人工智能增强超快速PSMA-PET扫描前列腺癌分期

IF 8.6 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
David Kersting, Katarzyna Borys, Alina Küper, Moon Kim, Johannes Haubold, Tsepo Goerttler, Lale Umutlu, Pedro Fragoso Costa, Jens Kleesiek, Christoph Rischpler, Felix Nensa, Ken Herrmann, Wolfgang P. Fendler, Manuel Weber, René Hosch, Robert Seifert
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引用次数: 0

摘要

sepsma -PET是前列腺癌患者的参考标准检查,但即使使用最近引入的数字PET探测器,使用标准视场扫描仪进行图像采集仍在20分钟的范围内。由于对PSMA-PET的需求不断增长,这可能会导致检查时段的限制。超快PSMA-PET可以提高吞吐量,但代价是图像质量差。本文的目的是评估人工智能增强的超快速PSMA-PET对前列腺癌患者分期的准确性。方法共纳入357组全身[68Ga]Ga-PSMA-11 PET数据集。患者接受了两次数字PET扫描,一次是标准速度,一次是超快速度(表速:0.6-1.2 mm/s vs. 50 mm/s)。利用286个数据集对改进的pix2pixHD生成对抗网络进行了训练,并对剩余的71个数据集进行了评估。将超快PSMA-PET和ai增强超快PET分别与PROMISE V2.0提出的miTNM区域的参考标准PET进行分期精度比较。结果与未增强的图像数据相比,人工智能网络显著提高了大多数miTNM区域的视觉图像质量和检出率(T: 69.6% vs. 43.5%, p < 0.05;N: 46.3%对27.8%,p < 0.01;M1a 64.4% vs. 47.5%, p < 0.01;M1b: 85.7% vs. 72.1%, p < 0.01)。然而,M1c的改善并不显著(42.9 vs 28.6%, p > 0.05)。与检测到的病变相比,遗漏病变的SUVmax和病变大小更小(例如N: 9.5 vs 26.5 SUVmax;4 vs. 10 mm)。超快速PET与参比标准PET在所有miTNM区域的病变SUVmax值均有显著差异,而ai增强PET与参比标准PET之间仅在t区域存在差异。结论基于人工智能的图像增强,图像质量和区域检测率平均提高了17.9%。由于合成PET对小病变和低摄取病变的敏感性有限,潜在的临床应用案例可能是对接受PSMA放射配体治疗的高肿瘤体积和PSMA摄取患者的疾病监测。远端转移检出率无明显提高。这表明需要更多的训练数据来确保对于出现频率较低的病变也有可靠的结果。未来对加速PSMA-PET的研究似乎是有必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI

Purpose

PSMA-PET is a reference standard examination for patients with prostate cancer, but even using recently introduced digital PET detectors image acquisition with standard field-of-view scanners is still in the range of 20 min. This may cause limited access to examination slots because of the growing demand for PSMA-PET. Ultra-fast PSMA-PET may enhance throughput but comes at the cost of poor image quality. The aim of this manuscript is to evaluate the accuracy of AI-enhanced ultra-fast PSMA-PET for staging of patients with prostate cancer.

Methods

A total number of 357 whole-body [68Ga]Ga-PSMA-11 PET datasets were included. Patients underwent two digital PET scans, one at standard and one at ultra-fast speed (table speed: 0.6–1.2 mm/s vs. 50 mm/s). A modified pix2pixHD generative adversarial network to enhance the ultra-fast images was trained with 286 datasets and evaluated with the remaining 71 datasets. The staging accuracy of ultra-fast PSMA-PET and AI-enhanced ultra-fast PET was compared with the reference standard PET separately for miTNM regions proposed by PROMISE V2.0.

Results

The AI-network significantly improved the visual image quality and detection rate in most miTNM regions compared with the non-enhanced image data (T: 69.6% vs. 43.5%, p < 0.05; N: 46.3% vs. 27.8%, p < 0.01; M1a 64.4% vs. 47.5%, p < 0.01; M1b: 85.7% vs. 72.1%, p < 0.01). However, improvement was not significant for the M1c category (42.9 vs. 28.6%, p > 0.05). Missed lesions had a smaller SUVmax and lesion size compared with detected lesions (exemplary for N: 9.5 vs. 26.5 SUVmax; 4 vs. 10 mm). SUVmax values of lesions were significantly different in all miTNM regions between the ultra-fast and reference standard PET, but only in the T-region between the AI-enhanced and reference standard PET.

Conclusion

The AI-based image enhancement improved image quality and region detection rates by a mean of 17.9%. As the sensitivity of synthetic PET for small and low-uptake lesions was limited, a potential clinical use case could be disease monitoring in patients with high tumor volume and PSMA uptake undergoing PSMA radioligand therapy. The improvement in detection rate of distant metastases was not significant. This indicates that more training data is needed to ensure robust results also for lesions that have lower appearance frequency. Future studies on accelerated PSMA-PET seem warranted.

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来源期刊
CiteScore
15.60
自引率
9.90%
发文量
392
审稿时长
3 months
期刊介绍: The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.
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