Unsupervised Machine Learning of MRI Radiomics Features Identifies Two Distinct Subgroups with Different Liver Function Reserve and Risks of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

Qiang Wang; Changfeng Li; Geng Chen; Kai Feng; Zhiyu Chen; Feng Xia; Ping Cai; Leida Zhang; Ernesto Sparrelid; Torkel B Brismar; Kuansheng Ma

doi:10.3390/cancers15123197

Unsupervised Machine Learning of MRI Radiomics Features Identifies Two Distinct Subgroups with Different Liver Function Reserve and Risks of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

Cancers (Basel). 2023 Jun 15;15(12):3197. doi: 10.3390/cancers15123197.

Authors

Qiang Wang^{1

2}, Changfeng Li³, Geng Chen⁴, Kai Feng³, Zhiyu Chen³, Feng Xia³, Ping Cai⁵, Leida Zhang³, Ernesto Sparrelid⁶, Torkel B Brismar^{1

2}, Kuansheng Ma³

Affiliations

¹ Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 141 86 Stockholm, Sweden.
² Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 141 86 Stockholm, Sweden.
³ Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China.
⁴ Department of Hepatobiliary Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China.
⁵ Department of Radiology, Southwest Hospital, Army Medical University, Chongqing 400038, China.
⁶ Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 141 86 Stockholm, Sweden.

Abstract

Objective: To identify subgroups of patients with hepatocellular carcinoma (HCC) with different liver function reserves using an unsupervised machine-learning approach on the radiomics features from preoperative gadoxetic-acid-enhanced MRIs and to evaluate their association with the risk of post-hepatectomy liver failure (PHLF).

Methods: Clinical data from 276 consecutive HCC patients who underwent liver resections between January 2017 and March 2019 were retrospectively collected. Radiomics features were extracted from the non-tumorous liver tissue at the gadoxetic-acid-enhanced hepatobiliary phase MRI. The reproducible and non-redundant features were selected for consensus clustering analysis to detect distinct subgroups. After that, clinical variables were compared between the identified subgroups to evaluate the clustering efficacy. The liver function reserve of the subgroups was compared and the correlations between the subgroups and PHLF, postoperative complications, and length of hospital stay were evaluated.

Results: A total of 107 radiomics features were extracted and 37 were selected for unsupervised clustering analysis, which identified two distinct subgroups (138 patients in each subgroup). Compared with subgroup 1, subgroup 2 had significantly more patients with older age, albumin-bilirubin grades 2 and 3, a higher indocyanine green retention rate, and a lower indocyanine green plasma disappearance rate (all p < 0.05). Subgroup 2 was also associated with a higher risk of PHLF, postoperative complications, and longer hospital stays (>18 days) than that of subgroup 1, with an odds ratio of 2.83 (95% CI: 1.58-5.23), 2.41(95% CI: 1.15-5.35), and 2.14 (95% CI: 1.32-3.47), respectively. The odds ratio of our method was similar to the albumin-bilirubin grade for postoperative complications and length of hospital stay (2.41 vs. 2.29 and 2.14 vs. 2.16, respectively), but was inferior for PHLF (2.83 vs. 4.55).

Conclusions: Based on the radiomics features of gadoxetic-acid-enhanced MRI, unsupervised clustering analysis identified two distinct subgroups with different liver function reserves and risks of PHLF in HCC patients. Future studies are required to validate our findings.

Keywords: hepatocellular carcinoma; liver function; machine learning; magnetic resonance imaging; radiomics.

Abstract

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