{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:47:39Z","timestamp":1791928059714,"version":"4.3.4"},"reference-count":24,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Cancer"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Cervical nodal metastasis remains one of the strongest determinants of treatment planning and prognosis in oral squamous cell carcinoma. Histopathology remains the reference standard, but it does not provide immediate information at the point of surgical specimen assessment. Elastic scattering spectroscopy (ESS) offers rapid, label-free optical interrogation of tissue microstructure, and may support ex vivo nodal assessment.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to evaluate the diagnostic accuracy of ESS combined with machine learning classification for ex vivo detection of cervical lymph node metastasis in patients with oral squamous cell carcinoma.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a single-center observational ex vivo diagnostic modeling study in patients with oral squamous cell carcinoma undergoing primary tumor resection and neck dissection. A total of 3 ESS spectra were acquired from each bivalved lymph node before routine histopathological processing. Internal validation used repeated nested 5-fold cross-validation grouped by patient, so every spectrum and lymph node from a patient remained in the same fold. All preprocessing, scaling, dimensionality reduction, within-model-family configuration, and hyperparameter tuning, probability calibration where required, and decision-threshold selection were restricted to training data within the nested validation process; comparisons between candidate model families were exploratory and were based on pooled patient-grouped out-of-fold predictions. Spectrum-level probabilities were aggregated within each lymph node, and histopathology was the reference standard.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 52 patients were assessed for eligibility, 45 were included, and 280 fresh lymph nodes were assessed by ESS; 96 were pathology positive, and 184 were pathology negative. Of 840 acquired spectra, 760 passed quality control. Of the 280 nodes, 274 retained at least 2 usable spectra and comprised the analytical cohort; 6 pathology-negative nodes yielded no analyzable node-level ESS result. The hybrid model was selected because it had the highest pooled patient-grouped out-of-fold node-level area under the receiver operating characteristic curve (AUROC), with an internally validated, exploratory estimate of 0.966 (95% CI 0.929\u20100.988). The same out-of-fold predictions informed model-family selection and performance reporting; this estimate does not represent independent validation of a prospectively fixed final model. At the cross-validation\u2013derived summary threshold, secondary descriptive operating characteristics were sensitivity 93.8% (95% CI 86.4%\u201097.6%), specificity 95.5% (95% CI 90.1%\u201098.3%), accuracy 94.9% (95% CI 90.4%\u201097.3%), positive predictive value 91.8% (95% CI 82.9%\u201096.3%), and negative predictive value 96.6% (95% CI 92.4%\u201098.8%). The Brier score was 0.063, with calibration-in-the-large of \u20130.03 and a calibration slope of 0.94. Patient-level sensitivity was 96% and specificity was 90%. Pathology-positive and pathology-negative nodes also showed differences across amplitude-related and shape-related spectral features.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n ESS showed promising ex vivo discrimination of metastatic cervical lymph nodes in this single-center model-development and internal-validation study. The findings support further investigation of ESS as a potential pathology-triage adjunct rather than a replacement for histopathology.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/103960","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:23:39Z","timestamp":1791926619000},"page":"e103960-e103960","source":"Crossref","is-referenced-by-count":0,"title":["Detection of Cervical Nodal Metastasis in Oral Squamous Cell Carcinoma Using Elastic Scattering Spectroscopy: Ex Vivo Diagnostic Modeling Study"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6914-7401","authenticated-orcid":false,"given":"Waseem","family":"Jerjes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8334-4519","authenticated-orcid":false,"given":"Zaid","family":"Hamdoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8669-7414","authenticated-orcid":false,"given":"Dara","family":"Rashed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4710-5921","authenticated-orcid":false,"given":"Colin","family":"Hopper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","journal-title":"CA Cancer J Clin"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","DOI":"10.3390\/medsci11020042","article-title":"Epidemiology, risk factors, and prevention of head and neck squamous cell carcinoma","volume":"11","author":"Barsouk","journal-title":"Med Sci (Basel)"},{"issue":"6","key":"R3","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1001\/jama.2025.21733","article-title":"Head and neck cancer: a review","volume":"335","author":"Dunn","journal-title":"JAMA"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1401211","article-title":"Histopathological predictors of lymph node metastasis in oral cavity squamous cell carcinoma: