{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:22:12Z","timestamp":1783441332619,"version":"3.54.6"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: Lung cancer causes more deaths than any other malignancy worldwide, accounting for 2.2 million new cases and 1.8 million deaths in 2020. Extracting structured clinical knowledge from unstructured French-language oncology records remains methodologically unresolved in Tunisian and Francophone healthcare systems, where validated natural language processing tools do not yet exist. This study examined the effectiveness of transformer-based named-entity recognition for automated clinical annotation of Tunisian lung cancer reports. Aim: The study aimed to (i) establish performance baselines for four transformer-based models on a publicly available thoracic radiology dataset, (ii) evaluate five models, including a French biomedical specialist, on a newly constructed Tunisian clinical corpus, and (iii) demonstrate prototype deployment feasibility for structured clinical decision support. Methods: An initial comparative study evaluated BERT, RoBERTa, BioClinicalBERT, and CamemBERT using the official RadGraph dataset partitions, which natively comprise a total of 600 annotated thoracic radiology reports distributed across a standardized 80\/10\/10 split. Subsequently, five models were evaluated on 200 manually annotated diagnostic reports from Mami Pneumo-Phthisiology Hospital, Tunis. For the Tunisian corpus, a five-fold cross-validation approach was implemented to ensure robust performance estimation, followed by final evaluation on a dedicated hold-out test set. All models were trained for a maximum of 10 epochs, with a learning rate of 5 \u00d7 10\u22125 and a batch size of 16. Results: Based on the initial comparative study on the RadGraph dataset, where RoBERTa was the top performer and achieved the highest F1-score of 0.873 (precision: 0.869, recall: 0.877), we evaluated its specialized biomedical variant, DR-BERT, on our Tunisian clinical dataset. DR-BERT demonstrated strong generalization on the hold-out test set with an F1-score of 0.824, outperforming the baseline RoBERTa (test F1: 0.791) and showing competitive performance relative to multilingual BERT (0.843 \u00b1 0.005 in five-fold cross-validation). A prototype interface generated structured clinical summaries encompassing prior conditions, imaging modalities, and TNM staging. Conclusion: Language- and domain-adapted transformer models effectively extract structured clinical entities from French-language Tunisian lung cancer reports. DR-BERT\u2019s superior generalization on unseen data confirms that biomedical pretraining in the target language is a key driver of robust performance in specialized French oncology text. This work establishes foundational infrastructure for NLP-driven oncology data management in Tunisia and comparable Francophone settings.<\/jats:p>","DOI":"10.3390\/bioengineering13070724","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:02:56Z","timestamp":1783436576000},"page":"724","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer-Based Clinical Annotation of Lung Cancer Reports: A Benchmark and Fine-Tuning Study on a Novel Tunisian Corpus"],"prefix":"10.3390","volume":"13","author":[{"given":"Ranim","family":"Yahyaoui","sequence":"first","affiliation":[{"name":"Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis (ISTMT), University of Tunis El Manar, Tunis 1002, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8091-1856","authenticated-orcid":false,"given":"Ismail","family":"Dergaa","sequence":"additional","affiliation":[{"name":"High Institute of Sport and Physical Education of Ksar Said, University of Manouba, Manouba 2010, Tunisia"},{"name":"Physical Activity Research Unit, Sport and Health (UR18JS01), National Observatory of Sports, Tunis 1003, Tunisia"},{"name":"High Institute of Sport and Physical Education of Kef, University of Jendouba, Jendouba 7100, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean No\u00ebl","family":"Nikiema","sequence":"additional","affiliation":[{"name":"Department of Management, Evaluation and Health Policy, School of Public Health, University of Montreal, Montreal, QC H3T 1J4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2214-4667","authenticated-orcid":false,"given":"Halil \u0130brahim","family":"Ceylan","sequence":"additional","affiliation":[{"name":"Physical Education of Sports Teaching Department, Faculty of Sports Sciences, Atat\u00fcrk University, Erzurum 25240, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola Luigi","family":"Bragazzi","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, Saarland University, 66123 Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7386-7987","authenticated-orcid":false,"given":"Saoussen","family":"Hantous-Zannad","sequence":"additional","affiliation":[{"name":"Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis 1007, Tunisia"},{"name":"Department of Radiology, Abderrahmane Mami Hospital, Ariana 2035, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanene Boussi","family":"Rahmouni","sequence":"additional","affiliation":[{"name":"Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis (ISTMT), University of Tunis El Manar, Tunis 1002, Tunisia"},{"name":"The Computer Science Research Center, The University of the West of England, Bristol BS16 1QY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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