{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:16:42Z","timestamp":1790749002648,"version":"4.1.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1112","license":[{"start":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T00:00:00Z","timestamp":1592524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:sec>\n                  <jats:title>Objectives:<\/jats:title>\n                  <jats:p>High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods:<\/jats:title>\n                  <jats:p>Three neural network models, including back propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and Probabilistic Genetic Algorithm-Back Propagation (PGA-BP) neural networks were trained to simulate association between patient survival and radiomics data in radiotherapy. To evaluate the models, a subgroup of 59 head-and-neck patients with primary cancers in oral tongue area were utilized. Quantitative image features were extracted from planning CT images, a novel t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to remove irrelevant and redundant image features before fed into the network models. 80% patients were used to train the models, and remaining 20% were used for evaluation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results:<\/jats:title>\n                  <jats:p>Of the three supervised machine-learning methods studied, PGA-BP yielded the best predictive performance. The reported actual patient survival interval of 30.5 \u00b1 21.3 months, the predicted survival times were 47.3 \u00b1 38.8, 38.5 \u00b1 13.5\u2009and 29.9 \u00b1 15.3 months using the traditional PCA. Combining with the novel t-SNE dimensionality reduction algorithm, the predicted survival intervals are 35.8 \u00b1 15.2, 32.3 \u00b1 13.1\u2009and 31.6 \u00b1 15.8 months for the BP, GA-BP and PGA-BP neural network models, respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion:<\/jats:title>\n                  <jats:p>The work demonstrated that the proposed probabilistic genetic algorithm optimized neural network models, integrating with the t-SNE dimensionality reduction algorithm, achieved accurate prediction of patient survival.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge:<\/jats:title>\n                  <jats:p>The proposed PGA-BP neural network, integrating with an advanced dimensionality reduction algorithm (t-SNE), improved patient survival prediction accuracy using pre-treatment quantitative CT image features of head-and-neck cancer patients.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1259\/bjr.20190825","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T20:51:18Z","timestamp":1591822278000},"source":"Crossref","is-referenced-by-count":28,"title":["Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models"],"prefix":"10.1093","volume":"93","author":[{"given":"Xiaoying","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications , Xi\u2019an , Shaanxi 710121, PR China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing , Xi\u2019an , Shaanxi 710121, PR China"},{"name":"Department of Radiation Oncology, University of California Los Angeles , Los Angeles, CA 90095 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications , Xi\u2019an , Shaanxi 710121, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"QingPing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications , Xi\u2019an , Shaanxi 710121, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications , Xi\u2019an , Shaanxi 710121, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Claude","family":"Rwigema","sequence":"additional","affiliation":[{"name":"Dept. of Radiation Oncology, MAYO CLINIC COLLEGE OF MEDICINE AND SCIENCE ARIZONA , Phoenix, AZ , United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"X. 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