a systematic review and meta-analysis","volume":"14","author":"Alqutub","journal-title":"Front Oncol"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1097\/JS9.0000000000001953","article-title":"Frequency of lymph node metastases at different neck levels in patients with oral squamous cell carcinoma: a systematic review and meta-analysis","volume":"111","author":"Yu","journal-title":"Int J Surg"},{"issue":"24","key":"R6","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13247622","article-title":"Comparing the efficacy of CT, MRI, PET-CT, and US in the detection of cervical lymph node metastases in head and neck squamous cell carcinoma with clinically negative neck lymph node: a systematic review and meta-analysis","volume":"13","author":"Alsibani","journal-title":"J Clin Med"},{"issue":"17","key":"R7","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16172954","article-title":"FDG PET-CT for the detection of occult nodal metastases in head and neck cancer: a systematic review and meta-analysis","volume":"16","author":"Guedj","journal-title":"Cancers (Basel)"},{"issue":"15","key":"R8","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12154958","article-title":"Radiomics-based analysis in the prediction of occult lymph node metastases in patients with oral cancer: a systematic review","volume":"12","author":"Jiang","journal-title":"J Clin Med"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.1177\/01455613241245204","article-title":"Past, present, and future diagnostic methods for the early noninvasive detection of oral premalignant lesions: a state of the art and systematic review","author":"Khong","journal-title":"Ear Nose Throat J"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neo.2024.101118","article-title":"Recent advances of photodiagnosis and treatment for head and neck squamous cell carcinoma","volume":"60","author":"Zhang","journal-title":"Neoplasia"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.1017\/erm.2024.26","article-title":"Advances in Raman spectroscopy for characterising oral cancer and oral potentially malignant disorders","volume":"26","author":"Hanna","journal-title":"Expert Rev Mol Med"},{"issue":"10","key":"R12","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines12102315","article-title":"Advancements in hyperspectral imaging and computer-aided diagnostic methods for the enhanced detection and diagnosis of head and neck cancer","volume":"12","author":"Wu","journal-title":"Biomedicines"},{"issue":"5","key":"R13","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17050796","article-title":"A scoping review of infrared spectroscopy and machine learning methods for head and neck precancer and cancer diagnosis and prognosis","volume":"17","author":"Alajaji","journal-title":"Cancers (Basel)"},{"issue":"7","key":"R14","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.oraloncology.2004.01.009","article-title":"Detection of cervical intranodal metastasis in oral cancer using elastic scattering spectroscopy","volume":"40","author":"Jerjes","journal-title":"Oral Oncol"},{"issue":"5","key":"R15","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1088\/0031-9155\/42\/5\/005","article-title":"Ultraviolet and visible spectroscopies for tissue diagnostics: fluorescence spectroscopy and elastic-scattering spectroscopy","volume":"42","author":"Bigio","journal-title":"Phys Med Biol"},{"issue":"2","key":"R16","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1117\/1.429990","article-title":"Diagnosis of breast cancer using elastic-scattering spectroscopy: preliminary clinical results","volume":"5","author":"Bigio","journal-title":"J Biomed Opt"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"issue":"10","key":"R18","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","journal-title":"Nat Med"},{"key":"R19","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"377","author":"Vasey","journal-title":"BMJ"},{"key":"R21","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.h5527","article-title":"STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies","volume":"351","author":"Bossuyt","journal-title":"BMJ"},{"issue":"4","key":"R22","first-page":"237","volume":"76","author":"Woolgar","journal-title":"Ann R Coll Surg Engl"},{"issue":"2","key":"R23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/s0009-9260(99)91066-8","article-title":"Magnetic resonance imaging in the assessment of cervical nodal metastasis in oral squamous cell carcinoma","volume":"54","author":"Wide","journal-title":"Clin Radiol"},{"issue":"11","key":"R24","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1001\/jamaoto.2024.3094","article-title":"Frozen section analysis of sentinel nodes in patients with oral squamous cell carcinoma: a systematic review and meta-analysis","volume":"150","author":"Abukhder","journal-title":"JAMA Otolaryngol Head Neck Surg"}],"container-title":["JMIR Cancer"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:23:41Z","timestamp":1791926621000},"score":1,"resource":{"primary":{"URL":"https:\/\/cancer.jmir.org\/2026\/1\/e103960"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,13]]},"references-count":24,"URL":"https:\/\/doi.org\/10.2196\/103960","relation":{},"ISSN":["2369-1999"],"issn-type":[{"value":"2369-1999","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,13]]},"article-number":"v12i3e103960"}}