Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma

被引:3
作者
Ahrari, Shamimeh [1 ,2 ]
Zaragori, Timothee [1 ,2 ]
Zinsz, Adeline [3 ]
Oster, Julien [1 ]
Imbert, Laetitia [1 ,2 ,3 ]
Verger, Antoine [1 ,2 ,3 ]
机构
[1] Univ Lorraine, Inst Natl Sante & Rech Med, Imagerie Adaptat Diagnost & Intervent, U1254, F-54000 Nancy, France
[2] Univ Lorraine, Nancyclotep Imaging Platform, F-54000 Nancy, France
[3] Univ Nancy, Ctr Hosp Reg, Dept Nucl Med, F-54000 Nancy, France
关键词
CENTRAL-NERVOUS-SYSTEM; FEATURES; CLASSIFICATION; GLIOBLASTOMA; RECURRENCE; DISCOVERY; CONSENSUS; CRITERIA; OUTCOMES; TUMORS;
D O I
10.1038/s41598-024-53693-x
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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-[F-18]-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.
引用
收藏
页数:12
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