AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.
Authors
Affiliations (17)
Affiliations (17)
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang, Gyeonggi-do, Korea.
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea.
- Divisions of Gastroenterology and Hepatology, Department of Internal Medicine, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Korea.
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Korea.
- Seoul HIM Clinic, Seoul, Korea.
- Department of Internal Medicine, ABC Hospital, Hwaseong, Gyeonggi-do, Korea.
- Department of Internal Medicine, College of Medicine, Chung-Ang University, Seoul, Korea.
- Division of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
- Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Korea.
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
- AI Center, MedicalIP. Co. Ltd., Seoul, Korea.
- AI Center, MedicalIP. Co. Ltd., Seoul, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- Department of Anesthesiology, Seoul National University College of Medicine, Seoul, Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea; Inocras Inc., San Diego, CA, USA. Electronic address: [email protected].
Abstract
Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables. An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software. The derivation cohort (n = 5,585) was randomly divided into the training and internal validation sets at a 3:1 ratio. The external validation cohort included 2,883 patients. Six imaging biomarkers (i.e. abdominal visceral fat-total fat volume ratio, total fat-trunk volume ratio, spleen volume, liver volume, liver-spleen Hounsfield unit ratio, and muscle Hounsfield unit) and eight clinical variables were selected as the main variables of our model, PLAN-B-DF. In the internal validation set (median follow-up duration = 7.4 years), PLAN-B-DF demonstrated an excellent predictive performance with a c-index of 0.91 and good calibration function (p = 0.78 by the Hosmer-Lemeshow test). In the external validation cohort (median follow-up duration = 4.6 years), PLAN-B-DF showed a significantly better discrimination function compared to previous models, including PLAN-B, PAGE-B, modified PAGE-B, and CU-HCC (c-index, 0.89 vs. 0.65-0.78; all p <0.001), and maintained a good calibration function (p = 0.42 by the Hosmer-Lemeshow test). When patients were classified into four groups according to the risk probability calculated by PLAN-B-DF, the 10-year cumulative HCC incidence was 0.0%, 0.4%, 16.0%, and 46.2% in the minimal-, low-, intermediate-, and high-risk groups, respectively. This AI prediction model, integrating deep learning-based auto-segmentation of CT images, offers improved performance in predicting HCC risk among patients with CHB compared to previous models. The novel predictive model PLAN-B-DF, employing an automated computed tomography segmentation algorithm, significantly improves predictive accuracy and risk stratification for hepatocellular carcinoma in patients with chronic hepatitis B (CHB). Using a gradient-boosting algorithm and computed tomography metrics, such as visceral fat volume and myosteatosis, PLAN-B-DF outperforms previous models based solely on clinical and demographic data. This model not only shows a higher c-index compared to previous models, but also effectively classifies patients with CHB into different risk groups. This model uses machine learning to analyze the complex relationships among various risk factors contributing to hepatocellular carcinoma occurrence, thereby enabling more personalized surveillance for patients with CHB.