Seid Miad Zandavi, Christy Kim, Thomas Goodwin, Cynthuja Thilakanathan, Maryam Bostanara, Anna Camille Akon, Daniel Al Mouiee, Sasha Barisic, Ammar Majeed, William Kemp, Francis Chu, Marty Smith, Kate Collins, Vincent Wai-Sun Wong, Grace Lai-Hung Wong, Jason Behary, Stuart K. Roberts, Kelvin K. C. Ng, Fatemeh Vafaee, Amany Zekry
{"title":"AI-powered prediction of HCC recurrence after surgical resection: Personalised intervention opportunities using patient-specific risk factors","authors":"Seid Miad Zandavi, Christy Kim, Thomas Goodwin, Cynthuja Thilakanathan, Maryam Bostanara, Anna Camille Akon, Daniel Al Mouiee, Sasha Barisic, Ammar Majeed, William Kemp, Francis Chu, Marty Smith, Kate Collins, Vincent Wai-Sun Wong, Grace Lai-Hung Wong, Jason Behary, Stuart K. Roberts, Kelvin K. C. Ng, Fatemeh Vafaee, Amany Zekry","doi":"10.1111/liv.16050","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Hepatocellular carcinoma (HCC) recurrence following surgical resection remains a significant clinical challenge, necessitating reliable predictive models to guide personalised interventions. In this study, we sought to harness the power of artificial intelligence (AI) to develop a robust predictive model for HCC recurrence using comprehensive clinical datasets.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Leveraging data from 958 patients across multiple centres in Australia and Hong Kong, we employed a multilayer perceptron (MLP) as the optimal classifier for model generation.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Through rigorous internal cross-validation, including a cohort from the Chinese University of Hong Kong (CUHK), our AI model successfully identified specific pre-surgical risk factors associated with HCC recurrence. These factors encompassed hepatic synthetic function, liver disease aetiology, ethnicity and modifiable metabolic risk factors, collectively contributing to the predictive <i>synergy</i> of our model. Notably, our model exhibited high accuracy during cross-validation (.857 ± .023) and testing on the CUHK cohort (.835), with a notable degree of confidence in predicting HCC recurrence within accurately classified patient cohorts. To facilitate clinical application, we developed an online AI digital tool capable of real-time prediction of HCC recurrence risk, demonstrating acceptable accuracy at the individual patient level.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Our findings underscore the potential of AI-driven predictive models in facilitating personalised risk stratification and targeted interventions to mitigate HCC recurrence by identifying modifiable risk factors unique to each patient. This model aims to aid clinicians in devising strategies to disrupt the underlying carcinogenic network driving recurrence.</p>\n </section>\n </div>","PeriodicalId":18101,"journal":{"name":"Liver International","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/liv.16050","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Liver International","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/liv.16050","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Hepatocellular carcinoma (HCC) recurrence following surgical resection remains a significant clinical challenge, necessitating reliable predictive models to guide personalised interventions. In this study, we sought to harness the power of artificial intelligence (AI) to develop a robust predictive model for HCC recurrence using comprehensive clinical datasets.
Methods
Leveraging data from 958 patients across multiple centres in Australia and Hong Kong, we employed a multilayer perceptron (MLP) as the optimal classifier for model generation.
Results
Through rigorous internal cross-validation, including a cohort from the Chinese University of Hong Kong (CUHK), our AI model successfully identified specific pre-surgical risk factors associated with HCC recurrence. These factors encompassed hepatic synthetic function, liver disease aetiology, ethnicity and modifiable metabolic risk factors, collectively contributing to the predictive synergy of our model. Notably, our model exhibited high accuracy during cross-validation (.857 ± .023) and testing on the CUHK cohort (.835), with a notable degree of confidence in predicting HCC recurrence within accurately classified patient cohorts. To facilitate clinical application, we developed an online AI digital tool capable of real-time prediction of HCC recurrence risk, demonstrating acceptable accuracy at the individual patient level.
Conclusion
Our findings underscore the potential of AI-driven predictive models in facilitating personalised risk stratification and targeted interventions to mitigate HCC recurrence by identifying modifiable risk factors unique to each patient. This model aims to aid clinicians in devising strategies to disrupt the underlying carcinogenic network driving recurrence.
期刊介绍:
Liver International promotes all aspects of the science of hepatology from basic research to applied clinical studies. Providing an international forum for the publication of high-quality original research in hepatology, it is an essential resource for everyone working on normal and abnormal structure and function in the liver and its constituent cells, including clinicians and basic scientists involved in the multi-disciplinary field of hepatology. The journal welcomes articles from all fields of hepatology, which may be published as original articles, brief definitive reports, reviews, mini-reviews, images in hepatology and letters to the Editor.