{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T10:30:40Z","timestamp":1786617040526,"version":"build-2736575974"},"reference-count":46,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100016003","name":"Scientific Research Projects Coordination Unit of \u0130stanbul University-Cerrahpa\u015fa Rectorate","doi-asserted-by":"publisher","award":["37374"],"award-info":[{"award-number":["37374"]}],"id":[{"id":"10.13039\/501100016003","id-type":"DOI","asserted-by":"publisher"}]},{"award":["37374"],"award-info":[{"award-number":["37374"]}],"id":[{"id":"https:\/\/ror.org\/01dzn5f42","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study aimed to develop a personalized, supervised machine learning-driven predictive model for multiple chronic toxicities by integrating clinical, dosimetric, and genetic data specifically evaluating the impact of the NBN gene rs1805794 (c.553G&gt;C) polymorphism. Methods: This study enrolled 125 patients with HNSCC who received curative-intent radiotherapy and remained disease-free during follow-up with a median of 98 months. Comprehensive clinical and dosimetric data were collected, and chronic toxicities were recorded. Peripheral blood samples were analyzed for the NBN rs1805794 polymorphism using allele-specific PCR (AS-PCR). Following feature selection, four supervised machine learning classifiers were trained and evaluated to identify the optimal model for predicting multiple chronic toxicities. Results: The XGBoost algorithm emerged as the highest performing model. Baseline clinico-dosimetric predictors of multiple chronic toxicities included PTV70 volume, the addition of concurrent chemotherapy, advanced T and N stages, and continued smoking. Integrating the NBN rs1805794 genotype into the XGBoost architecture enhances its predictive capability. The final model accurately identified patients at high risk for multiple chronic toxicities, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, a sensitivity of 0.74, and a specificity of 0.79. Conclusions: Integrating clinical, dosimetric, and genetic data within a machine learning framework effectively predicts multiple chronic toxicities in HNSCC. This approach enabled early risk stratification, providing the potential for personalized therapy.<\/jats:p>","DOI":"10.3390\/jcm15166264","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T09:31:09Z","timestamp":1786613469000},"page":"6264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NBN rs1805794 Polymorphism Increases the Predictive Performance of Machine Learning Models for Multiple Chronic Toxicities in Head and Neck Cancer Survivors Treated with Definitive Radiotherapy \u00b1 Chemotherapy"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-7285","authenticated-orcid":false,"given":"Sevda","family":"Yener","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Ba\u011fc\u0131lar Training and Research Hospital, 34164 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4785-8189","authenticated-orcid":false,"given":"Seda","family":"Ekizoglu","sequence":"additional","affiliation":[{"name":"Department of Medical Biology, Cerrahpa\u015fa Medical Faculty, Istanbul University-Cerrahpa\u015fa, 34098 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2009-0002","authenticated-orcid":false,"given":"Meltem","family":"Da\u011fdelen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cerrahpa\u015fa Medical Faculty, Istanbul University-Cerrahpa\u015fa, 34098 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00f6k\u00e7en","family":"Civan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beykoz State Hospital, 34800 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1744-6995","authenticated-orcid":false,"given":"F\u0131rat","family":"Teveto\u011flu","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Cerrahpa\u015fa Medical Faculty, Istanbul University-Cerrahpa\u015fa, 34098 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2271-0209","authenticated-orcid":false,"given":"Zeliha K\u00fcbra","family":"\u00c7akan","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Cerrahpa\u015fa Medical Faculty, Istanbul University-Cerrahpa\u015fa, 34098 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ay\u015fe","family":"\u00c7\u0131rako\u011flu","sequence":"additional","affiliation":[{"name":"Department of Medical Biology, Cerrahpa\u015fa Medical Faculty, Istanbul University-Cerrahpa\u015fa, 34098 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d6mer Erol","family":"Uzel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cerrahpa\u015fa Medical Faculty, Istanbul University-Cerrahpa\u015fa, 34098 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int. J. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","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. Primers"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1016\/j.ijrobp.2007.04.088","article-title":"Meta-Analyses of Chemotherapy in Head and Neck Cancer (MACH-NC): An update","volume":"69","author":"Pignon","year":"2007","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.radonc.2021.01.024","article-title":"Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer: A new concept for individually optimised treatment","volume":"157","author":"Hoebers","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1007\/s00066-025-02437-5","article-title":"Is less more? Revisiting elective nodal irradiation in head and neck cancer","volume":"201","author":"Haderlein","year":"2025","journal-title":"Strahlenther. Onkol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1007\/s00066-025-02423-x","article-title":"Pentoxifylline and vitamin E for treating radiation-induced fibrosis in breast and head and neck cancer patients","volume":"201","author":"Andreassen","year":"2025","journal-title":"Strahlenther. Onkol."