PDF(Pubmed)
Abstract：
The integration of automated whole-body tumor segmentation using 18F-FDG PET/CT images represents a pivotal shift in oncologic diagnostics, enhancing the precision and efficiency of tumor burden assessment. This editorial examines the transition toward automation, propelled by advancements in artificial intelligence, notably through deep learning techniques. We highlight the current availability of commercial tools and the academic efforts that have set the stage for these developments. Further, we comment on the challenges of data diversity, validation needs, and regulatory barriers. The role of metabolic tumor volume and total lesion glycolysis as vital metrics in cancer management underscores the significance of this evaluation. Despite promising progress, we call for increased collaboration across academia, clinical users, and industry to better realize the clinical benefits of automated segmentation, thus helping to streamline workflows and improve patient outcomes in oncology.
摘要：
使用18F-FDGPET/CT图像集成自动全身肿瘤分割代表了肿瘤诊断的关键转变。提高肿瘤负荷评估的准确性和效率。这篇社论探讨了向自动化的过渡，在人工智能进步的推动下，特别是通过深度学习技术。我们强调商业工具的当前可用性以及为这些发展奠定基础的学术努力。Further,我们评论数据多样性的挑战，验证需求，和监管障碍。代谢性肿瘤体积和总病变糖酵解作为癌症治疗中的重要指标的作用强调了这种评估的重要性。尽管取得了可喜的进展，我们呼吁加强学术界的合作，临床使用者,和行业更好地实现自动分割的临床优势，从而有助于简化工作流程并改善肿瘤学患者的预后.
