Sun Zhuxu, Yang Tianshuo, Ding Chongyang, Shi Yuye, Cheng Luyi, Jia Qingshen, Tao Weijing
Department of Nuclear Medicine, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Cancer Imaging. 2024 Dec 18;24(1):168. doi: 10.1186/s40644-024-00810-8.
Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous hematological malignancy resulting in a range of outcomes, and the early prediction of these outcomes has important implications for patient management. Clinical scoring systems provide the most commonly used prognostic evaluation criteria, and the value of genetic testing has also been confirmed by in-depth research on molecular typing. [F]-fluorodeoxyglucose positron emission tomography / computed tomography ([F]FDG PET/CT) is an invaluable tool for predicting DLBCL progression. Conventional baseline image-based parameters and machine learning models have been used in prognostic FDG PET/CT studies of DLBCL; however, numerous studies have shown that combinations of baseline clinical scoring systems, molecular subtypes, and parameters and models based on baseline FDG PET/CT image may provide better predictions of patient outcomes and aid clinical decision-making in patients with DLBCL.
弥漫性大B细胞淋巴瘤（DLBCL）是一种高度异质性的血液系统恶性肿瘤，会导致一系列不同的结果，而对这些结果的早期预测对患者管理具有重要意义。临床评分系统提供了最常用的预后评估标准，基因检测的价值也已通过对分子分型的深入研究得到证实。[F] - 氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（[F]FDG PET/CT）是预测DLBCL进展的一项宝贵工具。传统的基于基线图像的参数和机器学习模型已用于DLBCL的预后FDG PET/CT研究；然而，大量研究表明，基线临床评分系统、分子亚型以及基于基线FDG PET/CT图像的参数和模型相结合，可能会更好地预测患者预后，并有助于DLBCL患者的临床决策。