},{"key":"ref_7","first-page":"13","article-title":"Up-to-date modalities in the prevention of oral mucositis: A literature review","volume":"42","author":"Poposki","year":"2025","journal-title":"Acta Fac. Medicae Naissensis"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103335","DOI":"10.1016\/j.critrevonc.2021.103335","article-title":"Late radiation effects in survivors of head and neck cancer: State of the science","volume":"162","author":"Ortigara","year":"2021","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_9","first-page":"1034","article-title":"Otoprotective measures for cisplatin-based chemoradiotherapy-induced toxicity in patients with head and neck cancer: A systematic review and meta-analysis","volume":"33","author":"Marchetti","year":"2025","journal-title":"Support. Care Cancer"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3857\/roj.2014.32.3.103","article-title":"Mechanisms of radiation-induced normal tissue toxicity and implications for future clinical trials","volume":"32","author":"Kim","year":"2014","journal-title":"Radiat. Oncol. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S1470-2045(13)70011-1","article-title":"Induction chemotherapy followed by concurrent chemoradiotherapy (sequential chemoradiotherapy) versus concurrent chemoradiotherapy alone in locally advanced head and neck cancer (PARADIGM): A randomised phase 3 trial","volume":"14","author":"Haddad","year":"2013","journal-title":"Lancet Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6662","DOI":"10.1038\/s41598-022-10538-9","article-title":"Genetic susceptibility to patient-reported xerostomia among long-term oropharyngeal cancer survivors","volume":"12","author":"Aggarwal","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.2174\/1871530320999200918143856","article-title":"An Intimate Alliance of DNA-Damage Response Network with Cell-Cycle Checkpoints Amid Events of Uncontrolled Cellular Proliferation: A Mini-Review","volume":"21","author":"Chatterjee","year":"2021","journal-title":"Endocr. Metab. Immune Disord.-Drug Targets Former. Curr. Drug Targets-Immune Endocr. Metab. Disord."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Domagala, P., Jakubowska, A., Jaworska-Bieniek, K., Kaczmarek, K., Durda, K., Kurlapska, A., Cybulski, C., and Lubinski, J. (2015). Prevalence of germline mutations in genes engaged in DNA damage repair by homologous recombination in patients with triple-negative and hereditary non-triple-negative breast cancers. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130393"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Seemanova, E., Varon, R., Vejvalka, J., Jarolim, P., Seeman, P., Chrzanowska, K.H., Digweed, M., Resnick, I., Kremensky, I., and Saar, K. (2016). The Slavic NBN founder mutation: A role for reproductive fitness?. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0167984"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Hong, Y., Li, M., Long, J., Zhao, Y.-P., Zhang, J.-X., Li, Q., You, H., Tong, W.-M., and Jia, J.-D. (2013). Mutation inactivation of Nijmegen breakage syndrome gene (NBS1) in hepatocellular carcinoma and intrahepatic cholangiocarcinoma. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0082426"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1007\/s11033-011-1126-2","article-title":"Association between CYP1A1 polymorphism and colorectal cancer risk: A meta-analysis","volume":"39","author":"Zheng","year":"2012","journal-title":"Mol. Biol. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4217","DOI":"10.1158\/0008-5472.CAN-08-4915","article-title":"Genetic variation in B-cell\u2013activating factor is associated with an increased risk of developing B-cell non\u2013Hodgkin lymphoma","volume":"69","author":"Novak","year":"2009","journal-title":"Cancer Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1002\/ijc.28089","article-title":"Associations of Lys939Gln and Ala499Val polymorphisms of the XPC gene with cancer susceptibility: A meta-analysis","volume":"133","author":"He","year":"2013","journal-title":"Int. J. Cancer"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.ijrobp.2013.07.029","article-title":"Predicting esophagitis after chemoradiation therapy for non-small cell lung cancer: An individual patient data meta-analysis","volume":"87","author":"Palma","year":"2013","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Isaksson, L.J., Pepa, M., Zaffaroni, M., Marvaso, G., Alterio, D., Volpe, S., Corrao, G., Augugliaro, M., Starzy\u0144ska, A., and Leonardi, M.C. (2020). Machine learning-based models for prediction of toxicity outcomes in radiotherapy. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.00790"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.canrad.2023.05.001","article-title":"Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review","volume":"27","author":"Tan","year":"2023","journal-title":"Cancer Radioth\u00e9rapie"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Holmes, S., Chatzopoulou, D., and Coulthard, P. (2026). AI in Head and Neck Cancer Screening. Artificial Intelligence in Facial Trauma, Oral Diseases, and Systemic Health, Springer.","DOI":"10.1007\/978-3-032-11531-7"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1007\/s00066-020-01626-8","article-title":"Applications of radiomics and machine learning for radiotherapy of malignant brain tumors","volume":"196","author":"Kocher","year":"2020","journal-title":"Strahlenther. Onkol."