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Your search keyword '"Kim, Kang Mo"' showing total 14 results
Start Over Author "Kim, Kang Mo" Journal liver international
14 results on '"Kim, Kang Mo"'

7. Clinical outcomes of systemic therapy in patients with unresectable or metastatic combined hepatocellular‐cholangiocarcinoma

Author

Kim, Eo Jin, primary, Yoo, Changhoon, additional, Kang, Hyo Jeong, additional, Kim, Kyu‐Pyo, additional, Ryu, Min‐Hee, additional, Park, Sook Ryun, additional, Lee, Danbi, additional, Choi, Jonggi, additional, Shim, Ju Hyun, additional, Kim, Kang Mo, additional, Lim, Young‐Suk, additional, Lee, Han Chu, additional, and Ryoo, Baek‐Yeol, additional

Published
2021
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14. A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B.

Author

Ha, Yeonjung, Lee, Seungseok, Lim, Jihye, Lee, Kwanjoo, Chon, Young Eun, Lee, Joo Ho, Lee, Kwan Sik, Kim, Kang Mo, Shim, Ju Hyun, Lee, Danbi, Yon, Dong Keon, Lee, Jinseok, and Lee, Han Chu

Subjects
*MACHINE learning, *CHRONIC hepatitis B, *HEPATOCELLULAR carcinoma, *RANDOM forest algorithms, *LOGISTIC regression analysis
Abstract

ABSTRACT Background and Aims Methods Results Conclusions This study aims to develop and validate a machine learning (ML) model predicting hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients after the first 5 years of entecavir (ETV) or tenofovir (TFV) therapy.CHB patients treated with ETV/TFV for > 5 years and not diagnosed with HCC during the first 5 years of therapy were selected from two hospitals. We used 36 variables, including baseline characteristics (age, sex, cirrhosis, and type of antiviral agent) and laboratory values (at baseline, at 5 years, and changes between 5 years) for model development. Five machine learning algorithms were applied to the training dataset and internally validated using a test dataset. External validation was performed.In years 5–15, a total of 279/5908 (4.7%) and 25/562 (4.5%) patients developed HCC in the derivation and external validation cohorts, respectively. In the training dataset (n = 4726), logistic regression showed the highest area under the receiver operating curve (AUC) of 0.803 and a balanced accuracy of 0.735, outperforming other ML algorithms. An ensemble model combining logistic regression and random forest performed best (AUC, 0.811 and balanced accuracy, 0.754). The results from the test dataset (n = 1182) verified the good performance of the ensemble model (AUC, 0.784 and balanced accuracy, 0.712). External validation confirmed the predictive accuracy of our ensemble model (AUC, 0.862 and balanced accuracy, 0.771). A web‐based calculator was developed (http://ai‐wm.khu.ac.kr/HCC/).The proposed ML model excellently predicted HCC risk beyond year 5 of ETV/TFV therapy and, therefore, could facilitate individualised HCC surveillance based on risk stratification. [ABSTRACT FROM AUTHOR]

Published
2024
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