000162370 001__ 162370
000162370 005__ 20251017144650.0
000162370 0247_ $$2doi$$a10.1016/j.cmpb.2025.108931
000162370 0248_ $$2sideral$$a144922
000162370 037__ $$aART-2025-144922
000162370 041__ $$aeng
000162370 100__ $$aPérez-Benito, Ángela$$uUniversidad de Zaragoza
000162370 245__ $$aIn-silico patient-specific modelling of prostate cancer: Predicting PSA dynamics and treatment response
000162370 260__ $$c2025
000162370 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162370 5203_ $$aBackground and Objective:Prostate cancer remains a significant global health concern, with treatment response varying among patients. Radiotherapy, often combined with hormone therapy, is a key treatment approach, but predicting individual outcomes remains challenging. Computational models have emerged as valuable tools to simulate tumour behaviour and optimise treatment strategies. This study presents a patient-specific computational model designed to predict tumour response by associating Prostate-Specific Antigen (PSA) dynamics with tumour biological behaviour under therapy.
Methods:The model integrates patient-specific clinical data and imaging biomarkers from a retrospective study, including apparent diffusion coefficient values from diffusion-weighted imaging to represent tumour cellularity and perfusion parameters from dynamic contrast-enhanced MRI to characterise vascular properties. Clinical data from five patients undergoing radiotherapy, hormone therapy, or combination therapy are used for model development and validation. Due to the limited availability of patient data, PSA is the only parameter used for calibration and validation. One patient is used for calibration, while six serve for validation. Model performance is evaluated by calculating the mean absolute error (MAE) between simulated and observed PSA values post-treatment. The model also estimates tumour shrinkage, though this cannot be directly validated. To assess predictive capacity, two patients are selected for additional analysis simulating different treatment strategies and their impact on PSA dynamics and tumour shrinkage.
Results:The model successfully replicates PSA trends, with MAE values of 0.1, 0.08, 0.23, 0.14, 0.11 and 0.15 ng/mL and RMSE of 0.18, 0.15, 0.24, 0.18, 0.12 and 0.15 ng/mL for the six validation patients, with Patient C showing the closest correspondence to clinical data (MAE = 0.08). Overall, the MAE ranges from 0.08 ng/mL to 0.23 ng/mL, indicating the model’s ability to approximate treatment response. In the two selected patients, simulated treatment variations result in distinct PSA dynamics and estimated tumour shrinkage, highlighting interpatient variability in treatment response.
Conclusions:This computational model provides a predictive framework for assessing prostate cancer treatment response based on patient-specific PSA dynamics and imaging biomarkers. While tumour shrinkage estimates cannot be validated, the model offers insights into treatment-induced PSA fluctuations. The findings support the potential of in-silico tools in personalised medicine, aiding clinical decision-making by evaluating different therapeutic strategies. Further validation with larger datasets is necessary for clinical integration.
000162370 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T50-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709
000162370 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000162370 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162370 700__ $$aGaliana-Bordera, Adrián
000162370 700__ $$aMartínez-Gironés, Pedro-Miguel
000162370 700__ $$aUrbanos, Gemma
000162370 700__ $$aNogué Infante, Anna
000162370 700__ $$0(orcid)0000-0002-1878-8997$$aGómez-Benito, María José$$uUniversidad de Zaragoza
000162370 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, María Ángeles$$uUniversidad de Zaragoza
000162370 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000162370 773__ $$g270 (2025), 108931 [16 pp.]$$pComput. methods programs biomed.$$tComputer Methods and Programs in Biomedicine$$x0169-2607
000162370 8564_ $$s2776210$$uhttps://zaguan.unizar.es/record/162370/files/texto_completo.pdf$$yVersión publicada
000162370 8564_ $$s2495213$$uhttps://zaguan.unizar.es/record/162370/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162370 909CO $$ooai:zaguan.unizar.es:162370$$particulos$$pdriver
000162370 951__ $$a2025-10-17-14:35:56
000162370 980__ $$aARTICLE