This manuscript aims to address the diagnostic challenges of mediastinal bulky lymphomas with the baseline value of 18F-FDG PET/CT metabolic, volumetric and texture parame- ters, also relying on machine learning techniques, in patients with grey zone lymphoma, primary diffuse large B-cell lymphoma of the mediastinum and classical Hodgkin lymphoma. Different types of histology demonstrated several baseline 18F-FDG PET/CT radiomics parameters that were significantly different from one another, suggesting the possibility of identifying potential histological heterogeneity and aggressive transformation. Moreover, using radiomics-based imaging biomarkers, machine learning techniques offer a solution for separating not completely disjoint histological types. To date, the gold standard for diagnosis is biopsy, but machine learning methods could be combined with radiomics to build a histological representation of mediastinal bulky masses that is able to suc- cessfully identify different types of lymphomas. Finally, this preliminary study supports the potential of metabolic texture analyses as future imaging biomarkers, with a growing role in clinical diagnosis.

Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques / Abenavoli, Elisabetta Maria; Barbetti, Matteo; Linguanti, Flavia; Mungai, Francesco; Nassi, Luca; Puccini, Benedetta; Romano, Ilaria; Sordi, Benedetta; Santi, Raffaella; Passeri, Alessandro; Sciagrà, Roberto; Talamonti, Cinzia; Cistaro, Angelina; Vannucchi, Alessandro Maria; Berti, Valentina. - In: CANCERS. - ISSN 2072-6694. - ELETTRONICO. - 15:(2023), pp. 0-0. [10.3390/cancers15071931]

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

Abenavoli, Elisabetta Maria;Barbetti, Matteo;Linguanti, Flavia;Mungai, Francesco;Puccini, Benedetta;Romano, Ilaria;Sordi, Benedetta;Santi, Raffaella;Passeri, Alessandro;Sciagrà, Roberto;Talamonti, Cinzia;Vannucchi, Alessandro Maria;Berti, Valentina
2023

Abstract

This manuscript aims to address the diagnostic challenges of mediastinal bulky lymphomas with the baseline value of 18F-FDG PET/CT metabolic, volumetric and texture parame- ters, also relying on machine learning techniques, in patients with grey zone lymphoma, primary diffuse large B-cell lymphoma of the mediastinum and classical Hodgkin lymphoma. Different types of histology demonstrated several baseline 18F-FDG PET/CT radiomics parameters that were significantly different from one another, suggesting the possibility of identifying potential histological heterogeneity and aggressive transformation. Moreover, using radiomics-based imaging biomarkers, machine learning techniques offer a solution for separating not completely disjoint histological types. To date, the gold standard for diagnosis is biopsy, but machine learning methods could be combined with radiomics to build a histological representation of mediastinal bulky masses that is able to suc- cessfully identify different types of lymphomas. Finally, this preliminary study supports the potential of metabolic texture analyses as future imaging biomarkers, with a growing role in clinical diagnosis.
2023
15
0
0
Abenavoli, Elisabetta Maria; Barbetti, Matteo; Linguanti, Flavia; Mungai, Francesco; Nassi, Luca; Puccini, Benedetta; Romano, Ilaria; Sordi, Benedetta; Santi, Raffaella; Passeri, Alessandro; Sciagrà, Roberto; Talamonti, Cinzia; Cistaro, Angelina; Vannucchi, Alessandro Maria; Berti, Valentina
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1302711
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