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Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. | LitMetric

AI Article Synopsis

  • Differentiating between treatment-induced necrosis and tumor recurrence is a major challenge in neuro-oncology due to their similar appearances on imaging studies and overlapping clinical symptoms.
  • Accurate distinction is critical for effective diagnosis and treatment planning, leading to increased research on noninvasive imaging methods.
  • The article discusses recent advancements in imaging techniques, computational methods for analyzing imaging data, and proposes a treatment-planning algorithm that utilizes advanced imaging information in patient care.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3635510PMC
http://dx.doi.org/10.1093/neuonc/nos307DOI Listing

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