Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Neuro Oncol. 2013 May;15(5):515-34. doi: 10.1093/neuonc/nos307. Epub 2013 Jan 16.
Differentiating treatment-induced necrosis from tumor recurrence is a central challenge in neuro-oncology. These 2 very different outcomes after brain tumor treatment often appear similarly on routine follow-up imaging studies. They may even manifest with similar clinical symptoms, further confounding an already difficult process for physicians attempting to characterize a new contrast-enhancing lesion appearing on a patient's follow-up imaging. Distinguishing treatment necrosis from tumor recurrence is crucial for diagnosis and treatment planning, and therefore, much effort has been put forth to develop noninvasive methods to differentiate between these disparate outcomes. In this article, we review the latest developments and key findings from research studies exploring the efficacy of structural and functional imaging modalities for differentiating treatment necrosis from tumor recurrence. We discuss the possibility of computational approaches to investigate the usefulness of fine-grained imaging characteristics that are difficult to observe through visual inspection of images. We also propose a flexible treatment-planning algorithm that incorporates advanced functional imaging techniques when indicated by the patient's routine follow-up images and clinical condition.
区分治疗引起的坏死与肿瘤复发是神经肿瘤学的一个核心挑战。这两种截然不同的脑肿瘤治疗结果在常规随访影像学研究中通常表现相似。它们甚至可能表现出相似的临床症状，这使得本来就很困难的医生对患者随访影像学上出现的新增强病变进行特征描述的过程更加复杂。区分治疗性坏死与肿瘤复发对于诊断和治疗计划至关重要，因此，人们已经投入了大量精力来开发非侵入性方法来区分这些不同的结果。在本文中，我们回顾了探索结构和功能成像方式区分治疗性坏死与肿瘤复发的最新研究进展和关键发现。我们讨论了计算方法的可能性，以研究通过图像视觉检查难以观察到的精细成像特征的有用性。我们还提出了一种灵活的治疗计划算法，当患者的常规随访图像和临床状况提示时，该算法会纳入先进的功能成像技术。
