GLOW-FDG可自动分割全身肿瘤，准确率超现有模型。
GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$F-FDG-PET/CT
- 基于1563例数据训练，支持多癌种全身病变分割
- 在185例外部数据上检测准确率更高，假阳性更低
- 适合临床量化肿瘤负荷，辅助放疗与治疗评估
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诊疗中广泛应用，但手动勾画病灶耗时长、主观性强且难以规模化。本文提出GLOW-FDG，一个开源的人工智能模型，用于氟脱氧葡萄糖正电子发射断层扫描与计算机断层扫描中的全身肿瘤病灶分割。该模型在涵盖多种癌症类型的1,563例扫描数据上训练，并在来自独立机构的185例外部数据上进行评估。在乳腺癌、非转移性和寡转移性肺癌、头颈部癌及转移性黑色素瘤中，GLOW-FDG在病灶检测方面持续优于公开基准模型，同时减少假阳性并保持高分割精度。肿瘤总负荷和总病灶糖酵解量的量化结果在各队列中均表现稳健，性能接近不同专家放射肿瘤医师间的差异水平。这些结果表明，GLOW-FDG是可用于全身成像中自动化肿瘤分割与定量影像生物标志物提取的通用工具。
原文摘要 · Abstract (English)
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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