Background: PSMA PET radiomics is a promising tool for primary prostate cancer (PCa) characterisation. However, small single-centre studies and lack of external validation hinder definitive conclusions on the potential of PSMA PET radiomics in the initial workup of PCa. We aimed to validate a radiomics signature in a larger internal cohort and in an external cohort from a separate centre.Methods: One hundred and twenty-seven PCa patients were retrospectively enrolled across two independent hospitals. The first centre (IRCCS San Raffaele Scientific Institute, Centre 1) contributed 62 [68Ga]Ga-PSMA-11 PET scans, 20 patients classified as low-grade (ISUP grade < 4), and 42 as high-grade (ISUP grade ≥ 4). The second centre (Stanford University Hospital, Centre 2) provided 65 [68Ga]Ga-PSMA-11 PET scans, and 49 low-grade and 16 high-grade patients. A radiomics model previously generated in Centre 1 was tested on the two cohorts separately and afterward on the entire dataset. Then, we evaluated whether the radiomics features selected in the previous investigation could generalise to new data. Several machine learning (ML) models underwent training and testing using 100-fold Monte Carlo cross-validation, independently at both Centre 1 and Centre 2, with a 70–30% train–test split. Additionally, models were trained in one centre and tested in the other, and vice versa. Furthermore, data from both centres were combined for training and testing using Monte Carlo cross-validation. Finally, a new radiomics signature built on this bicentric dataset was proposed. Several performance metrics were computed.Results:The previously generated radiomics signature resulted in an area under the receiver operating characteristic curve (AUC) of 80.4% when tested on Centre 1, while it generalised poorly to Centre 2, where it reached an AUC of 62.7%. When the whole cohort was considered, AUC was 72.5%. Similarly, new ML models trained on the previously selected features yielded, at best, an AUC of 80.9% for Centre 1 and performed at chance for Centre 2 (AUC of 49.3%). A new signature built on this bicentric dataset reached, at best, an average AUC of 91.4% in the test set.Conclusions:The satisfying performance of radiomics models when used in the original development settings, paired with the poor performance otherwise observed, emphasises the need to consider centre-specific factors and dataset characteristics when developing radiomics models. Combining radiomics datasets is a viable strategy to reduce such centre-specific biases, but external validation is still needed.
背景：PSMA PET影像组学是原发性前列腺癌（PCa）特征分析的一种有前景的工具。然而，小规模单中心研究和缺乏外部验证阻碍了对PSMA PET影像组学在PCa初始诊疗中潜力的明确结论。本研究旨在在一个更大的内部队列和一个来自独立中心的外部队列中验证一个影像组学特征。 方法：回顾性纳入来自两家独立医院的127名PCa患者。第一中心（IRCCS圣拉斐尔科学研究所，中心1）提供了62例[68Ga]Ga-PSMA-11 PET扫描，其中20名患者被分类为低级别（ISUP分级 < 4），42名为高级别（ISUP分级 ≥ 4）。第二中心（斯坦福大学医院，中心2）提供了65例[68Ga]Ga-PSMA-11 PET扫描，其中49名为低级别，16名为高级别患者。先前在中心1生成的影像组学模型分别在两个队列以及整个数据集上进行测试。随后，我们评估了先前研究中选定的影像组学特征是否能推广到新数据。多个机器学习（ML）模型在中心1和中心2分别独立地使用100次蒙特卡洛交叉验证进行训练和测试，训练集-测试集分割比例为70%-30%。此外，模型在一个中心训练，在另一个中心测试，反之亦然。并且，将两个中心的数据合并，使用蒙特卡洛交叉验证进行训练和测试。最后，提出了一个基于此双中心数据集构建的新影像组学特征。计算了多项性能指标。 结果：先前生成的影像组学特征在中心1测试时，其受试者工作特征曲线下面积（AUC）为80.4%，而在推广到中心2时表现不佳，AUC仅为62.7%。当考虑整个队列时，AUC为72.5%。类似地，基于先前选定特征训练的新ML模型，在中心1的最佳AUC为80.9%，而在中心2的表现接近随机水平（AUC为49.3%）。基于此双中心数据集构建的新特征，在测试集中最佳平均AUC达到了91.4%。 结论：影像组学模型在原始开发环境中使用时性能令人满意，而在其他情况下观察到的性能较差，这强调了在开发影像组学模型时需要考虑中心特异性因素和数据集特征。合并影像组学数据集是减少此类中心特异性偏倚的可行策略，但仍需进行外部验证。
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