本研究的目的是系统评估计算机视觉中阈值算法对前列腺特异性膜抗原正电子发射断层扫描（PSMA-TV）衍生的肿瘤体积的定量分析在晚期前列腺癌患者中的影响。结果验证了这种分析与晚期前列腺癌患者总生存率的预测有关。本次研究中包括2018年1月至2020年12月接受[177Lu]Lu-PSMA-617放射性治疗的78位患者。使用放射性核素治疗前采集的[68Ga]Ga-PSMA-11正电子发射断层扫描（PET）图像来分析阈值算法。首先，使用先前评估的专有软件方案对所有PET图像进行半自动分析作为基准方法。随后，应用五种基于直方图的阈值方法和两种计算机视觉中广泛应用的本地自适应阈值方法来量化分子肿瘤体积。将得到的全身分子肿瘤体积与基线方法的统计相关性以及它们在标准鬼影扫描中的表现验证其对晚期前列腺癌患者总生存率的预测。使用不同阈值方法量化的整体PSMA-TV显示出与基准方法的高正相关性，其中基于广义直方图阈值（GHT）（Pearson r（r），p值（p）：r = 0.977，p < 0.001）和Sauvola阈值（r = 0.974，p < 0.001）的相关性最高，而Multiotsu （r = 0.877，p < 0.001）和Yen阈值方法（r = 0.878，p < 0.001）的相关性最低。所有患者的中位生存时间为9.87个月（95％CI[9.3至10.13]）。依据中位数整体PSMA-TV进行分层，低肿瘤负荷患者组的中位生存时间从11.8到13.5个月，而高肿瘤负荷患者组的中位生存时间为6.5到6.6个月。低肿瘤负荷的患者组在九种阈值方法中的八种（图2）中具有更高的生存概率（p <0.00625），包括SUVmax50（p = 0.0038），SUV≥3（p = 0.0034），Multiotsu（p = 0.0015），Yen （p = 0.0015），Niblack（p = 0.001），Sauvola（p = 0.0001），Otsu（p = 0.0053）和Li阈值（p = 0.0053）。计算机视觉中通常使用的阈值方法是半自动量化整体PSMA-TV的有前途的工具。提出的算法驱动的阈值策略比具有预定义值的阈值方法更少任意性且更不易偏见，有可能提高将整体PSMA-TV作为成像生物标志物的应用。©2023年。作者。

The aim of this study was to systematically evaluate the effect of thresholding algorithms used in computer vision for the quantification of prostate-specific membrane antigen positron emission tomography (PET) derived tumor volume (PSMA-TV) in patients with advanced prostate cancer. The results were validated with respect to the prognostication of overall survival in patients with advanced-stage prostate cancer.A total of 78 patients who underwent [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to December 2020 were retrospectively included in this study. [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide therapy, were used for the analysis of thresholding algorithms. All PET images were first analyzed semi-automatically using a pre-evaluated, proprietary software solution as the baseline method. Subsequently, five histogram-based thresholding methods and two local adaptive thresholding methods that are well established in computer vision were applied to quantify molecular tumor volume. The resulting whole-body molecular tumor volumes were validated with respect to the prognostication of overall patient survival as well as their statistical correlation to the baseline methods and their performance on standardized phantom scans.The whole-body PSMA-TVs, quantified using different thresholding methods, demonstrate a high positive correlation with the baseline methods. We observed the highest correlation with generalized histogram thresholding (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and Sauvola thresholding (r = 0.974, p < 0.001) and the lowest correlation with Multiotsu (r = 0.877, p < 0.001) and Yen thresholding methods (r = 0.878, p < 0.001). The median survival time of all patients was 9.87 months (95% CI [9.3 to 10.13]). Stratification by median whole-body PSMA-TV resulted in a median survival time from 11.8 to 13.5 months for the patient group with lower tumor burden and 6.5 to 6.6 months for the patient group with higher tumor burden. The patient group with lower tumor burden had significantly higher probability of survival (p < 0.00625) in eight out of nine thresholding methods (Fig. 2); those methods were SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p = 0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p = 0.0001), Otsu (p = 0.0053), and Li thresholding (p = 0.0053).Thresholding methods commonly used in computer vision are promising tools for the semiautomatic quantification of whole-body PSMA-TV in [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven thresholding strategy is less arbitrary and less prone to biases than thresholding with predefined values, potentially improving the application of whole-body PSMA-TV as an imaging biomarker.© 2023. The Author(s).