Machine learning model-based approach using cellular proliferation marker expression for preoperative clinical...

The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma (HCC) using a machine learning model-based approach is a scientific approach. This study looked into the possibilities of using a Ki-67 (a marker for cell proliferation) expression-based machine learning model to help doctors make decisions about treatment options for patients with HCC before surgery. The study used reconstructed tomography images of 164 patients with confirmed HCC from the intratumoral and peritumoral regions. The features were chosen using various statistical methods, including least absolute shrinkage and selection operator regression. Also, a nomogram was made using Radscore and clinical risk factors. It was tested for its ability to predict receiver operating characteristic curves and calibration curves, and its clinical benefits were found using decision curve analysis. The calibration curve demonstrated excellent consistency between predicted and actual probability, and the decision curve confirmed its clinical benefit. The proposed model is helpful for treating patients with HCC because the predicted and actual probabilities are very close to each other, as shown by the decision curve analysis. Further prospective studies are required, incorporating a multicenter and large sample size design, additional relevant exclusion criteria, information on tumors (size, number, and grade), and cancer stage to strengthen the clinical benefit in patients with HCC.