{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:04:11Z","timestamp":1789707851698,"version":"4.0.1"},"reference-count":34,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:00:00Z","timestamp":1789171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: To characterize the degenerative vocabulary of 500 consecutive lumbar MRI reports using a transparent, reproducible text extraction pipeline; to quantify which descriptors distinguish included from excluded reports and which factors predict text-derived severity; and to specify a reproducible image analysis pipeline whose formal validation against radiologist Pfirrmann grading is defined as the next step. Methods: We analyzed 500 lumbar MRI reports (386 patients; December 2018\u2013November 2025), extracting morphological and severity keywords, segmental levels (L1\u2013L2 to L5\u2013S1), Modic mentions, and a custom text-derived ordinal severity (0\u20133). Keyword prevalence differences were tested with the Fisher exact test under Benjamini\u2013Hochberg false discovery rate control, with 95% confidence intervals (CIs) for all odds ratios (ORs) and risk differences; predictors of severity were examined by ordinal logistic regression. Robustness to within-patient clustering was assessed with patient-clustered robust estimation and one-report-per-patient analyses. A MATLAB pipeline derived per-disc candidate imaging features (predicted Pfirrmann grade, normalized disc height, T2 signal index, quality control) across 316 studies. Results: In total, 385\/500 reports (77.0%) met the inclusion criteria. The structured morphological keyword field was missing in 49.4% of records, varying by year (58.6% in 2020, 68.7% in 2023, 41.9% in 2024, 33.5% in 2025), indicating systematic reporting drift. Bulge was the dominant descriptor (53.8%); L4\u2013L5 and L5\u2013S1 dominated level mentions. After correction, bulge (OR 6.70, 95% CI 3.86\u201311.65), mild (5.08, 2.39\u201310.78), dehydration (9.82, 2.36\u201340.87) and central (8.63, 2.07\u201336.01) were enriched among the included reports. All four remained significant in clustering-aware sensitivity analyses. Older age independently predicted higher severity (OR 1.36 per 10 years, 95% CI 1.19\u20131.56). The image pipeline produced per-disc candidate biomarkers across 316 studies. Conclusions: Free-text lumbar MRI reports encode a recognizable but heterogeneous degenerative vocabulary, sufficient for cohort construction yet inconsistent for quantitative grading; the text-derived severity is a noisy proxy. The reproducible image analysis pipeline yields candidate biomarkers whose formal validation against an adjudicated radiologist Pfirrmann reference standard is the explicit, pre-specified next step.<\/jats:p>","DOI":"10.3390\/diagnostics16182951","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T09:34:01Z","timestamp":1789378441000},"page":"2951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transparent, Reproducible Text-Based Phenotyping of Lumbar Intervertebral Disc Degeneration from 500 Consecutive MRI Reports, with a Pre-Specified Image Analysis Framework"],"prefix":"10.3390","volume":"16","author":[{"given":"Ahmed Ibrahim","family":"Haidar","sequence":"first","affiliation":[{"name":"MRI Unit, Radiology Department, Imam Abdulrahman Al Faisal Hospital, First Health Cluster, Riyadh 14723, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9195-0105","authenticated-orcid":false,"given":"Mohammed","family":"Emam","sequence":"additional","affiliation":[{"name":"Department of Radiologic Technology, College of Applied Medical Sciences, Qassim University, Buraidah 51452, Saudi Arabia"},{"name":"Physics Department, Faculty of Science, Al-Azhar University, Nasr City, Cairo 11884, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulwahab Ali","family":"Aljubran","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, Buraidah Central Hospital, Qassim Health Cluster, Buraidah 52361, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khudhair Mohammed","family":"Alkhudhair","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, King Saud Medical City (KSMC), First Health Cluster, Riyadh 12746, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mosa Mohammed","family":"Alassiri","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, King Saud Medical City (KSMC), First Health Cluster, Riyadh 12746, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4034-5487","authenticated-orcid":false,"given":"Basim Sallah","family":"Almutairi","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, King Saud Medical City (KSMC), First Health Cluster, Riyadh 12746, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saleh Abdullah","family":"Alsulaiman","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, Imam Abdulrahman Al Faisal Hospital, First