这项研究的目的是探讨[68Ga]Ga-PSMA-11 PET放射学在预测原发性前列腺癌（PCa）手术后国际泌尿学病理学会（PSISUP）分级中的作用。该回顾性研究包括47例在进行根治性前列腺切除术前接受[68Ga]Ga-PSMA-11 PET检查的患者。全前列腺在PET图像上手动勾画，并提取了103个符合图像生物标记标准化倡议（IBSI）的放射学特征（RF）。然后，使用最小冗余最大相关性算法选择特征，并使用4个最相关的RF的组合对12个放射学机器学习模型进行训练，以预测PSISUP分级：ISUP≥4 vs ISUP＜4。 机器学习模型通过五倍重复交叉验证进行验证，并生成了两个对照模型来评估我们的发现是否是虚假关联。对于所有生成的模型，收集了平衡精度（bACC），并使用Kruskal-Wallis和Mann-Whitney测试进行比较。还报告了敏感性、特异性和阳性和阴性预测值，以提供模型性能的完整概述。最佳表现模型的预测与活检时的ISUP分级进行了比较。在前列腺切除术后，9/47例患者的ISUP分级升级，导致bACC = 85.9％，SN = 71.9％ ，SP = 100％，PPV = 100％和NPV = 62.5％，而表现最佳的放射学模型产生的bACC = 87.6％，SN = 88.6％，SP = 86.7％，PPV = 94％和NPV = 82.5％。训练至少使用2个RF（GLSZM-Zone Entropy和Shape-Least Axis Length）的所有放射学模型均优于对照模型。相反，在训练2个或更多RF的放射学模型中未发现显着差异（Mann-Whitney p> 0.05）。这些发现支持[68Ga]Ga-PSMA-11 PET放射学在准确和非侵入性地预测PSISUP分级方面的作用。 ©2023。作者（s）在Springer-Verlag GmbH Germany的独家许可下，属于Springer Nature。

The aim of this study is to investigate the role of [68Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (PSISUP) grade in primary prostate cancer (PCa).This retrospective study included 47 PCa patients who underwent [68Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of PSISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal-Wallis and Mann-Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models' performance. The predictions of the best performing model were compared against ISUP grade at biopsy.ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM-Zone Entropy and Shape-Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann-Whitney p > 0.05).These findings support the role of [68Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PSISUP grade.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.