{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:54:37Z","timestamp":1780102477874,"version":"3.54.0"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"content-version":"vor","delay-in-days":51,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81572652"],"award-info":[{"award-number":["81572652"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771007"],"award-info":[{"award-number":["61771007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003785","name":"Guangdong Medical Research Foundation","doi-asserted-by":"publisher","award":["A2016320"],"award-info":[{"award-number":["A2016320"]}],"id":[{"id":"10.13039\/501100003785","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:p>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\u2010group 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\u2010fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis\u2010free survival (DMFS), and local\u2010region relapse\u2010free survival (LRFS), and their sum was recorded as the final voting score, representative of progression\u2010free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data\u2010based model compared with the tumor, node, and metastasis (TNM) system\u2010based model was 0.796 (<jats:italic>P<\/jats:italic> = 0.008) for OS, 0.752 (<jats:italic>P<\/jats:italic> = 0.053) for DMFS, and 0.721 (<jats:italic>P<\/jats:italic> = 0.025) for LRFS. The Kaplan\u2010Meier (KM) test values for II\/I, III\/II, IV\/III groups in our new machine learning\u2010based scoring system were 0.011, 0.010, and &lt;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 &lt;0.001, respectively. Significant differences were observed in the new machine learning\u2010based scoring system analysis of each curve (<jats:italic>P<\/jats:italic> &lt; 0.05), whereas the <jats:italic>P<\/jats:italic> 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.<\/jats:p>","DOI":"10.1155\/2020\/8068913","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T23:37:13Z","timestamp":1582328233000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8578-6425","authenticated-orcid":false,"given":"Chunyan","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunxin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annan","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5854-3989","authenticated-orcid":false,"given":"Haojiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3977-0518","authenticated-orcid":false,"given":"Lizhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,2,21]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.epi-06-0353"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(15)00055-0"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.5732\/cjc.014.10086"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21338"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0360-3016(02)02760-8"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.01.018"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0360-3016(02)02724-4"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2004.05.022"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.03.024"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0360-3016(01)01537-1"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(20011201)92:11<2845::aid-cncr10133>3.0.co;2-7"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2004.07.702"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1097\/00004728-199607000-00012"},{"key":"e_1_2_10_14_2","volume-title":"AJCC Cancer Staging Manual","author":"Amin M. 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