Machine Learning–Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation

Masaya Sato,Satoshi Kodera,Yutaka Yatomi,Makoto Moriyama,Kota Ninomiya,Hayato Nakagawa,Tomoharu Yamada,Tsuyoshi Fukumoto,Kenichiro Enooku,Shuichiro Shiina,Tatsuya Minami,Koji Uchino,Mizuki Nishibatake Kinoshita,Ryosuke Tateishi,Takuma Nakatsuka,Ryo Nakagomi,Kazuhiko Koike

doi:10.1016/j.gastha.2021.09.003

Masaya Sato, Satoshi Kodera + Show 15 more

Open Access

https://doi.org/10.1016/j.gastha.2021.09.003

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Abstract

<h3>Background and Aims</h3> Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. <h3>Methods</h3> We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models—including the deep learning–based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. <h3>Results</h3> The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (<i>P</i> < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. <h3>Conclusion</h3> We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.

Highlights

Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related deaths worldwide, and its incidence is expected to rise further.[1]
This study aimed to develop an machine learning (ML) model for hazard identification to predict the risk of hepatocellular carcinoma (HCC) recurrence after Radiofrequency ablation (RFA) treatment for individual patients
We developed a conventional Cox proportional hazard (CPH) model and 6 ML models: a Deep learning (DL)-based model (DeepSurv), neural multitask logistic regression model (MTLR), random forest (RF), gradient boosting decision tree (GBDT), elastic net penalized regression, and support vector machine (SVM)

Summary

Introduction

Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related deaths worldwide, and its incidence is expected to rise further.[1]. As many as 70% of patients have HCC recurrence within 5 years after RFA treatment; this includes distant recurrence due to intrahepatic metastasis or de novo primary cancer development and local tumor progression.[3] The HCC recurrence risk varied widely among patients and was shown to be affected by tumor factors—including tumor size, tumor number, or tumor markers—as well as underlying chronic liver diseases—such as fibrosis or inflammation. These contribute to the development of HCC.[3,6] current guidelines recommend regular follow-ups—using imaging and serum tumor marker studies of patients who received curative treatment for HCC7–12—surveillance intervals after. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence

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