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Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma
- Source :
- European Journal of Nuclear Medicine and Molecular Imaging
- Publication Year :
- 2019
- Publisher :
- Springer Berlin Heidelberg, 2019.
-
Abstract
- Purpose To determine whether [18F]FDG PET/CT-derived radiomic features alone or in combination with clinical, laboratory and biological parameters are predictive of 2-year progression-free survival (PFS) in patients with mantle cell lymphoma (MCL), and whether they enable outcome prognostication. Methods Included in this retrospective study were 107 treatment-naive MCL patients scheduled to receive CD20 antibody-based immuno(chemo)therapy. Standardized uptake values (SUV), total lesion glycolysis, and 16 co-occurrence matrix radiomic features were extracted from metabolic tumour volumes on pretherapy [18F]FDG PET/CT scans. A multilayer perceptron neural network in combination with logistic regression analyses for feature selection was used for prediction of 2-year PFS. International prognostic indices for MCL (MIPI and MIPI-b) were calculated and combined with the radiomic data. Kaplan–Meier estimates with log-rank tests were used for PFS prognostication. Results SUVmean (OR 1.272, P = 0.013) and Entropy (heterogeneity of glucose metabolism; OR 1.131, P = 0.027) were significantly predictive of 2-year PFS: median areas under the curve were 0.72 based on the two radiomic features alone, and 0.82 with the addition of clinical/laboratory/biological data. Higher SUVmean in combination with higher Entropy (SUVmean >3.55 and entropy >3.5), reflecting high “metabolic risk”, was associated with a poorer prognosis (median PFS 20.3 vs. 39.4 months, HR 2.285, P = 0.005). The best PFS prognostication was achieved using the MIPI-bm (MIPI-b and metabolic risk combined): median PFS 43.2, 38.2 and 20.3 months in the low-risk, intermediate-risk and high-risk groups respectively (P = 0.005). Conclusion In MCL, the [18F]FDG PET/CT-derived radiomic features SUVmean and Entropy may improve prediction of 2-year PFS and PFS prognostication. The best results may be achieved using a combination of metabolic, clinical, laboratory and biological parameters.
- Subjects :
- Oncology
Male
medicine.medical_specialty
Lymphoma
FDG
PET/CT
Lymphoma, Mantle-Cell
Logistic regression
Disease-Free Survival
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Internal medicine
Positron Emission Tomography Computed Tomography
medicine
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
In patient
Aged
Retrospective Studies
PET-CT
business.industry
Metabolic risk
Retrospective cohort study
General Medicine
medicine.disease
Prognosis
Glucose
030220 oncology & carcinogenesis
Mantle cell lymphoma
Original Article
Female
business
Multilayer perceptron neural network
Subjects
Details
- Language :
- English
- ISSN :
- 16197089 and 16197070
- Volume :
- 46
- Issue :
- 13
- Database :
- OpenAIRE
- Journal :
- European Journal of Nuclear Medicine and Molecular Imaging
- Accession number :
- edsair.doi.dedup.....b1fbd9f2681351a43c96d742b0f0e980