Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement
- 1Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
- 2Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
- 3Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
- 4Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy.
- 5Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
- 0Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
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Summary
This summary is machine-generated.Identifying patients with refractory Hodgkin Lymphoma (HL) is crucial. This study found that combining PET scan texture analysis (HISTO_EntropyPET) with clinical staging (GHSG stage) accurately predicts treatment refractoriness in newly diagnosed classical HL.
Area Of Science
- Oncology
- Radiology
- Medical Imaging
Background
- Accurate prognostication is vital for newly diagnosed classical Hodgkin Lymphoma (cHL) patients, especially those with mediastinal bulk, to identify high-risk individuals needing intensified therapy.
- Current prognostic models require enhancement with novel parameters to improve the stratification of patients likely to experience refractory disease.
Purpose Of The Study
- To investigate the predictive value of metabolic and texture features from baseline 18F-FDG PET/CT and CECT scans, alongside clinical data, for first-line therapy refractoriness in cHL.
- To identify independent predictors of refractoriness and assess their combined prognostic power.
Main Methods
- Retrospective review of 69 cHL patients who underwent staging PET and CECT scans.
- Extraction of metabolic and texture features using LIFEx software; analysis of clinical data, including GHSG staging.
- Statistical evaluation using ROC, Cox regression, and Kaplan-Meier analyses to determine prognostic significance and independent predictors.
Main Results
- GHSG stage was a significant clinical predictor of refractoriness.
- Second-order texture features from CECT (GLCM, GLRLM) and various PET features (SUVmean, shape, GLCM, GLRLM, NGLDM) showed prognostic power.
- HISTO_EntropyPET from PET scans and GHSG stage emerged as significant independent predictors at multivariate analysis.
Conclusions
- Radiomic features from PET/CT, particularly HISTO_EntropyPET, offer valuable prognostic information for mediastinal bulky cHL.
- Combining HISTO_EntropyPET with GHSG stage significantly improves the prediction of treatment refractoriness, identifying distinct risk groups.
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