Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis

• Published in: BioMed research international Vol. 2020; no. 1; p. 8068913
• Main Authors: Cui, Chunyan, Wang, Shunxin, Zhou, Jian, Dong, Annan, Xie, Fei, Li, Haojiang, Liu, Lizhi
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2020; John Wiley & Sons, Inc
• Subjects: Adult; Algorithms; Artificial intelligence; Cancer; Cancer therapies; Carcinoma; Clinical medicine; Clinical Study; Female; Generalized linear models; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medical prognosis; Metastases; Metastasis; Middle Aged; Nasopharyngeal carcinoma; Nasopharyngeal Carcinoma - diagnostic imaging; Nasopharyngeal Neoplasms - diagnostic imaging; Nasopharynx - diagnostic imaging; Neoplasm Staging; Precision medicine; Prognosis; Radiation therapy; Retrospective Studies; Statistical analysis; Survival; Systems analysis; Throat cancer; Tumor staging; Tumors; China; Taiwan
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2020/8068913

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.