RuleFit-Based Nomogram Using Inflammatory Indicators for Predicting Survival in Nasopharyngeal Carcinoma, a Bi-Center Study

Abstract

Chao Luo,1,* Shuqi Li,1,* Qin Zhao,1,* Qiaowen Ou,2 Wenjie Huang,1 Guangying Ruan,1 Shaobo Liang,3 Lizhi Liu,1,4 Yu Zhang,5 Haojiang Li1 1Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People’s Republic of China; 2Department of Clinical Nutrition, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, People’s Republic of China; 3Department of Radiotherapy, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People’s Republic of China; 4Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, Guangdong, People’s Republic of China; 5Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Haojiang Li, Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected] Yu Zhang, Department of Pathology, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of China, Email [email protected]: Traditional prognostic studies utilized different cut-off values, without evaluating potential information contained in inflammation-related hematological indicators. Using the interpretable machine-learning algorithm RuleFit, this study aimed to explore valuable inflammatory rules reflecting prognosis in nasopharyngeal carcinoma (NPC) patients.Patients and Methods: In total, 1706 biopsy-proven NPC patients treated in two independent hospitals (1320 and 386) between January 2010 and March 2014 were included. RuleFit was used to develop risk-predictive rules using hematological indicators with no distributive difference between the two centers. Time-event-dependent hematological rules were further selected by stepwise multivariate Cox analysis. Combining high-efficiency hematological rules and clinical predictors, a final model was established. Models based on other algorithms (AutoML, Lasso) and clinical predictors were built for comparison, as well as a reported nomogram. Area under the receiver operating characteristic curve (AUROC) and concordance index (C-index) were used to verify the predictive precision of different models. A site-based app was established for convenience.Results: RuleFit identified 22 combined baseline hematological rules, achieving AUROCs of 0.69 and 0.64 in the training and validation cohorts, respectively. By contrast, the AUROCs of the optimal contrast model based on AutoML were 1.00 and 0.58. For overall survival, the final model had a much higher C-index than the base model using TN staging in two cohorts (0.769 vs 0.717, P< 0.001; 0.752 vs 0.688, P< 0.001), and showing great generalizability in training and validation cohorts. The two models based on RuleFit rules performed best, compared with other models. As for other endpoints, the final model showed a similar trend. Kaplan–Meier curve exhibited 22.9% (390/1706) patients were “misclassified” by AJCC staging, but the final model could assess risk classification accurately.Conclusion: The proposed final models based on inflammation-related rules based on RuleFit showed significantly elevated predictive performance.Keywords: machine learning, nomograms, nasopharyngeal carcinoma, prognosis, survival analysis

Keywords

• machine learning
• nomograms
• nasopharyngeal carcinoma
• prognosis
• survival analysis