{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:20Z","timestamp":1753893080857,"version":"3.41.2"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Background and purpose<\/jats:title><jats:p>We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2024.1329737","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T04:59:41Z","timestamp":1712293181000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer"],"prefix":"10.3389","volume":"7","author":[{"given":"Saad Bin Saeed","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahzaib","family":"Naeem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agha Muhammad Hammad","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bilal Mazhar","family":"Qureshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amjad","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bulent","family":"Aydogan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wazir","family":"Muhammad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1093\/annonc\/mdu444","article-title":"Association between treatment toxicity and outcomes in oncology clinical trials","volume":"25","author":"Abola","year":"2014","journal-title":"Ann. Oncol."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"106386","DOI":"10.1016\/j.oraloncology.2023.106386","article-title":"Machine learning for the prediction of toxicities from head and neck cancer treatment: a systematic review with meta-analysis","volume":"140","author":"Ara\u00fajo","year":"2023","journal-title":"Oral Oncol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1080\/0284186X.2023.2213445","article-title":"Liver cancer risk quantification through an artificial neural network based on personal health data","volume":"62","author":"Ataei","year":"2023","journal-title":"Acta Oncol."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.1002\/mp.13460","article-title":"Longitudinal fan-beam computed tomography dataset for head-and-neck squamous cell carcinoma patients","volume":"46","author":"Bejarano","year":"2019","journal-title":"Med. Phys."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/s13014-020-01591-7","article-title":"Clinical implication in the use of the AAA algorithm versus the AXB in nasopharyngeal carcinomas by comparison of TCP and NTCP values","volume":"15","author":"Bufacchi","year":"2020","journal-title":"Radiat. Oncol."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.ejmp.2015.10.091","article-title":"RADBIOMOD: a simple program for utilising biological modelling in radiotherapy plan evaluation","volume":"32","author":"Chang","year":"2016","journal-title":"Phys. Med."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer imaging archive (TCIA): maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"S58","DOI":"10.1016\/j.ijrobp.2009.06.090","article-title":"Radiotherapydose\u2013volume effects on salivary gland function. International journal of radiation oncology* biology*","volume":"76","author":"Deasy","year":"2010","journal-title":"Physics"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3747\/co.27.6233","article-title":"Radiotherapy side effects: integrating a survivorship clinical lens to better serve patients","volume":"27","author":"Dilalla","year":"2020","journal-title":"Curr. Oncol."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.hoc.2019.08.005","article-title":"Modern radiation therapy planning and delivery","volume":"33","author":"Gardner","year":"2019","journal-title":"Hematol. Clin."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ejmp.2007.07.001","article-title":"A free program for calculating EUD-based NTCP and TCP in external beam radiotherapy","volume":"23","author":"Gay","year":"2007","journal-title":"Phys. Med."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"174","DOI":"10.3389\/fonc.2019.00174","article-title":"Radiomics and machine learning for radiotherapy in head and neck cancers","volume":"9","author":"Giraud","year":"2019","journal-title":"Front. Oncol."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e282","DOI":"10.1016\/j.prro.2022.12.003","article-title":"Deep learning\u2013based dose prediction for automated, individualized quality Assurance of Head and Neck Radiation Therapy Plans","volume":"13","author":"Gronberg","year":"2023","journal-title":"Pract. Radiat. Oncol."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-018-0002-5","article-title":"Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy","volume":"5","author":"Grossberg","year":"2018","journal-title":"Sci. Data"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/s11864-017-0484-y","article-title":"Overview of the 8th edition TNM classification for head and neck cancer","volume":"18","author":"Huang","year":"2017","journal-title":"Curr. Treat. Options Oncol."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.ijrobp.2012.08.030","article-title":"Toxicities affecting quality of life after chemo-IMRT of oropharyngeal Cancer: prospective study of patient-reported, observer-rated, and objective outcomes","volume":"85","author":"Hunter","year":"2013","journal-title":"Int. J. Radiat. Oncol."