Published online Nov 21, 2023. doi: 10.3748/wjg.v29.i43.5804
Peer-review started: August 26, 2023
First decision: September 18, 2023
Revised: October 7, 2023
Accepted: November 3, 2023
Article in press: November 3, 2023
Published online: November 21, 2023
Surgical resection is still the main treatment for hepatocellular carcinoma (HCC). HCC recurrence is the main factor affecting patients' survival rate after surgery. Developing pre-operative non-invasive predictive methods will be highly significant in identifying patients at high risk of postoperative recurrence and precise management of those patients by closely monitoring and individualized treatment on time.
To develop a new risk prediction model for the early postoperative recurrence of HCC and enhance the feasibility and applicability of the constructed model.
This study aimed to identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.
The demographic and clinical data of 371 HCC patients were collected and analyzed, and the key feature variables were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.
Following machine learning analysis, eight key feature variables were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 [95% confidence interval (95%CI): 0.982-1.000], 0.734 (95%CI: 0.601-0.867), and 0.706 (95%CI: 0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.
The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.
A multicenter study with large samples should be conducted in the future, and comparing our model with other prediction models is needed to further verify its reliability.