Machine learning to improve prognosis prediction of metastatic clear-cell renal cell carcinoma treated with cytoreductive nephrectomy and systemic therapy

Abstract

Cytoreductive nephrectomy (CN) combined with systemic therapy is commonly used to treat metastatic clear-cell renal cell carcinoma (mccRCC). However, prognostic models for these patients are limited. In the present study, the clinical data of 782 mccRCC patients who received both CN and systemic therapy were obtained from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2016), and patients were divided into training and internal test cohorts. A total of 144 patients who met the same criteria from our center (Peking Union Medical College Hospital) were placed in the external test cohort. The cancer-specific survival rate (CSS) at 1, 3, and 5 years was set as the research outcome. Then, four ML models, i.e., a gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and logistic regression (LR), were established. Fifteen potential independent features were included in this study. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA). Seven clinical features, namely pathological grade, T stage, N stage, number of metastatic sites, brain or liver metastases, and metastasectomy were selected for subsequent analysis via the recursive feature elimination (RFE) algorithm. In conclusion, the GBM model performed best at 1-, 3- and 5-year CSS prediction (0.836, 0.819 and 0.808, respectively in the internal test cohort and 0.819, 0.805 and 0.786, respectively in the external cohort). Furthermore, we divided the patients into three strata (high-, intermediate- and low-risk) via X-tile analysis and concluded that clinically individualized treatment can be aided by these practical prognostic models.

Machine-learning based integrating bulk and single-cell RNA sequencing reveals the SLC38A5-CCL5 signaling as a promising target for clear cell renal cell carcinoma treatment

“…Benefiting from advances in high throughput sequencing and bioinformatic methodologies, various biological processes-associated mRNA/lncRNA/miRNA signatures have been established to predict the prognosis and response to therapy [ 34 , 35 ]. However, these multigene signatures are hardly applied to clinical practices due to inappropriate machine-learning methods and underutilized data information.…”

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

“…Machine learning is a new type of artificial intelligence, which has been widely used in medical data analysis and is a powerful tool for improving clinical strategies [14][15][16]. Some tree-based machine learning methods (such as survival tree [ST], random survival forest [RSF], and gradient boosting machine [GBM]) can account for interaction and effect modification between variables and have been applied in some prognosis studies [17][18][19][20][21][22][23][24][25][26][27]. In many studies, in which the categorical variable was the dependent variable, the prediction performance of machine learning is better than that of traditional models [6,14,28,29].…”