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"790","DOI":"10.3389\/fonc.2020.00790","article-title":"Machine learning-based models for prediction of toxicity outcomes in radiotherapy","volume":"10","author":"Isaksson","year":"2020","journal-title":"Front. Oncol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/0360-3016(94)00471-4","article-title":"Analysis of clinical complication data for radiation hepatitis using a parallel architecture model. International journal of radiation oncology* biology*","volume":"31","author":"Jackson","year":"1995","journal-title":"Physics"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1038\/s41572-020-00224-3","article-title":"Head and neck squamous cell carcinoma","volume":"6","author":"Johnson","year":"2020","journal-title":"Nat. Rev. Dis. Primer."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.jdsr.2020.02.001","article-title":"Oral management strategies for radiotherapy of head and neck cancer","volume":"56","author":"Kawashita","year":"2020","journal-title":"Jpn. Dent. Sci. Rev."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.3389\/fonc.2020.01020","article-title":"A literature review of the potential diagnostic biomarkers of head and neck neoplasms","volume":"10","author":"Konings","year":"2020","journal-title":"Front. Oncol."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"S13","DOI":"10.2307\/3576626","article-title":"Complication probability as assessed from dose-volume histograms","volume":"104","author":"Lyman","year":"1985","journal-title":"Radiat. Res."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1038\/s41416-021-01386-x","article-title":"Artificial intelligence-based methods in head and neck cancer diagnosis: an overview","volume":"124","author":"Mahmood","year":"2021","journal-title":"Br. J. Cancer"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","article-title":"A scaled conjugate gradient algorithm for fast supervised learning","volume":"6","author":"M\u00f8ller","year":"1993","journal-title":"Neural Netw."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3389\/frai.2019.00002","article-title":"Pancreatic cancer prediction through an artificial neural network","volume":"2","author":"Muhammad","year":"2019","journal-title":"Front. Artif. Intell."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"272","DOI":"10.3389\/fonc.2015.00272","article-title":"Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer","volume":"5","author":"Parmar","year":"2015","journal-title":"Front. Oncol."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.4103\/jcrt.JCRT_330_20","article-title":"Development and validation of an indigenous, radiobiological model-based tumor control probability and normal tissue complication probability estimation software for routine plan evaluation in clinics","volume":"18","author":"Patel","year":"2022","journal-title":"J. Cancer Res. Ther."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"475","DOI":"10.7150\/ijbs.55716","article-title":"Application of radiomics and machine learning in head and neck cancers","volume":"17","author":"Peng","year":"2021","journal-title":"Int. J. Biol. Sci."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"e231090","DOI":"10.1155\/2014\/231090","article-title":"The role of imaging in radiation therapy planning: past, present, and future","volume":"2014","author":"Pereira","year":"2014","journal-title":"Biomed. Res. Int."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"105613","DOI":"10.1016\/j.rinp.2022.105613","article-title":"Comparative study of artificial neural network versus parametric method in COVID-19 data analysis","volume":"38","author":"Shafiq","year":"2022","journal-title":"Results Phys."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.phro.2021.07.009","article-title":"Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy","volume":"19","author":"Thor","year":"2021","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1120\/jacmp.v5i1.1970","article-title":"A TCP-NTCP estimation module using DVHs and known radiobiological models and parameter sets","volume":"5","author":"Warkentin","year":"2004","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1088\/0031-9155\/38\/6\/001","article-title":"A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density","volume":"38","author":"Webb","year":"1993","journal-title":"Phys. Med. Biol."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1002\/mp.16274","article-title":"Modeling clinical outcomes in radiotherapy: NTCP, TCP and the \u201cTECs\u201d","volume":"50","author":"Yorke","year":"2023","journal-title":"Med. Phys."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1088\/0031-9155\/45\/2\/303","article-title":"Tumour control probability: a formulation applicable to any temporal protocol of dose delivery","volume":"45","author":"Zaider","year":"2000","journal-title":"Phys. Med. Biol."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1329737\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T04:59:43Z","timestamp":1712293183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1329737\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,5]]},"references-count":35,"alternative-id":["10.3389\/frai.2024.1329737"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1329737","relation":{},"ISSN":["2624-8212"],"issn-type":[{"type":"electronic","value":"2624-8212"}],"subject":[],"published":{"date-parts":[[2024,4,5]]},"article-number":"1329737"}}