Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer

Abstract

BackgroundThe liver is the most common site of distant metastasis in rectal cancer, and liver metastasis dramatically affects the treatment strategy of patients. This study aimed to develop and validate a clinical prediction model based on machine learning algorithms to predict the risk of liver metastasis in patients with rectal cancer.MethodsWe integrated two rectal cancer cohorts from Surveillance, Epidemiology, and End Results (SEER) and Chinese multicenter hospitals from 2010-2017. We also built and validated liver metastasis prediction models for rectal cancer using six machine learning algorithms, including random forest (RF), light gradient boosting (LGBM), extreme gradient boosting (XGB), multilayer perceptron (MLP), logistic regression (LR), and K-nearest neighbor (KNN). The models were evaluated by combining several metrics, such as the area under the curve (AUC), accuracy score, sensitivity, specificity and F1 score. Finally, we created a network calculator using the best model.ResultsThe study cohort consisted of 19,958 patients from the SEER database and 924 patients from two hospitals in China. The AUC values of the six prediction models ranged from 0.70 to 0.95. The XGB model showed the best predictive power, with the following metrics assessed in the internal test set: AUC (0.918), accuracy (0.884), sensitivity (0.721), and specificity (0.787). The XGB model was assessed in the outer test set with the following metrics: AUC (0.926), accuracy (0.919), sensitivity (0.740), and specificity (0.765). The XGB algorithm also shows a good fit on the calibration decision curves for both the internal test set and the external validation set. Finally, we constructed an online web calculator using the XGB model to help generalize the model and to assist physicians in their decision-making better.ConclusionWe successfully developed an XGB-based machine learning model to predict liver metastasis from rectal cancer, which was also validated with a real-world dataset. Finally, we developed a web-based predictor to guide clinical diagnosis and treatment strategies better.

Establishment and validation of diagnostic and prognostic prediction models for liver metastasis in patients with rectal cancer: a SEER based study.

“…Distant metastasis from rectal cancer usually results in poorer survival and quality of life (24), half of them had liver-limited disease(LLD) (25).Therefore, we must identify the effective risk factors and prognostic factors in patients with liver metastases from rectal cancer, so as to facilitate early prevention and diagnosis, and effectively evaluate the prognosis of such patients. In the present study, we constructed a diagnostic nomogram for predicting the occurrence of liver metastases in patients with rectal cancer, and a prognostic nomogram for patients with RCLM.…”

“…In addition, to the best of our knowledge, only a realistic model has been developed to predict the occurrence risk of RCLM. In the prediction model established by Qiu et al, only seven clinical features were included, and there was no relevant treatment information, which led to the fact that this model could not comprehensively predict the occurrence liver metastasis (20). To address this limitation, we integrated the latest large sample with comprehensive clinical information from the SEER database, including more clinicopathological features and information on chemotherapy, radiotherapy, and surgery, and developed two novel nomograms to be applied to the diagnosis and prognosis of RCLM.…”

A bibliometric analysis of the advance of artificial intelligence in medicine

“…In dermatology, the combination of convolutional neural networks and transfer learning techniques is shown to be effective in skin lesion classification, providing a valuable tool for the early detection of skin cancer [38]. Furthermore, the application of machine learning algorithms, such as XGBoost, to the prediction of liver metastases in rectal cancer demonstrates a significant impact on clinical decision-making, supported by substantial accuracy in the evaluation of multiple metrics [41]. In the context of lung cancer, the use of machine learning methods to analyze histopathological images demonstrates the ability of AI to accurately predict patient prognosis, thereby contributing to precision oncology [43].…”