Role of Novel Quantitative Imaging Techniques in Hematological Malignancies

• Published in: PET clinics
• Main Authors: Parghane, Rahul V, Basu, Sandip
• Format: Journal Article
• Language: English
• Published: United States 01.10.2024
• Subjects: Dmax; Artificial intelligence (AI); Metabolic tumor volume (MTV); PET/CT; Total metabolic tumor volume (TMTV); Convolutional neural networks (CNNs); Hematological malignancies; Total lesion glycolysis (TLG)
• ISSN: 1879-9809
• DOI: 10.1016/j.cpet.2024.05.008

Hematological malignancies exhibit a widespread distribution, necessitating evaluation of disease activity over the entire body. In clinical practice, visual analysis and semiquantitative parameters are used to assess  F-FDGPET/CT imaging, which solely represents measurements of disease activity from limited area and may not adequately reflect global disease assessment. An efficient method for assessing the global disease burden of hematological malignancies is to employ PET/computed tomography based novel quantitative parameters. In this article, we explored novel quantitative parameters on PET/CT imaging for assessing global disease burden and the potential role of artificial intelligence (AI) to determine these parameters in evaluation of hematological malignancies.