145621 20260217205536.0 doi 10.1002/aisy.202400308 sideral 140482 ART-2024-140482 eng Hermoso-Durán, Sonia Universidad de Zaragoza Development of a Machine-Learning Model for Diagnosis of Pancreatic Cancer from Serum Samples Analyzed by Thermal Liquid Biopsy 2024 Access copy available to the general public Unrestricted Pancreatic ductal adenocarcinoma (PDAC) poses a considerable diagnostic and therapeutic challenge due to the lack of specific biomarkers and late diagnosis. Early detection is crucial for improving prognosis, but current techniques are insufficient. An innovative approach based on differential scanning calorimetry (DSC) of blood serum samples, thermal liquid biopsy (TLB), combined with machine‐learning (ML) analysis, may offer a more efficient method for diagnosing PDAC. Serum samples from a cohort of 212 PDAC patients and 184 healthy controls are studied. DSC thermograms are analyzed using ML models. The generated models are built applying algorithms based on penalized regression, resampling, categorization, cross validation, and variable selection. The ML‐based model demonstrates outstanding ability to discriminate between PDAC patients and control subjects, with a sensitivity of 90% and an area under the ROC receiver operating characteristic curve of 0.83 in the training and test groups. Application of the model to an independent validation cohort of 113 PDAC patients confirms its robustness and utility as a diagnosis tool. The application of ML to serum TLB data emerges as a promising  methodology for early diagnosis, representing a significant advance for detecting and managing PDAC, envisaging a minimally invasive and more efficient methodology for identifying biomarkers. info:eu-repo/grantAgreement/EUR/COST-Action/CA21116-TRANSPAN info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 6.1 2024 AUTOMATION & CONTROL SYSTEMS 16 / 89 = 0.18 2024 Q1 T1 ROBOTICS 7 / 48 = 0.146 2024 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 44 / 204 = 0.216 2024 Q1 T1 1.14 2024 Artificial Intelligence 2024 Q1 Computer Vision and Pattern Recognition 2024 Q1 Control and Systems Engineering 2024 Q1 Mechanical Engineering 2024 Q1 Human-Computer Interaction 2024 Q1 Materials Science (miscellaneous) 2024 Q1 Electrical and Electronic Engineering 2024 Q1 4.7 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Fraunhoffer, Nicolas Millastre-Bocos, Judith Universidad de Zaragoza Sanchez-Gracia, Oscar Garrido, Pablo F. Vega, Sonia (orcid)0000-0002-1232-6310 Lanas, Ángel Universidad de Zaragoza (orcid)0000-0001-5932-2889 Iovanna, Juan Velázquez-Campoy, Adrián Universidad de Zaragoza (orcid)0000-0001-5702-4538 Abian, Olga Universidad de Zaragoza (orcid)0000-0001-5664-1729 1007 610 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Medicina 1002 060 Universidad de Zaragoza Dpto. Bioq.Biolog.Mol. Celular Área Bioquímica y Biolog.Mole. 7, 1 (2024), 2400308 [10 pp.] Adv. Intell. Syst. Advanced Intelligent Systems 2640-4567 1320399 http://zaguan.unizar.es/record/145621/files/texto_completo.pdf Versión publicada 2461392 http://zaguan.unizar.es/record/145621/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:145621 articulos driver 2026-02-17-20:34:35 ARTICLE