000147911 001__ 147911
000147911 005__ 20250923084442.0
000147911 0247_ $$2doi$$a10.3390/cancers16234024
000147911 0248_ $$2sideral$$a141441
000147911 037__ $$aART-2024-141441
000147911 041__ $$aeng
000147911 100__ $$aMillastre, Judith$$uUniversidad de Zaragoza
000147911 245__ $$aThermal Liquid Biopsy: A Promising Tool for the Differential Diagnosis of Pancreatic Cystic Lesions and Malignancy Detection
000147911 260__ $$c2024
000147911 5060_ $$aAccess copy available to the general public$$fUnrestricted
000147911 5203_ $$aBackground/Objectives: Mucinous epithelial pancreatic cystic lesions (PCLs) are premalignant lesions readily detectable through imaging techniques such as multidetector computed tomography, magnetic resonance imaging, and endoscopic ultrasound (EUS). However, distinguishing these from other PCLs with lower or no malignant potential, and the early identification of those undergoing malignant transformation, remains a diagnostic challenge. Current methods, including biochemical markers in intracystic fluid (ICF) and genomic studies, offer some assistance but are not always reliable or accessible. Thermal liquid biopsy (TLB) is a novel diagnostic tool that examines the thermal profile (thermogram) of biological samples, reflecting their response to heat and thereby revealing characteristics of their overall composition or disease-induced alterations. Methods: In this retrospective study, a total of 35 ICF samples (49% mucinous) obtained via EUS-FNA (fine needle aspiration) were analyzed using TLB. Thermogram data were utilized to develop predictive models for differential diagnosis between mucinous and non-mucinous PCLs or malignancy detection through machine learning algorithms. Results: Two classification models were developed: TLB1 (“mucinous” vs. “non-mucinous” PCLs) and TLB2 (“benign mucinous” vs. “malignant mucinous” PCLs). The TLB1 model demonstrated a sensitivity of 92% and a negative predictive value of 86%, with an area under the curve (AUC) of 0.79 (0.59–0.99), indicating good discriminative ability between the two groups. The TLB2 model exhibited excellent predictive capability, with an AUC of 1.00. Conclusions: TLB analysis of PCLs is a promising tool that could significantly enhance the differential diagnosis of PCLs, enabling the efficient identification of mucinous lesions and even those undergoing malignant transformation.
000147911 536__ $$9info:eu-repo/grantAgreement/ES/DGA/B25-23R$$9info:eu-repo/grantAgreement/ES/DGA/E45-23R$$9info:eu-repo/grantAgreement/ES/ISCIII-ERDF-ESF/PI18-00349-Investing in your future$$9info:eu-repo/grantAgreement/ES/ISCIII-ERDF-ESF/PI21-00394$$9info:eu-repo/grantAgreement/ES/MICINN/AEI/PID2021-127296OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PRTR-C17.I1
000147911 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000147911 590__ $$a4.4$$b2024
000147911 592__ $$a1.462$$b2024
000147911 591__ $$aONCOLOGY$$b85 / 326 = 0.261$$c2024$$dQ2$$eT1
000147911 593__ $$aOncology$$c2024$$dQ1
000147911 593__ $$aCancer Research$$c2024$$dQ2
000147911 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000147911 700__ $$aHermoso-Durán, Sonia$$uUniversidad de Zaragoza
000147911 700__ $$aSolórzano, María Ortiz de
000147911 700__ $$aFraunhoffer, Nicolas
000147911 700__ $$aGarcía-Rayado, Guillermo$$uUniversidad de Zaragoza
000147911 700__ $$0(orcid)0000-0002-1232-6310$$aVega, Sonia
000147911 700__ $$aBujanda, Luis
000147911 700__ $$0(orcid)0000-0001-7466-3876$$aSostres, Carlos$$uUniversidad de Zaragoza
000147911 700__ $$0(orcid)0000-0001-5932-2889$$aLanas, Ángel$$uUniversidad de Zaragoza
000147911 700__ $$0(orcid)0000-0001-5702-4538$$aVelázquez-Campoy, Adrián$$uUniversidad de Zaragoza
000147911 700__ $$0(orcid)0000-0001-5664-1729$$aAbian, Olga$$uUniversidad de Zaragoza
000147911 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000147911 7102_ $$11002$$2060$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Bioquímica y Biolog.Mole.
000147911 773__ $$g16, 23 (2024), 4024 [16 pp.]$$pCancers$$tCancers$$x2072-6694
000147911 8564_ $$s1843101$$uhttps://zaguan.unizar.es/record/147911/files/texto_completo.pdf$$yVersión publicada
000147911 8564_ $$s2888143$$uhttps://zaguan.unizar.es/record/147911/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000147911 909CO $$ooai:zaguan.unizar.es:147911$$particulos$$pdriver
000147911 951__ $$a2025-09-22-14:51:49
000147911 980__ $$aARTICLE