000145729 001__ 145729
000145729 005__ 20241122130824.0
000145729 0247_ $$2doi$$a10.3390/biomedicines12092162
000145729 0248_ $$2sideral$$a140684
000145729 037__ $$aART-2024-140684
000145729 041__ $$aeng
000145729 100__ $$0(orcid)0000-0003-1415-146X$$aAznar-Gimeno, Rocío
000145729 245__ $$aGastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer
000145729 260__ $$c2024
000145729 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145729 5203_ $$aBackground/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Methods: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. Results: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical–demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. Conclusions: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.
000145729 536__ $$9info:eu-repo/grantAgreement/ES/DGA/B25-23R$$9info:eu-repo/grantAgreement/ES/ISCIII/FORT23-00028$$9info:eu-repo/grantAgreement/ES/ISCIII/PI22-00537
000145729 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145729 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145729 700__ $$aGarcía-González, María Asunción
000145729 700__ $$aMuñoz-Sierra, Rubén
000145729 700__ $$aCarrera-Lasfuentes, Patricia
000145729 700__ $$aRodrigálvarez-Chamarro, María de la Vega
000145729 700__ $$aGonzález-Muñoz, Carlos
000145729 700__ $$aMeléndez-Estrada, Enrique
000145729 700__ $$0(orcid)0000-0001-5932-2889$$aLanas, Ángel$$uUniversidad de Zaragoza
000145729 700__ $$0(orcid)0000-0003-2755-5500$$aHoyo-Alonso, Rafael del
000145729 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000145729 773__ $$g12, 9 (2024), 2162 [24 pp.]$$tBiomedicines$$x2227-9059
000145729 8564_ $$s2883307$$uhttps://zaguan.unizar.es/record/145729/files/texto_completo.pdf$$yVersión publicada
000145729 8564_ $$s2730365$$uhttps://zaguan.unizar.es/record/145729/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145729 909CO $$ooai:zaguan.unizar.es:145729$$particulos$$pdriver
000145729 951__ $$a2024-11-22-11:53:25
000145729 980__ $$aARTICLE