Geisel School of Medicine at Dartmouth, Hanover, NH.
Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Geisel School of Medicine at Dartmouth, Hanover, NH.
Semin Nucl Med. 2023 May;53(3):426-448. doi: 10.1053/j.semnuclmed.2022.11.003. Epub 2023 Mar 3.
Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.
我们的综述表明，基于人工智能的淋巴瘤全身 FDG-PET/CT 分析可以为临床管理的各个阶段提供信息，包括分期、预后、治疗计划和治疗反应评估。我们强调了神经网络在执行自动图像分割以计算基于 PET 的成像生物标志物（如总代谢肿瘤体积（TMTV））方面的作用的进展。基于人工智能的图像分割方法已经达到了可以半自动实施的水平，只需最少的人工输入，并且接近第二位放射科医生的水平。在区分淋巴瘤与非淋巴瘤 FDG 摄取区域方面，自动分割方法的进展尤为明显，这一直延续到自动分期。除了自动计算 Dmax 等指标外，自动 TMTV 计算器还为无进展生存期的稳健模型提供信息，然后可以将其纳入改进的治疗计划中。
