Frontiers in Oncology (Jun 2022)

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

  • Zsombor Ritter,
  • László Papp,
  • Katalin Zámbó,
  • Zoltán Tóth,
  • Dániel Dezső,
  • Dániel Sándor Veres,
  • Domokos Máthé,
  • Domokos Máthé,
  • Ferenc Budán,
  • Ferenc Budán,
  • Éva Karádi,
  • Anett Balikó,
  • László Pajor,
  • Árpád Szomor,
  • Erzsébet Schmidt,
  • Hussain Alizadeh

DOI
https://doi.org/10.3389/fonc.2022.820136
Journal volume & issue
Vol. 12

Abstract

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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.

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