Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma - Long, Ze; Yi, Min; Qin, Yong; Ye, Qianwen; Che, Xiaotong; Wang, Shengjie; Lei, Mingxing (Frontiers in Oncology, 2023)
Purpose: Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases. Methods: We extracted a cohort of 124770 patients with a diagnosis of hepatocellular carcinoma from the Surveillance, Epidemiology, and End Results (SEER) program and enrolled a cohort of 1897 patients who were diagnosed as having bone metastases. Patients with a survival time of three or less months were considered to have had early death. Patients were randomly divided into two groups: a training cohort (n=1509, 80%) and an internal testing cohort (n=388, 20%). In the training cohort, five machine learning techniques were employed to train and optimize models, and an ensemble machine learning technique was used to generate risk probability in a way of soft voting. The study employed both internal and external validations, and the key performance indicators included area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. Patients from two tertiary hospitals were chosen as the external testing cohorts (n=98). Results: The early mortality was 55.5% (1052/1897). Eleven clinical characteristics were included as input features of machine learning models: sex (P=0.019), marital status (P=0.004), tumor stage (P=0.025), node stage (P=0.001), fibrosis score (P=0.040), AFP level (P=0.032), tumor size (P=0.001), lung metastases (P<0.001), cancer-directed surgery (P<0.001), radiation (P<0.001), and chemotherapy (P<0.001). Application of the ensemble model in the internal testing population yielded an AUROC of 0.779 (95% confident interval [CI]: 0.727-0.820), which was the largest AUROC among all models. Additionally, the ensemble model (0.191) outperformed the other five machine learning models in terms of Brier score. In terms of decision curves, the ensemble model also showed favorable clinical usefulness. External validation showed the similar results, with the AUROC of 0.764 and Brier score of 0.195, the prediction performance was further improved after revision of the model. Conclusions: The ensemble machine learning model exhibits promising prediction performance for early mortality among HCC patients with bone metastases.