},{"key":"ref_27","first-page":"27","article-title":"Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy","volume":"8","author":"Dean","year":"2018","journal-title":"Clin. Transl. Radiat. Oncol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.ijrobp.2019.06.009","article-title":"A deep learning model for predicting xerostomia due to radiation therapy for head and neck squamous cell carcinoma in the RTOG 0522 clinical trial","volume":"105","author":"Men","year":"2019","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Piras, A., Corso, R., Benfante, V., Ali, M., Laudicella, R., Alongi, P., D\u2019Aviero, A., Cusumano, D., Boldrini, L., and Salvaggio, G. (2024). Artificial intelligence and statistical models for the prediction of radiotherapy toxicity in prostate cancer: A systematic review. Appl. Sci., 14.","DOI":"10.3390\/app142310947"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lee, S., Liang, X., Woods, M., Reiner, A.S., Concannon, P., Bernstein, L., Lynch, C.F., Boice, J.D., Deasy, J.O., and Bernstein, J.L. (2020). Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0226157"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111011","DOI":"10.1016\/j.radonc.2025.111011","article-title":"Multiomic based Bayesian network toxicity modeling for simultaneous prediction of multiple toxicity outcomes in NSCLC","volume":"210","author":"Nair","year":"2025","journal-title":"Radiother. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"162","DOI":"10.4274\/uob.galenos.2020.1830","article-title":"Effect of Diabetes Mellitus and Metformin Usage on Treatment Outcomes and Side Effects on Prostate Cancer Treated with Radical Radiotherapy","volume":"20","author":"Barlas","year":"2021","journal-title":"Bull. Urooncol."},{"key":"ref_33","first-page":"466","article-title":"Does Diabetes Mellitus Increase Radiotherapy\/Chemoradiotherapy Acute Toxicities?","volume":"38","year":"2023","journal-title":"Turk. J. Oncol. T\u00fcrk Onkol. Derg."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1177\/0194599815607852","article-title":"Impact of diabetes mellitus on head and neck cancer patients undergoing surgery","volume":"154","author":"Raikundalia","year":"2016","journal-title":"Otolaryngol.\u2013Head Neck Surg."},{"key":"ref_35","first-page":"580","article-title":"Head and neck cancer patients: Impact of diabetes mellitus on surgical outcomes","volume":"21","author":"Bianchini","year":"2016","journal-title":"J. BUON"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1038\/s41416-022-01945-w","article-title":"Post-diagnosis smoking cessation and survival of patients with head and neck cancer: A systematic review and meta-analysis","volume":"127","author":"Caini","year":"2022","journal-title":"Br. J. Cancer"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e70207","DOI":"10.1002\/lio2.70207","article-title":"\u201cWho Needs More Help?\u201d: The Predictors of Continued Smoking in Laryngeal Cancer Patients at Different Periods","volume":"10","author":"Yener","year":"2025","journal-title":"Laryngoscope Investig. Otolaryngol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.oraloncology.2017.05.009","article-title":"Long-term toxicities in 10-year survivors of radiation treatment for head and neck cancer","volume":"71","author":"Dong","year":"2017","journal-title":"Oral Oncol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1016\/j.ijrobp.2019.03.043","article-title":"Locoregional control and mild late toxicity after reducing target volumes and radiation doses in patients with locoregionally advanced nasopharyngeal carcinoma treated with induction chemotherapy (IC) followed by concurrent chemoradiotherapy: 10-year results of a phase 2 study","volume":"104","author":"Zhao","year":"2019","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1186\/s13014-015-0558-6","article-title":"ERCC2 polymorphisms and radiation-induced adverse effects on normal tissue: Systematic review with meta-analysis and trial sequential analysis","volume":"10","author":"Song","year":"2015","journal-title":"Radiat. Oncol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Song, Y.-Z., Han, F.-J., Liu, M., Xia, C.-C., Shi, W.-Y., and Dong, L.-H. (2015). Association between single nucleotide polymorphisms in XRCC3 and radiation-induced adverse effects on normal tissue: A meta-analysis. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130388"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Delobel, J.-B., Gnep, K., Ospina, J.D., Beckendorf, V., Chira, C., Zhu, J., Bossi, A., Messai, T., Acosta, O., and Castelli, J. (2017). Nomogram to predict rectal toxicity following prostate cancer radiotherapy. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0179845"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gori\u010dar, K., Dugar, F., Dol\u017ean, V., and Marinko, T. (2022). NBN, RAD51 and XRCC3 polymorphisms as potential predictive biomarkers of adjuvant radiotherapy toxicity in early HER2-positive breast cancer. Cancers, 14.","DOI":"10.3390\/cancers14184365"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.radonc.2013.03.021","article-title":"A predictive model for dysphagia following IMRT for head and neck cancer: Introduction of the EMLasso technique","volume":"107","author":"Duprez","year":"2013","journal-title":"Radiother. Oncol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"E139","DOI":"10.1016\/j.ijrobp.2019.06.2182","article-title":"Machine learning to predict toxicity in head and neck cancer patients treated with definitive chemoradiation","volume":"105","author":"Wojcieszynski","year":"2019","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Barioni, E.D., Orhan, K., Borges-Oliveira, A.C., Lopes, S.L.P.d.C., and Costa, A.L.F. (2026). Multimodal Radiogenomic Imaging in Oropharyngeal Squamous Cell Carcinoma: Implications for Dentomaxillofacial Radiology. Med. Sci., 14.","DOI":"10.3390\/medsci14020174"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/16\/6264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T09:46:59Z","timestamp":1786614419000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/16\/6264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":46,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jcm15166264"],"URL":"https:\/\/doi.org\/10.3390\/jcm15166264","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}