Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma.
Sci Rep
; 14(1): 3256, 2024 02 08.
Article
en En
| MEDLINE
| ID: mdl-38332004
ABSTRACT
This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE) C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Glioma
/
Radiómica
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
País de afiliación:
Francia