Quantitative Imaging and Medical Physics Team, Medical University of Vienna, Vienna, Austria
Quantitative Imaging and Medical Physics Team, Medical University of Vienna, Vienna, Austria.
J Nucl Med. 2024 Jul 1;65(7):995-997. doi: 10.2967/jnumed.123.267183.
The integration of automated whole-body tumor segmentation using F-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.
使用 F-FDG PET/CT 图像进行全自动全身肿瘤分割的整合代表了肿瘤诊断学的重大转变，提高了肿瘤负担评估的准确性和效率。这篇社论探讨了人工智能推动下的自动化发展，特别是通过深度学习技术。我们强调了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。此外，我们还评论了数据多样性、验证需求和监管障碍等挑战。代谢肿瘤体积和总病变糖酵解作为癌症管理的重要指标，突显了这种评估的重要性。尽管取得了有希望的进展，但我们呼吁学术界、临床用户和行业加强合作，以更好地实现自动化分割的临床效益，从而帮助简化工作流程并改善肿瘤学患者的治疗效果。
