000151650 001__ 151650
000151650 005__ 20250319155217.0
000151650 0247_ $$2doi$$a10.1038/s41598-025-91146-1
000151650 0248_ $$2sideral$$a143261
000151650 037__ $$aART-2025-143261
000151650 041__ $$aeng
000151650 100__ $$aRamírez-Torres, Erick E.
000151650 245__ $$aProper likelihood functions for parameter estimation in S-shaped models of unperturbed tumor growth
000151650 260__ $$c2025
000151650 5060_ $$aAccess copy available to the general public$$fUnrestricted
000151650 5203_ $$aThe analysis of unperturbed tumor growth kinetics, particularly the estimation of parameters for S-shaped equations used to describe growth, requires an appropriate likelihood function that accounts for the increasing error in solid tumor measurements as tumor size grows over time. This study aims to propose suitable likelihood functions for parameter estimation in S-shaped models of unperturbed tumor growth. Five different likelihood functions are evaluated and compared using three Bayesian criteria (the Bayesian Information Criterion, Deviance Information Criterion, and Bayes Factor) along with hypothesis tests on residuals. These functions are applied to fit data from unperturbed Ehrlich, fibrosarcoma Sa-37, and F3II tumors using the Gompertz equation, though they are generalizable to other S-shaped growth models for solid tumors or analogous systems (e.g., microorganisms, viruses). Results indicate that error models with tumor volume-dependent dispersion outperform standard constant-variance models in capturing the variability of tumor measurements, particularly the Thres model, which provides interpretable parameters for tumor growth. Additionally, constant-variance models, such as those assuming a normal error distribution, remain valuable as complementary benchmarks in analysis. It is concluded that models incorporating volume-dependent dispersion are preferred for accurate and clinically meaningful tumor growth modeling, whereas constant-dispersion models serve as useful complements for consistency and historical comparability.
000151650 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-141385NB-I00
000151650 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000151650 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000151650 700__ $$aSelva Castañeda, Antonio R.
000151650 700__ $$0(orcid)0000-0002-4238-3228$$aRández, Luis$$uUniversidad de Zaragoza
000151650 700__ $$aSisson, Scott A.
000151650 700__ $$aCabrales, Luis E. Bergues
000151650 700__ $$0(orcid)0000-0001-6120-4427$$aMontijano, Juan I.$$uUniversidad de Zaragoza
000151650 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000151650 773__ $$g15, 1 (2025), 6598 [16 pp.]$$pSci. rep. (Nat. Publ. Group)$$tScientific reports (Nature Publishing Group)$$x2045-2322
000151650 8564_ $$s7359668$$uhttps://zaguan.unizar.es/record/151650/files/texto_completo.pdf$$yVersión publicada
000151650 8564_ $$s2555207$$uhttps://zaguan.unizar.es/record/151650/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000151650 909CO $$ooai:zaguan.unizar.es:151650$$particulos$$pdriver
000151650 951__ $$a2025-03-19-14:20:03
000151650 980__ $$aARTICLE