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Baseline 18 F-FDG PET/CT Radiomics in Classical Hodgkin's Lymphoma: The Predictive Role of the Largest and the Hottest Lesions.
- Source :
- Diagnostics (2075-4418); Apr2023, Vol. 13 Issue 8, p1391, 14p
- Publication Year :
- 2023
-
Abstract
- This study investigated the predictive role of baseline <superscript>18</superscript>F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin's lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUV<subscript>max</subscript>. Deauville score at interim PET/CT (DS) and 24-month progression-free-survival (PFS) were recorded. Mann–Whitney test identified the most promising image features (p < 0.05) from both lesions with regards to DS and PFS; all possible radiomic bivariate models were then built through a logistic regression analysis and trained/tested with a cross-fold validation test. The best bivariate models were selected based on their mean area under curve (mAUC). A total of 227 cHL patients were included. The best models for DS prediction had 0.78 ± 0.05 maximum mAUC, with a predominant contribution of Lesion_A features to the combinations. The best models for 24-month PFS prediction reached 0.74 ± 0.12 mAUC and mainly depended on Lesion_B features. bFDG-PET/CT radiomic features from the largest and hottest lesions in patients with cHL may provide relevant information in terms of early response-to-treatment and prognosis, thus representing an earlier and stronger decision-making support for therapeutic strategies. External validations of the proposed model are planned. [ABSTRACT FROM AUTHOR]
- Subjects :
- HODGKIN'S disease
RADIOMICS
LOGISTIC regression analysis
FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 13
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Diagnostics (2075-4418)
- Publication Type :
- Academic Journal
- Accession number :
- 163382715
- Full Text :
- https://doi.org/10.3390/diagnostics13081391