arXiv 2607.039312026-07-07eess.IVcs.CVphysics.med-ph

GLOW-FDG：用于¹⁸F-FDG-PET/CT的广义癌症病灶全身分割模型

GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$F-FDG-PET/CT

Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland（苏黎世大学放射肿瘤科，苏黎世大学，苏黎世，瑞士）；Faculty of Medicine, University of Zurich, Zurich, Switzerland（苏黎世大学医学院，苏黎世，瑞士）；Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany（德国癌症研究中心（DKFZ）医学图像计算部，海德堡，德国）；Department of Radiation Oncology, Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, France（洛林癌症研究所放射肿瘤科，法国 Vandœuvre-lès-Nancy）；Department of Thoracic Surgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland（苏黎世大学胸外科科，苏黎世大学，苏黎世，瑞士）；ETOP IBCSG Partners Foundation, Bern, Switzerland（ETOP IBCSG合作伙伴基金会，瑞士伯恩）；Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland（苏黎世大学核医学科，苏黎世大学，苏黎世，瑞士）

Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś, Yannick Kirchhoff, Benjamin Hamm, Sebastian M. Christ, Nicolas Martz, Isabelle Opitz, Rolf Stahel, Martin H… 展开作者

Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś, Yannick Kirchhoff, Benjamin Hamm, Sebastian M. Christ, Nicolas Martz, Isabelle Opitz, Rolf Stahel, Martin Hüllner, Matthias Guckenberger, Klaus Maier-Hein, Stephanie Tanadini-Lang

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AI总结：

研究针对¹⁸F-FDG-PET/CT全身癌症病灶分割，提出开源人工智能模型GLOW-FDG，经多类型训练及外部评估，在检测中性能优于基准模型，可提取定量成像生物标志物。

摘要：

全身氟脱氧葡萄糖正电子发射断层扫描结合计算机断层扫描在癌症治疗中广泛应用，但手动病灶描绘缓慢、主观且难以扩展。我们提出了GLOW-FDG，这是一种用于氟脱氧葡萄糖正电子发射断层扫描和计算机断层扫描中全身癌症病灶分割的开源人工智能模型。该模型在跨越多种癌症类型的1563次扫描上进行训练，并在来自独立机构的185次外部扫描上进行评估。在乳腺癌、非转移性和寡转移性肺癌、头颈癌和转移性黑色素瘤中，GLOW-FDG在病灶检测方面始终优于公开可用的基准模型，同时减少了假阳性并保持了强大的分割准确性。总肿瘤负荷和总病灶糖酵解的量化在各队列中都很稳健，并且性能接近专家放射肿瘤学家之间观察到的变异性。这些结果支持GLOW-FDG作为全身成像中自动癌症分割和定量成像生物标志物提取的通用工具。

英文摘要：

Whole-body fluorodeoxyglucose positron emission tomography combined with computed tomography is widely used in cancer care, but manual lesion delineation is slow, subjective, and difficult to scale. We present GLOW-FDG, an open-source artificial intelligence model for whole-body cancer lesion segmentation in fluorodeoxyglucose positron emission tomography and computed tomography. The model was trained on 1,563 scans spanning multiple cancer types and evaluated on 185 external scans from independent institutions. Across breast cancer, nonmetastatic and oligometastatic lung cancer, head and neck cancer, and metastatic melanoma, GLOW-FDG consistently outperformed publicly available benchmark models in lesion detection, while reducing false positives and maintaining strong segmentation accuracy. Quantification of total tumor burden and total lesion glycolysis was robust across cohorts, and performance approached the variability observed between expert radiation oncologists. These results support GLOW-FDG as a generalizable tool for automated cancer segmentation and quantitative imaging biomarker extraction in whole-body imaging.

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