{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T07:07:02Z","timestamp":1779088022859,"version":"3.51.4"},"reference-count":25,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001411","name":"Indian Council of Medical Research","doi-asserted-by":"crossref","award":["File No. 5\/3\/8\/41\/ITR-F\/2019-ITR"],"award-info":[{"award-number":["File No. 5\/3\/8\/41\/ITR-F\/2019-ITR"]}],"id":[{"id":"10.13039\/501100001411","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Optical coherence tomography (OCT) is extremely useful in the screening and detection of oral cancers. But various challenges, such as its subjectivity, operator dependence in interpretation, and lack of quantitative outcomes, have been delaying its adoption in clinical decision-making. The objective of this research is to quantify various A-scan features embedded in the OCT signal through advanced signal processing techniques and machine learning algorithms.\n                    <jats:italic>Ex vivo<\/jats:italic>\n                    imaging of biopsied oral tissues (normal mucosa, carcinoma\n                    <jats:italic>in situ<\/jats:italic>\n                    (CIS), well-differentiated, and poorly differentiated oral squamous cell carcinoma) was performed using a spectral domain OCT system. Our A-scan dataset consisted of representative 1D signals obtained from different regions of the tissue bed. A set of 12 time- and 8 frequency-domain features were computed on each A-scan, and ten machine learning models were evaluated for binary and multi-label classification. LightGBM achieved the highest performance in both binary (accuracy: 0.8847,\n                    <jats:italic>F<\/jats:italic>\n                    1: 0.8878, AUC: 0.9539) and multi-label classification (accuracy: 0.8248,\n                    <jats:italic>F<\/jats:italic>\n                    1: 0.8201, AUC: 0.964). LightGBM was selected as the final model based on its superior and consistent performance across both classification paradigms. Our proof-of-concept feasibility study demonstrates good accuracy for differentiating oral mucosal tissues, highlighting the biomarker signature of A-scans.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/ae6aa1","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T22:47:50Z","timestamp":1778280470000},"page":"035027","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Oral squamous cell carcinoma diagnosis with time and frequency domain features from optical coherence tomography A-scan signals"],"prefix":"10.1088","volume":"12","author":[{"given":"Prashanth","family":"Panta","sequence":"first","affiliation":[{"name":"Indian Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarfaraj","family":"Mirza","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3254-2472","authenticated-orcid":true,"given":"Renu","family":"John","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"bpexae6aa1bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2024.107063","type":"journal-article","article-title":"Burden of oral cancer and associated risk factors at national and state levels: a systematic analysis from the global burden of disease in India, 1990","volume":"159","author":"Akashanand","year":"2021","journal-title":"Oral Oncol."},{"key":"bpexae6aa1bib2","doi-asserted-by":"publisher","DOI":"10.1142\/s1793545824500202","type":"journal-article","article-title":"Optical coherence tomography for surgical demarcation of GI tumor margins for accelerated decision-making during the surgical resection of tumors","volume":"18","author":"Veluthedath","year":"2025","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-01-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-08","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}