Application of the Preoperative Assistant System Based on Machine Learning in Hepatocellular Carcinoma Resection.

Shouyun Lv,Changxiong Wu,Kaiqiong Wang,Zhiwei Yu,Shizong Li,Dongwei Gong,Xin Qiao

doi:10.1155/2021/4757668

Abstract

To conduct better research in hepatocellular carcinoma resection, this paper used 3D machine learning and logistic regression algorithm to study the preoperative assistance of patients undergoing hepatectomy. In this study, the logistic regression model was analyzed to find the influencing factors for the survival and recurrence of patients. The clinical data of 50 HCC patients who underwent extensive hepatectomy (≥4 segments of the liver) admitted to our hospital from June 2020 to December 2020 were selected to calculate the liver volume, simulated surgical resection volume, residual liver volume, surgical margin, etc. The results showed that the simulated liver volume of 50 patients was 845.2 + 285.5 mL, and the actual liver volume of 50 patients was 826.3 ± 268.1 mL, and there was no significant difference between the two groups (t = 0.425; P > 0.05). Compared with the logistic regression model, the machine learning method has a better prediction effect, but the logistic regression model has better interpretability. The analysis of the relationship between the liver tumour and hepatic vessels in practical problems has specific clinical application value for accurately evaluating the volume of liver resection and surgical margin.

Highlights

Hepatectomy is one of the main methods of surgical treatment of liver cancer, such as hepatitis B, liver cirrhosis, and the application of surgical resection is limited
Considering patients with liver tumours less than 2 cm in diameter, there is no significant difference in radiofrequency ablation and surgical resection efficacy
Virtual hepatectomy was performed in the experimental group, and the volume of resected liver and residual liver were automatically calculated. e volume of resected specimens was measured after surgery and compared with the actual resected liver volume before surgery

Summary

Introduction

Hepatectomy is one of the main methods of surgical treatment of liver cancer, such as hepatitis B, liver cirrhosis, and the application of surgical resection is limited. Since the beginning of the twenty-first century, under the guidance of the concept of precision medicine, supported by functional liver anatomy, pathological anatomy, new imaging technologies, new methods of liver function evaluation, and new technologies of liver parenchyma separation, precision liver resection has become the forefront of liver surgery [3]. It is developing new strategies for early prediction of cancer treatment based on machine learning methods. Random forest, support vector machine, C5.0 decision tree, neural network, bagging algorithm, and AdaBoost algorithm were used to construct the three-year tumour-free survival time (disease-free survival, DFS). e classification and extraction rules of clinical medical data of liver tumours are extracted

Methods

Results

Conclusion