MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

Wei Pei; Chen Wang; Hai Liao; Xiaobo Chen; Yunyun Wei; Xia Huang; Xueli Liang; Huayan Bao; Danke Su; Guanqiao Jin

doi:10.1186/s12885-022-09832-6

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

BMC Cancer. 2022 Jul 6;22(1):739. doi: 10.1186/s12885-022-09832-6.

Authors

Wei Pei^#¹, Chen Wang^#¹, Hai Liao^#¹, Xiaobo Chen², Yunyun Wei¹, Xia Huang³, Xueli Liang¹, Huayan Bao¹, Danke Su⁴, Guanqiao Jin⁵

Affiliations

¹ Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, No. 71, Hedi Rd, Nanning, 530021, Guangxi, China.
² Southern Medical University, Guangzhou, China.
³ Department of Radiology, Wuzhou red cross hospital, Wuzhou, Guangxi, China.
⁴ Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, No. 71, Hedi Rd, Nanning, 530021, Guangxi, China. sudanke33@sina.com.
⁵ Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, No. 71, Hedi Rd, Nanning, 530021, Guangxi, China. jinguanqiao77@gxmu.edu.cn.

^# Contributed equally.

Abstract

Background: The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT).

Methods: Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients.

Results: A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05).

Conclusion: The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.

Keywords: Machine learning; Magnetic resonance imaging; Nasopharyngeal carcinoma; Radiomics; Radom survival forest.

MeSH terms

Chemoradiotherapy / methods
Humans
Induction Chemotherapy* / methods
Magnetic Resonance Imaging / methods
Nasopharyngeal Carcinoma / drug therapy
Nasopharyngeal Carcinoma / therapy
Nasopharyngeal Neoplasms* / diagnostic imaging
Nasopharyngeal Neoplasms* / drug therapy
Progression-Free Survival