Health Cluster, Riyadh 14723, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal Ibrahim","family":"Altamimi","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, Imam Abdulrahman Al Faisal Hospital, First Health Cluster, Riyadh 14723, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mashael Mubarak","family":"Alkahtani","sequence":"additional","affiliation":[{"name":"Department of Adult Neurology and Epilepsy, Imam Abdulrahman Al Faisal Hospital, First Health Cluster, Riyadh 14723, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim Ahmed","family":"Alyami","sequence":"additional","affiliation":[{"name":"MRI Unit, Radiology Department, King Saud Medical City (KSMC), First Health Cluster, Riyadh 12746, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khadijah Mohammed","family":"Mobaraki","sequence":"additional","affiliation":[{"name":"Al-Amal Medical Complex, Jazan 86622, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e316","DOI":"10.1016\/S2665-9913(23)00098-X","article-title":"Global, regional, and national burden of low back pain, 1990\u20132020, its attributable risk factors, and projections to 2050: A systematic analysis of the Global Burden of Disease Study 2021","volume":"5","author":"Ferreira","year":"2023","journal-title":"Lancet Rheumatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1136\/annrheumdis-2013-204428","article-title":"The global burden of low back pain: Estimates from the Global Burden of Disease 2010 study","volume":"73","author":"Hoy","year":"2014","journal-title":"Ann. Rheum. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.3174\/ajnr.A4498","article-title":"MRI findings of disc degeneration are more prevalent in adults with low back pain than in asymptomatic controls: A systematic review and meta-analysis","volume":"36","author":"Brinjikji","year":"2015","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.spinee.2015.09.060","article-title":"Modic changes of the lumbar spine: Prevalence, risk factors, and association with disc degeneration and low back pain in a large-scale population-based cohort","volume":"16","author":"Mok","year":"2016","journal-title":"Spine J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1097\/00007632-200109010-00011","article-title":"Magnetic resonance classification of lumbar intervertebral disc degeneration","volume":"26","author":"Pfirrmann","year":"2001","journal-title":"Spine"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1007\/s00586-016-4438-z","article-title":"The Pfirrmann classification of lumbar intervertebral disc degeneration: An independent inter- and intra-observer agreement assessment","volume":"25","author":"Urrutia","year":"2016","journal-title":"Eur. Spine J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"E708","DOI":"10.1097\/BRS.0b013e31815a59a0","article-title":"Modified Pfirrmann grading system for lumbar intervertebral disc degeneration","volume":"32","author":"Griffith","year":"2007","journal-title":"Spine"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e1321","DOI":"10.1002\/jsp2.1321","article-title":"Tracing the disc: The novel qualitative morphometric MRI based disc degeneration classification system","volume":"7","author":"Soydan","year":"2024","journal-title":"JOR Spine"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.jacr.2019.12.026","article-title":"Deep learning for natural language processing in radiology\u2014Fundamentals and a systematic review","volume":"17","author":"Sorin","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1007\/s00330-023-09977-3","article-title":"Information extraction from weakly structured radiological reports with natural language queries","volume":"34","author":"Dada","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1038\/s41746-024-01219-0","article-title":"A scoping review of large language model based approaches for information extraction from radiology reports","volume":"7","author":"Reichenpfader","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1007\/s00586-023-07604-9","article-title":"ISSLS PRIZE in Clinical Science 2023: Comparison of degenerative MRI features of the intervertebral disc between those with and without chronic low back pain. An exploratory study of two large female populations using automated annotation","volume":"32","author":"Jamaludin","year":"2023","journal-title":"Eur. Spine J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1148\/radiol.2021204289","article-title":"Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI","volume":"300","author":"Hallinan","year":"2021","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1097\/BRS.0000000000004572","article-title":"External validation of SpineNet, an open-source deep learning model for grading lumbar disk degeneration MRI features, using the Northern Finland Birth