Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data

Abstract

Aim To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning. Methods Data of patients who underwent treatment for HCC from 2008 to 2015 was provided by Korean Liver Cancer Study Group and Korea Central Cancer Registry. A total of 10,742 patients with HCC were divided into two groups, with Group I (2920 patients) confirmed on biopsy and Group II (5562 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. The data were modeled according to features of patient clinical characteristics. Features effective in predicting survival rate were analyzed retrospectively. Various machine learning methods were used. Results Target was overall survival time, which divided into approximately 60 months (= /< 60 m, > 60 m). Target distribution in Group I (total 514 samples) was 28.8%: (148 samples) less than 60 months, 71.2% (366 samples) greater than 60 months, and in Group II (total 757 samples) was 66.6% (504 samples) less than 60 months, 33.4% (253 samples) greater than 60 months. Using NG Boost method, its accuracy was 83%, precision 84%, sensitivity 95%, and F1 score 89% for more than 60 months survival time in Group I with surgical resection. Moreover, its accuracy was 79%, precision 82%, sensitivity 87%, and F1 score 84% for less than 60 months survival time in Group II with TACE. The feature importance with gain criterion indicated that pathology, portal vein invasion, surgery, metastasis, and needle biopsy features could be explained as important factors for prediction in case of biopsy (Group I). Conclusion By developing a predictive model using machine learning algorithms to predict prognosis of HCC patients, it is possible to project optimized treatment by case according to liver function and tumor status.

Augmenting care in hepatocellular carcinoma with artificial intelligence

“…To categorize liver lesions identified on CT, Yasaka et al designed a model to differentiate liver lesions on CT into five categories: HCC, other malignant tumors, indeterminate masses, hemangiomas, and cysts, with a median AUC of 0.92 [46] . Most recently, the LiSNet AI tool was developed for staging of HCC aggressiveness using CT images, where Sun et al showed results comparable to subspecialist analysis [21] . A human-AI partnered diagnosis was also attempted, combining experience-based binary diagnosis and LiSNet, resulting in the best predictive ability for certain parameters such as microvascular invasion (MVI) with AUC 0.705 [21] .…”

“…Most recently, the LiSNet AI tool was developed for staging of HCC aggressiveness using CT images, where Sun et al showed results comparable to subspecialist analysis [21] . A human-AI partnered diagnosis was also attempted, combining experience-based binary diagnosis and LiSNet, resulting in the best predictive ability for certain parameters such as microvascular invasion (MVI) with AUC 0.705 [21] .…”

“…In recent years, advancements in artificial intelligence (AI) capabilities have shown great potential to redefine the way we navigate clinical care for HCC patients. AI has the capacity to improve risk prediction in chronic hepatitis patients [13], accelerate the diagnostic process with early identification of HCC [14][15][16] , increase accuracy in the classification of liver lesions and HCC subtypes [17][18][19][20] , tumor staging [21] , and survival prediction [22,23] . Decisions regarding candidate selection and optimal treatment methods may also utilize AI in the prediction of treatment response, progression-free and overall survival [24,25] and risk of HCC recurrence [26] .…”

Comparison of Clinical Manifestations and Related Factors of Hepatocellular Carcinoma with Chronic Hepatitis B

“…At present, survival rates for HCC are still poor. 18 , 19 Therefore, significant efforts in early diagnosis and better treatment are certainly needed.…”