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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
背景：手术切除后肝细胞癌（HCC）的复发仍然是一个重大的临床挑战，需要可靠的预测模型来指导个性化干预。在这项研究中,我们试图利用人工智能(AI)的力量,利用全面的临床数据集开发HCC复发的稳健预测模型.
方法：利用来自澳大利亚和香港多个中心的958名患者的数据，我们采用多层感知器（MLP）作为模型生成的最佳分类器。
结果：通过严格的内部交叉验证，包括香港中文大学(中大)的一群人，我们的AI模型成功识别了与HCC复发相关的特定术前危险因素.这些因素包括肝脏合成功能，肝病病因，种族和可改变的代谢危险因素，共同促进了我们模型的预测协同作用。值得注意的是,我们的模型在交叉验证(.857±.023)和中大队列(.835)测试中表现出很高的准确性，在准确分类的患者队列中预测HCC复发具有显著的置信度。为了便于临床应用，我们开发了一种能够实时预测HCC复发风险的在线AI数字工具，在个体患者水平上证明了可接受的准确性。
结论：我们的发现强调了AI驱动的预测模型在促进个性化风险分层和有针对性的干预措施以通过识别每位患者特有的可改变的风险因素来减轻HCC复发方面的潜力。该模型旨在帮助临床医生制定策略来破坏潜在的致癌网络驱动复发。
方法：利用来自澳大利亚和香港多个中心的958名患者的数据，我们采用多层感知器（MLP）作为模型生成的最佳分类器。
结果：通过严格的内部交叉验证，包括香港中文大学(中大)的一群人，我们的AI模型成功识别了与HCC复发相关的特定术前危险因素.这些因素包括肝脏合成功能，肝病病因，种族和可改变的代谢危险因素，共同促进了我们模型的预测协同作用。值得注意的是,我们的模型在交叉验证(.857±.023)和中大队列(.835)测试中表现出很高的准确性，在准确分类的患者队列中预测HCC复发具有显著的置信度。为了便于临床应用，我们开发了一种能够实时预测HCC复发风险的在线AI数字工具，在个体患者水平上证明了可接受的准确性。
结论：我们的发现强调了AI驱动的预测模型在促进个性化风险分层和有针对性的干预措施以通过识别每位患者特有的可改变的风险因素来减轻HCC复发方面的潜力。该模型旨在帮助临床医生制定策略来破坏潜在的致癌网络驱动复发。
