Machine‐Based Learning Hierarchical Cluster Analysis: Sex‐Based Differences in Prognosis Following Resection of Hepatocellular Carcinoma

Abstract

Background Patients with hepatocellular carcinoma (HCC) may have a heterogeneous presentation, as well as different long-term outcomes following surgical resection. We sought to use machine learning to cluster patients into different prognostic groups based on preoperative characteristics. Methods Patients who underwent curative-intent liver resection for HCC between 2000 and 2020 were identified from a large international multi-institutional database. A hierarchical cluster analysis was performed based on preoperative factors to characterize patterns of presentation and define disease-free survival (DFS). Results Among 966 with HCC, 3 distinct clusters were identified: Cluster 1 (n = 160, 16.5%), Cluster 2 (n = 537, 55.6%) and Cluster 3 (n = 269, 27.8%). Cluster 1 (n = 160, 16.5%) consisted of female patients (n = 160, 100%), low inflammation-based scores, intermediate tumor burden score (TBS) (median: 4.71) and high alpha-fetoprotein (AFP) levels (median 41.3 ng/mL); Cluster 2 consisted of male individuals (n = 537, 100%), mainly with a history of HBV infection (n = 429, 79.9%), low inflammation-based scores, intermediate AFP levels (median 26.0 ng/mL) and lower TBS (median 4.49); Cluster 3 was comprised of older patients (median age 68 years) predominantly male (n = 248, 92.2%) who had low incidence of HBV/HCV infection (7.1% and 8.2%, respectively), intermediate AFP levels (median 16.8 ng/mL), high inflammation-based scores and high TBS (median 6.58). Median DFS worsened incrementally among the three different clusters with Cluster 3 having the lowest DFS (Cluster 1: median not reached; Cluster 2: 34 months, 95% CI 23.0-48.0, Cluster 3: 19 months, 95% CI 15.0-29.0, p \ 0.05). Conclusion Cluster analysis classified HCC patients into three distinct prognostic groups. Cluster assignment predicted DFS following resection of HCC with the female cluster having the most favorable prognosis following HCC resection.Vivian Resende and Diamantis I. Tsilimigras have contributed equally as co-first authors.

Application of artificial intelligence to hepatobiliary cancer clinical outcomes research

“…Different AI approaches to healthcare data AI can involve various statistical approaches based on different techniques and methodologies. Machine learning (ML) and natural language processing (NLP) are generally among the most used methodologies [19,20] . ML utilizes algorithms and statistical models to refine the performance of designated tasks through experiential learning, while not utilizing explicit factor-based programming.…”

“…NLP has given rise to applications such as chatbots, language translation services, and voiceactivated assistants [28][29][30] . NLP enables the handling of vast "unstructured" text-based data, and can enhance the ability to make clinical diagnoses and risk stratification of patients [20,31] .…”