000145621 001__ 145621
000145621 005__ 20241114112316.0
000145621 0247_ $$2doi$$a10.1002/aisy.202400308
000145621 0248_ $$2sideral$$a140482
000145621 037__ $$aART-2024-140482
000145621 041__ $$aeng
000145621 100__ $$aHermoso-Durán, Sonia$$uUniversidad de Zaragoza
000145621 245__ $$aDevelopment of a Machine-Learning Model for Diagnosis of Pancreatic Cancer from Serum Samples Analyzed by Thermal Liquid Biopsy
000145621 260__ $$c2024
000145621 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145621 5203_ $$aPancreatic ductal adenocarcinoma (PDAC) poses a considerable diagnostic and therapeutic challenge due to the lack of specific biomarkers and late diagnosis. Early detection is crucial for improving prognosis, but current techniques are insufficient. An innovative approach based on differential scanning calorimetry (DSC) of blood serum samples, thermal liquid biopsy (TLB), combined with machine‐learning (ML) analysis, may offer a more efficient method for diagnosing PDAC. Serum samples from a cohort of 212 PDAC patients and 184 healthy controls are studied. DSC thermograms are analyzed using ML models. The generated models are built applying algorithms based on penalized regression, resampling, categorization, cross validation, and variable selection. The ML‐based model demonstrates outstanding ability to discriminate between PDAC patients and control subjects, with a sensitivity of 90% and an area under the ROC receiver operating characteristic curve of 0.83 in the training and test groups. Application of the model to an independent validation cohort of 113 PDAC patients confirms its robustness and utility as a diagnosis tool. The application of ML to serum TLB data emerges as a promising methodology for early diagnosis, representing a significant advance for detecting and managing PDAC, envisaging a minimally invasive and more efficient methodology for identifying biomarkers.
000145621 536__ $$9info:eu-repo/grantAgreement/EUR/COST-Action/CA21116-TRANSPAN
000145621 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145621 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145621 700__ $$aFraunhoffer, Nicolas
000145621 700__ $$aMillastre-Bocos, Judith$$uUniversidad de Zaragoza
000145621 700__ $$aSanchez-Gracia, Oscar
000145621 700__ $$aGarrido, Pablo F.
000145621 700__ $$0(orcid)0000-0002-1232-6310$$aVega, Sonia
000145621 700__ $$0(orcid)0000-0001-5932-2889$$aLanas, Ángel$$uUniversidad de Zaragoza
000145621 700__ $$aIovanna, Juan
000145621 700__ $$0(orcid)0000-0001-5702-4538$$aVelázquez-Campoy, Adrián$$uUniversidad de Zaragoza
000145621 700__ $$0(orcid)0000-0001-5664-1729$$aAbian, Olga$$uUniversidad de Zaragoza
000145621 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000145621 7102_ $$11002$$2060$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Bioquímica y Biolog.Mole.
000145621 773__ $$g(2024), 2400308 [10 pp.]$$pAdv. Intell. Syst.$$tAdvanced Intelligent Systems$$x2640-4567
000145621 8564_ $$s1320399$$uhttps://zaguan.unizar.es/record/145621/files/texto_completo.pdf$$yVersión publicada
000145621 8564_ $$s2461392$$uhttps://zaguan.unizar.es/record/145621/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145621 909CO $$ooai:zaguan.unizar.es:145621$$particulos$$pdriver
000145621 951__ $$a2024-11-14-10:18:05
000145621 980__ $$aARTICLE