| Autor: |
Hasanabadi S; Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran 1983969411, Iran., Aghamiri SMR; Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran 1983969411, Iran., Abin AA; Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran 1983969411, Iran., Abdollahi H; Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada.; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada., Arabi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland., Zaidi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.; Department of Nuclear Medicine, University of Southern Denmark, 500 Odense, Denmark.; University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary. |
| Abstrakt: |
Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18 F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18 F-FDG PET/CT in the management of lymphoma patients. |
