Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters

PurposeFor the identification of high-risk patients in diffuse large B-cell lymphoma (DLBCL), we investigated the prognostic significance of in vivo radiomics derived from baseline [18F]FDG PET/CT and clinical parameters. MethodsPre-treatment [18F]FDG PET/CT scans of 85 patients diagnosed with DLBCL...

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Published in:Frontiers in oncology 2022-06, Vol.12, p.820136-820136
Main Authors: Ritter, Zsombor, Papp, László, Zámbó, Katalin, Tóth, Zoltán, Dezső, Dániel, Veres, Dániel Sándor, Máthé, Domokos, Budán, Ferenc, Karádi, Éva, Balikó, Anett, Pajor, László, Szomor, Árpád, Schmidt, Erzsébet, Alizadeh, Hussain
Format: Article
Language:English
Subjects:
[18F]FDG PET/CT
automated machine learning
DLBCL
Oncology
radiomics
tumor imaging
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Summary:PurposeFor the identification of high-risk patients in diffuse large B-cell lymphoma (DLBCL), we investigated the prognostic significance of in vivo radiomics derived from baseline [18F]FDG PET/CT and clinical parameters. MethodsPre-treatment [18F]FDG PET/CT scans of 85 patients diagnosed with DLBCL were assessed. The scans were carried out in two clinical centers. Two-year event-free survival (EFS) was defined. After delineation of lymphoma lesions, conventional PET parameters and in vivo radiomics were extracted. For 2-year EFS prognosis assessment, the Center 1 dataset was utilized as the training set and underwent automated machine learning analysis. The dataset of Center 2 was utilized as an independent test set to validate the established predictive model built by the dataset of Center 1. ResultsThe automated machine learning analysis of the Center 1 dataset revealed that the most important features for building 2-year EFS are as follows: max diameter, neighbor gray tone difference matrix (NGTDM) busyness, total lesion glycolysis, total metabolic tumor volume, and NGTDM coarseness. The predictive model built on the Center 1 dataset yielded 79% sensitivity, 83% specificity, 69% positive predictive value, 89% negative predictive value, and 0.85 AUC by evaluating the Center 2 dataset. ConclusionBased on our dual-center retrospective analysis, predicting 2-year EFS built on imaging features is feasible by utilizing high-performance automated machine learning.
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2022.820136