{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T11:05:18Z","timestamp":1771153518655,"version":"3.50.1"},"reference-count":37,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271490"],"award-info":[{"award-number":["62271490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Purpose<\/jats:title>\n                    <jats:p>Radiotherapy (RT) is a critical treatment for lung cancer; however, individual responses vary significantly. Pre\u2010treatment prediction of RT response could guide clinical decision\u2010making and identify patients unlikely to benefit. This study aims to predict RT response in lung cancer patients using multimodal data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients diagnosed with lung cancer and scheduled to undergo RT at a single institution between May 2022 and October 2024 were selected. Multimodal data, encompassing demographic, radiological, biological, and physiological characteristics, were collected 1 week before RT initiation. Treatment plans followed the International Commission on Radiation Units and Measurements (ICRU) Report 83 guidelines, developed using a commercial treatment planning system and delivered via a linear accelerator with 6 MV photon beams. Radiological and biological data were reassessed 4 weeks after completion of RT, with treatment response classified according to Response Evaluation Criteria in Solid Tumors (RECIST). The dataset was split into training (70%), validation (15%), and testing (15%) sets using a stratified random sampling approach. A back propagation neural network (BPNN) was trained on the training set, and model performance was validated on the testing set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 120 patients were analyzed. Of these, 41 were classified as having partial response (PR), 69 as stable disease (SD), and 10 as progressive disease (PD). Significant differences were observed among the groups in 58 characteristics, including 2 demographic, 5 radiological, 1 biological, and 50 physiological. Among the 34 features analyzed for PR prediction, the maximum vertical tumor diameter achieved an AUC of 0.699 (95% CI: 0.630\u20130.757). In contrast, the comprehensive BPNN model incorporating all characteristics showed an AUC of 0.855 (95% CI: 0.843\u20100.875), with a prediction mean squared error (MSE) of 0.07. Similarly, among the 36 features analyzed for PD prediction, the zero\u2010crossing ratio of surface electromyography signals achieved an AUC of 0.750 (95% CI: 0.648\u20130.841). The comprehensive model further increased AUC to 0.929 (95% CI: 0.900\u20130.960), with a prediction MSE of 0.01.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Pretreatment demographic, radiological, and physiological characteristics were associated with RT response in lung cancer patients. The developed BPNN models leveraging multimodal data effectively predicted PR and PD, to guide personalized treatment strategies and to identify patients unlikely to benefit from RT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70277","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T03:35:31Z","timestamp":1759894531000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Response prediction of radiotherapy in lung cancer patients using multimodal data"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1613-2927","authenticated-orcid":false,"given":"Xiaojuan","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering and Imaging Medicine Army Medical University  Chongqing China"},{"name":"Department of Oncology Second Affiliated Hospital of Army Medical University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yushun","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Imaging Medicine Army Medical University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Imaging Medicine Army Medical University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Oncology Second Affiliated Hospital of Army Medical University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering First Affiliated Hospital of Army Medical University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6114-2382","authenticated-orcid":false,"given":"Yongqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Imaging Medicine Army Medical University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jncc.2024.01.006"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.835844"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213\u20102600(24)00189\u20109"},{"issue":"10195","key":"e_1_2_13_5_1","article-title":"Stereotactic ablative radiotherapy with or without immunotherapy for early\u2010stage or isolated lung parenchymal recurrent node\u2010negative non\u2010small\u2010cell lung cancer: an open\u2010label, randomised, phase 2 trial","volume":"394","author":"Chang JY","year":"2024","journal-title":"Lancet"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.path.2019.11.002"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2016.09.001"},{"issue":"104","key":"e_1_2_13_8_1","article-title":"Current and emerging roles of functional imaging in radiation therapy","volume":"19","author":"Mahase SS","year":"2015","journal-title":"Discov Med"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40425\u2010016\u20100178\u20101"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2012.10.010"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.04.059"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43018\u2010022\u201000416\u20108"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.4347"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2025.02.011"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.110047"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2023.09.007"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.3978\/j.issn.2072\u20101439.2013.10.05"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2024.04.015"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885\u2010018\u20104904\u20106"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychores.2016.08.004"},{"issue":"12","key":"e_1_2_13_21_1","article-title":"A review on non\u2010invasive biosensors for continuous health monitoring","volume":"19","author":"Chaudhari P","year":"2019","journal-title":"Sensors"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080832"},{"key":"e_1_2_13_23_1","volume-title":"Practical Neural Network Recipes in C++","author":"Masters T","year":"1993"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/0925-2312(95)00039-9"},{"key":"e_1_2_13_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/biom15070963"},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1492399"},{"key":"e_1_2_13_27_1","doi-asserted-by":"publisher","DOI":"10.1158\/2159\u20108290.CD\u201013\u20100314"},{"key":"e_1_2_13_28_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010025\u201055847\u20105"},{"key":"e_1_2_13_29_1","unstructured":"National Comprehensive Cancer Network. Bone cancer (version 2. 2019). Accessed April 10 2019http:\/\/www.nccn.org\/professionals\/physician_gls\/pdf\/bone.pdf"},{"key":"e_1_2_13_30_1","doi-asserted-by":"crossref","unstructured":"RyuWK KimJS ParkMH et\u00a0al.Heterogeneous radiological response to chemotherapy is associated with poor prognosis in advanced non\u2010small\u2010cell lung cancer. Thorac Cancer.2021;12(24):3333\u20103339.10.1111\/1759\u20107714.14207","DOI":"10.1111\/1759-7714.14207"},{"key":"e_1_2_13_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10555\u2010007\u20109055\u20101"},{"key":"e_1_2_13_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2005.01.008"},{"key":"e_1_2_13_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuni.2023.09.011"},{"key":"e_1_2_13_34_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010019\u201050358\u2010y"},{"key":"e_1_2_13_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/bios13020236"},{"key":"e_1_2_13_36_1","doi-asserted-by":"publisher","DOI":"10.3233\/XST\u2010180472"},{"key":"e_1_2_13_37_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13054\u2010018\u20102172\u20100"},{"key":"e_1_2_13_38_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.00934"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70277","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/acm2.70277","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T10:49:57Z","timestamp":1771152597000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":37,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10.1002\/acm2.70277"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70277","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"2025-05-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70277"}}