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Copyright © 2020 Chunyan Cui et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0/

Abstract

We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification system for NPC, superior to the current clinical staging system. Between January 2010 and January 2013, 792 patients with recent diagnosis of NPC, who had MR image data, were enrolled in the study. The AutoML algorithm was used and all statistical analyses were based on the 10-fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis-free survival (DMFS), and local-region relapse-free survival (LRFS), and their sum was recorded as the final voting score, representative of progression-free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data-based model compared with the tumor, node, and metastasis (TNM) system-based model was 0.796 (P=0.008) for OS, 0.752 (P=0.053) for DMFS, and 0.721 (P=0.025) for LRFS. The Kaplan-Meier (KM) test values for II/I, III/II, IV/III groups in our new machine learning-based scoring system were 0.011, 0.010, and <0.001, respectively, whereas those for II/I, III/II, IV/III groups in the TNM/American Joint Committee on Cancer (AJCC) system were 0.118, 0.121, and <0.001, respectively. Significant differences were observed in the new machine learning-based scoring system analysis of each curve (P<0.05), whereas the P values of curves obtained from the TNM/AJCC system, between II/I and III/II, were 0.118 and 0.121, respectively, without a significant difference. In conclusion, the AutoML algorithm demonstrated better prognostic performance than the TNM/AJCC system for NPC. The algorithm showed a good potential for clinical application and may aid in improving counseling and facilitate the personalized management of patients with NPC. The clinical application of our new scoring and staging system may significantly improve precision medicine.

Details

Title
Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis
Author
Cui, Chunyan 1   VIAFID ORCID Logo  ; Wang, Shunxin 2 ; Zhou, Jian 1 ; Annan Dong 1 ; Xie, Fei 1 ; Li, Haojiang 1   VIAFID ORCID Logo  ; Liu, Lizhi 1   VIAFID ORCID Logo 

 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 510060, China 
 Zhongshan School of Medical, Sun Yat-sen University, Guangzhou, Guangdong 510080, China 
Editor
K H Mok
Publication year
2020
Publication date
2020
Publisher
John Wiley & Sons, Inc.
ISSN
23146133
e-ISSN
23146141
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2369203540
Copyright
Copyright © 2020 Chunyan Cui et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0/