Kersting David, Borys Katarzyna, Küper Alina, Kim Moon, Haubold Johannes, Goerttler Tsepo, Umutlu Lale, Costa Pedro Fragoso, Kleesiek Jens, Rischpler Christoph, Nensa Felix, Herrmann Ken, Fendler Wolfgang P, Weber Manuel, Hosch René, Seifert Robert
Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, Essen, 45147, Germany.
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
Eur J Nucl Med Mol Imaging. 2025 Apr;52(5):1658-1670. doi: 10.1007/s00259-024-07060-7. Epub 2025 Jan 11.
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.
A total number of 357 whole-body [Ga]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.
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.
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.
PSMA-PET是前列腺癌患者的一项参考标准检查,但即使使用最近推出的数字PET探测器,采用标准视野扫描仪进行图像采集仍需20分钟左右。由于对PSMA-PET的需求不断增加,这可能导致检查时段的获取受限。超快速PSMA-PET可提高通量,但代价是图像质量较差。本手稿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。
共纳入357例全身[镓]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,一次是超快速速度(床速:0.6-1.2毫米/秒与50毫米/秒)。使用286个数据集训练了一个改进的pix2pixHD生成对抗网络以增强超快速图像,并用其余71个数据集进行评估。对于PROMISE V2.0提出的miTNM区域,分别将超快速PSMA-PET和人工智能增强的超快速PET的分期准确性与参考标准PET进行比较。
与未增强的图像数据相比,人工智能网络在大多数miTNM区域显著提高了视觉图像质量和检测率(T:69.6%对43.5%,p<0.05;N:46.3%对27.8%,p<0.01;M1a:64.4%对47.5%,p<0.01;M1b:85.7%对72.1%,p<0.01)。然而,M1c类别改善不显著(42.9对28.6%,p>0.05)。与检测到的病变相比,漏诊病变的SUVmax和病变大小较小(以N为例:SUVmax为9.5对26.5;4对10毫米)。超快速PET与参考标准PET之间所有miTNM区域病变的SUVmax值均有显著差异,但人工智能增强PET与参考标准PET之间仅在T区域有显著差异。
基于人工智能的图像增强将图像质量和区域检测率平均提高了17.9%。由于合成PET对小的和低摄取病变的敏感性有限,一个潜在的临床应用案例可能是对肿瘤体积大且PSMA摄取高的患者进行PSMA放射性配体治疗时的疾病监测。远处转移检测率的提高不显著。这表明需要更多的训练数据,以确保对于出现频率较低的病变也能得到可靠的结果。未来关于加速PSMA-PET的研究似乎是有必要的。