MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features

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dc.contributor.authorDominguez, Ignacio
dc.contributor.authorRios-Ibacache, Odette
dc.contributor.authorCaprile, Paola
dc.contributor.authorGonzalez, Jose
dc.contributor.authorSan Francisco, Ignacio F.
dc.contributor.authorBesa, Cecilia
dc.date.accessioned2025-01-20T20:06:16Z
dc.date.available2025-01-20T20:06:16Z
dc.date.issued2023
dc.description.abstractThis study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 & PLUSMN; 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI-ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS & GE; 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.
dc.fuente.origenWOS
dc.identifier.doi10.3390/diagnostics13172779
dc.identifier.eissn2075-4418
dc.identifier.urihttps://doi.org/10.3390/diagnostics13172779
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91737
dc.identifier.wosidWOS:001061042800001
dc.issue.numero17
dc.language.isoen
dc.revistaDiagnostics
dc.rightsacceso restringido
dc.subjectprostate cancer
dc.subjectGleason score
dc.subjecttexture analysis
dc.subjectbpMRI
dc.subjectmachine learning
dc.subjectradiomics
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleMRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features
dc.typeartículo
dc.volumen13
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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