A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning

Ziye Wang; Chan Xu; Wencai Liu; Meiying Zhang; Jian'an Zou; Mingfeng Shao; Xiaowei Feng; Qinwen Yang; Wenle Li; Xiue Shi; Guangxi Zang; Chengliang Yin

doi:10.3389/fendo.2022.1083569

A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning

Front Endocrinol (Lausanne). 2023 Jan 5:13:1083569. doi: 10.3389/fendo.2022.1083569. eCollection 2022.

Authors

Ziye Wang¹, Chan Xu², Wencai Liu³, Meiying Zhang⁴, Jian'an Zou¹, Mingfeng Shao¹, Xiaowei Feng⁵, Qinwen Yang⁶, Wenle Li^{5

7}, Xiue Shi⁸, Guangxi Zang⁹, Chengliang Yin⁹

Affiliations

¹ Department of Urology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China.
² Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
³ Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
⁴ Department of Gastroenterology and Hepatology, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
⁵ Department of Neuro Rehabilitation, Shaanxi Provincial Rehabilitation Hospital, Xi'an, China.
⁶ School of Computer Science and Engineering, North Minzu University, Yinchuan, China.
⁷ State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
⁸ Department of Geriatrics, Shaanxi Provincial Rehabilitation Hospital, Xi'an, China.
⁹ Faculty of Medicine, Macau University of Science and Technology, Macau, Macao SAR, China.

Abstract

Background: Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment.

Methods: The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model.

Results: Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM.

Conclusions: This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

Keywords: liver metastasis; machine learning; prognostic factors; renal cell carcinoma; web calculator.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Carcinoma, Renal Cell*
Humans
Kidney Neoplasms*
Liver Neoplasms*
Machine Learning
Models, Statistical
Prognosis
Retrospective Studies