Cohort 1966","volume":"48","author":"McSweeney","year":"2023","journal-title":"Spine"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1007\/s00586-025-08900-2","article-title":"Comparison of lumbar disc degeneration grading between deep learning model SpineNet and radiologist: A longitudinal study with a 14-year follow-up","volume":"34","author":"Murto","year":"2025","journal-title":"Eur. Spine J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e1353","DOI":"10.1002\/jsp2.1353","article-title":"Automated magnetic resonance imaging\u2013based grading of the lumbar intervertebral disc and facet joints","volume":"7","author":"Nikpasand","year":"2024","journal-title":"JOR Spine"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1177\/21925682241299332","article-title":"Lumbar spinal stenosis grading in multiple level magnetic resonance imaging using deep convolutional neural networks","volume":"15","author":"Won","year":"2025","journal-title":"Glob. Spine J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1016\/j.ijrobp.2018.05.053","article-title":"Repeatability and reproducibility of radiomic features: A systematic review","volume":"102","author":"Traverso","year":"2018","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1007\/s00330-020-07284-9","article-title":"How can we combat multicenter variability in MR radiomics? Validation of a correction procedure","volume":"31","author":"Orlhac","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"172","DOI":"10.2967\/jnumed.121.262464","article-title":"A guide to ComBat harmonization of imaging biomarkers in multicenter studies","volume":"63","author":"Orlhac","year":"2022","journal-title":"J. Nucl. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mali, S.A., Ibrahim, A., Woodruff, H.C., Andrearczyk, V., M\u00fcller, H., Primakov, S., Salahuddin, Z., Chatterjee, A., and Lambin, P. (2021). Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization methods. J. Pers. Med., 11.","DOI":"10.3390\/jpm11090842"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1007\/s00261-023-04006-9","article-title":"Structured reporting in radiology enables epidemiological analysis through data mining: Urolithiasis as a use case","volume":"48","author":"Jorg","year":"2023","journal-title":"Abdom. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4091","DOI":"10.1007\/s12094-025-03939-5","article-title":"Insights into radiomics: A comprehensive review for beginners","volume":"27","author":"Mariotti","year":"2025","journal-title":"Clin. Transl. Oncol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Flaiban, E., Orhan, K., Gon\u00e7alves, B.C., Lopes, S.L.P.D.C., and Costa, A.L.F. (2025). Radiomics in Action: Multimodal Synergies for Imaging Biomarkers. Bioengineering, 12.","DOI":"10.3390\/bioengineering12111139"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e078378","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","year":"2024","journal-title":"BMJ"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1016\/j.spinee.2014.04.022","article-title":"Lumbar disc nomenclature: Version 2.0. Recommendations of the combined task forces of the North American Spine Society, the American Society of Spine Radiology and the American Society of Neuroradiology","volume":"14","author":"Fardon","year":"2014","journal-title":"Spine J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"152","DOI":"10.5395\/rde.2017.42.2.152","article-title":"Statistical notes for clinical researchers: Chi-squared test and Fisher\u2019s exact test","volume":"42","author":"Kim","year":"2017","journal-title":"Restor. Dent. Endod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1037\/h0026256","article-title":"Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit","volume":"70","author":"Cohen","year":"1968","journal-title":"Psychol. Bull."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1001\/jamaoncol.2019.1800","article-title":"Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports","volume":"5","author":"Kehl","year":"2019","journal-title":"JAMA Oncol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e230364","DOI":"10.1148\/ryai.230364","article-title":"Performance of an open-source large language model in extracting information from free-text radiology reports","volume":"6","author":"Geay","year":"2024","journal-title":"Radiol. Artif. Intell."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/2951\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:21:24Z","timestamp":1789705284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/2951"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,12]]},"references-count":34,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16182951"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16182951","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,12]]}}}