Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques

  • Elisabetta Maria Abenavoli
    Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences ‘Mario Serio’, University of Florence, 50139 Florence, Italy
  • Matteo Barbetti
    Department of Information Engineering, University of Florence, 50134 Florence, Italy
  • Flavia Linguanti
    Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences ‘Mario Serio’, University of Florence, 50139 Florence, Italy
  • Francesco Mungai
    Department of Radiology, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy
  • Luca Nassi
    Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
  • Benedetta Puccini
    Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
  • Ilaria Romano
    Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
  • Benedetta Sordi
    Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
  • Raffaella Santi
    Pathology Section, Department of Health Sciences, University of Florence, 50139 Florence, Italy
  • Alessandro Passeri
    Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences ‘Mario Serio’, University of Florence, 50139 Florence, Italy
  • Roberto Sciagrà
    Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences ‘Mario Serio’, University of Florence, 50139 Florence, Italy
  • Cinzia Talamonti
    Istituto Nazionale di Fisica Nucleare (INFN), Florence Division, 50019 Sesto Fiorentino, Italy
  • Angelina Cistaro
    Nuclear Medicine Department, Salus Alliance Medical, 16128 Genoa, Italy
  • Alessandro Maria Vannucchi
    Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, 50139 Florence, Italy
  • Valentina Berti
    Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences ‘Mario Serio’, University of Florence, 50139 Florence, Italy

抄録

<jats:p>Background: This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques. Methods: We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features. Results: The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation. Conclusions: Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.</jats:p>

収録刊行物

  • Cancers

    Cancers 15 (7), 1931-, 2023-03-23

    MDPI AG

被引用文献 (1)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