Purpose: Prostate-specific membrane antigen (PSMA) PET/CT has shown to be more sensitive and accurate than conventional imaging. Visual interpretation of the images causes both intra- and inter-reader disagreement and there is therefore a need for objective methods to analyze the images. The aim of this study was to develop an artificial intelligence (AI) tool for PSMA PET/CT and to evaluate the influence of the tool on inter-reader variability. Approach: We have recently trained AI tools to automatically segment organs, detect tumors, and quantify volume and tracer uptake of tumors in PET/CT. The primary prostate gland tumor, bone metastases, and lymph nodes were analyzed in patients with prostate cancer. These studies were based on non-PSMA targeting PET tracers. In this study an AI tool for PSMA PET/CT was developed based on our previous AI tools. Letting three physicians analyze ten PSMA PET/CT studies first without support from the AI tool and at a second occasion with the support of the AI tool assessed the influence of the tool. A two-sided sign test was used to analyze the number of cases with increased and decreased variability with support of the AI tool. Results: The range between the physicians in prostate tumor total lesion uptake (TLU) decreased for all ten patients with AI support (p=0.002) and decreased in bone metastases TLU for nine patients and increased in one patient (p=0.01). Regarding the number of detected lymph nodes the physicians agreed in on average 72% of the lesions without AI support and this number decreased to 65% with AI support. Conclusions: Physicians supported by an AI tool for automated analysis of PSMA-PET/CT studies showed significantly less inter-reader variability in the quantification of primary prostate tumors and bone metastases than when performing a completely manual analysis. A similar effect was not found for lymph node lesions. The tool may facilitate comparisons of studies from different centers, pooling data within multicenter trials and performing meta-analysis. We invite researchers to apply and evaluate our AI tool for their PSMA PET/CT studies. The AI tool is therefore available upon reasonable request for research purposes at www.recomia.org.

中文翻译：

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使用卷积神经网络自动分析PSMA-PET / CT研究

目的：前列腺特异性膜抗原（PSMA）PET / CT已显示出比常规成像更为灵敏和准确。图像的视觉解释会引起读者内部和读者之间的分歧，因此需要一种客观的方法来分析图像。这项研究的目的是开发一种用于PSMA PET / CT的人工智能（AI）工具，并评估该工具对阅读器间变异性的影响。方法：我们最近训练了AI工具，可以自动分割器官，检测肿瘤并量化PET / CT中肿瘤的体积和示踪剂摄取。对前列腺癌患者的原发性前列腺癌，骨转移和淋巴结进行了分析。这些研究基于非PSMA靶向PET示踪剂。在这项研究中，基于我们以前的AI工具开发了用于PSMA PET / CT的AI工具。让三位医生首先在没有AI工具支持的情况下分析十项PSMA PET / CT研究，然后在AI工具支持下第二次分析该工具的影响。在AI工具的支持下，使用了双向符号测试来分析变异性增加和降低的病例数。结果：医师之间的前列腺肿瘤总病灶摄取（TLU）范围在所有10名AI支持患者中均降低（p = 0.002），在9名患者中骨转移TLU降低，在1名患者中增加（P = 0.01）。关于检测到的淋巴结数目，医生平均同意在没有AI支持的情况下占72％的病灶，而在有AI支持的情况下，这一数字下降到65％。结论：在进行PSMA-PET / CT研究自动分析的AI工具的支持下，与进行完全手动分析相比，对原发性前列腺肿瘤和骨转移瘤进行定量分析的读者间差异明显更少。对于淋巴结病变未发现类似效果。该工具可能有助于比较来自不同中心的研究，在多中心试验中汇总数据并进行荟萃分析。我们邀请研究人员为PSMA PET / CT研究应用和评估我们的AI工具。因此，可出于合理目的出于研究目的在www.recomia.org上使用AI工具。对于淋巴结病变未发现类似效果。该工具可能有助于比较来自不同中心的研究，在多中心试验中汇总数据并进行荟萃分析。我们邀请研究人员为PSMA PET / CT研究应用和评估我们的AI工具。因此，可出于合理目的出于研究目的在www.recomia.org上使用AI工具。对于淋巴结病变未发现类似效果。该工具可能有助于比较来自不同中心的研究，在多中心试验中汇总数据并进行荟萃分析。我们邀请研究人员为PSMA PET / CT研究应用和评估我们的AI工具。因此，可出于合理目的出于研究目的在www.recomia.org上使用AI工具。