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European Radiology

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01-10-2020 | Lymphoma | Oncology

Development and validation of an ¹⁸F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma

Authors: Hongxi Wang, Shengnan Zhao, Li Li, Rong Tian

Published in: European Radiology | Issue 10/2020

Abstract

Objectives

To identify an ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) radiomics-based model for predicting progression-free survival (PFS) and overall survival (OS) of nasal-type extranodal natural killer/T cell lymphoma (ENKTL).

Methods

In this retrospective study, a total of 110 ENKTL patients were divided into a training cohort (n = 82) and a validation cohort (n = 28). Forty-one features were extracted from pretreatment PET images of the patients. Least absolute shrinkage and selection operator (LASSO) regression was used to develop the radiomic signatures (R-signatures). A radiomics-based model was built and validated in the two cohorts and compared with a metabolism-based model.

Results

The R-signatures were constructed with moderate predictive ability in the training and validation cohorts (R-signature_PFS: AUC = 0.788 and 0.473; R-signature_OS: AUC = 0.637 and 0.730). For PFS, the radiomics-based model showed better discrimination than the metabolism-based model in the training cohort (C-index = 0.811 vs. 0.751) but poorer discrimination in the validation cohort (C-index = 0.588 vs. 0.693). The calibration of the radiomics-based model was poorer than that of the metabolism-based model (training cohort: p = 0.415 vs. 0.428, validation cohort: p = 0.228 vs. 0.652). For OS, the performance of the radiomics-based model was poorer (training cohort: C-index = 0.818 vs. 0.828, p = 0.853 vs. 0.885; validation cohort: C-index = 0.628 vs. 0.753, p < 0.05 vs. 0.913).

Conclusions

Radiomic features derived from PET images can predict the outcomes of patients with ENKTL, but the performance of the radiomics-based model was inferior to that of the metabolism-based model.

Key Points

• The R-signatures calculated by using ¹⁸ F-FDG PET radiomic features can predict the survival of patients with ENKTL.

• The radiomics-based models integrating the R-signatures and clinical factors achieved good predictive values.

• The performance of the radiomics-based model was inferior to that of the metabolism-based model in the two cohorts.

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Title: Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma
Authors: Hongxi Wang
Shengnan Zhao
Li Li
Rong Tian
Publication date: 01-10-2020
Publisher: Springer Berlin Heidelberg
Keywords: Lymphoma
Positron Emission Tomography
Published in: European Radiology / Issue 10/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06943-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Development and validation of an ¹⁸F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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