0.857; external AUROC 0.758–0.798), significantly outperforming MVI (internal AUROC 0.518–0.590; external AUROC 0.557–0.615) and multiple clinical risk scores (ERASL-PRE, ERASL-POST, DFT, and Shim scores) (internal AUROC 0.523–0.587, external AUROC: 0.524–0.620), respectively (all p < 0.001). Recurr-NET was superior to MVI in stratifying recurrence risks at year 2 (internal: 72.5% vs. 50.0% in MVI; external: 65.3% vs. 46.6% in MVI) and year 5 (internal: 86.4% vs. 62.5% in MVI; external: 81.4% vs. 63.8% in MVI) (all p < 0.001). Recurr-NET was also superior to MVI in stratifying liver-related and all-cause mortality (all p < 0.001). The performance of Recurr-NET remained robust in subgroup analyses.</p><h3>Conclusions: </h3><p>Recurr-NET accurately predicted HCC recurrence, outperforming MVI and clinical prediction scores, highlighting its potential in preoperative prognostication.</p> | - |
dc.language | eng | - |
dc.publisher | Wiley | - |
dc.relation.ispartof | Hepatology | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Artificial intelligence | - |
dc.subject | Computed tomography | - |
dc.subject | Deep learning | - |
dc.subject | Hepatic surgery | - |
dc.subject | Hepatocellular carcinoma | - |
dc.title | Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery | - |
dc.type | Article | - |
dc.identifier.doi | 10.1097/HEP.0000000000001180 | - |
dc.identifier.scopus | eid_2-s2.0-85211384385 | - |
dc.identifier.eissn | 1527-3350 | - |
dc.identifier.issnl | 0270-9139 | - |