Chong ling Duan,1,* Lin An,1,* Yong feng Yang,1 Lili Yuan,1 Yandong Zhu,1 Qian Han,1 Hongbing Ma,1 Fei Zhao,1 Qing-qing Yu2 

1Jining No.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China; 2Clinical Research Center, Jining No. 1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Qing-qing Yu, Clinical Research Center, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China, Email yuqingqing_lucky@163.com

Abstract: Lymphomas are a hematopoietic malignancies that encompass over 90 subtypes. Traditionally, they have been categorized into two main groups, non-Hodgkin lymphoma (NHL) and Hodgkin lymphoma (HL). Based on morphology and immunohistochemistry, HL can be classified into nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) and classical HL (cHL). NHL represents the most common form of lymphoma, including more than 50 subtypes, such as mantle cell lymphoma (MCL), follicular lymphoma (FL), marginal zone lymphoma (MZL), and the most common, diffuse large B-cell lymphoma (DLBCL). Medical imaging plays a pivotal role in lymphoma management, with positron emission tomography/computed tomography (PET/CT) serving as an indispensable tool. 2-Deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) PET/CT is extensively utilized in lymphoma management, having demonstrated its value in providing crucial data for precise disease burden quantification, treatment response evaluation, and prognostic assessment. Radiomics is an innovative approach that entails the computer-aided extraction of quantitative, searchable data from medical images and its association with biological and clinical outcomes. The rapid advancement of radiomics research has opened new avenues for cancer diagnosis and therapy. Our findings indicate that artificial intelligence based PET/CT radiomics has demonstrated significant potential in lymphoma diagnosis, subtyping, staging, treatment selection, and survival prognosis assessment, offering clinicians powerful decision-support tools. However, challenges remain, such as the lack of standardized image quality in machine learning applications.

Keywords: lymphomas, PET/CT, radiomics, imaging, artificial intelligence