评估[18F]-DCFPyL PET/MRI放射组学技术在治疗前未治疗前列腺癌(PCa)患者中预测病理分级的诊断准确性。本次回顾性分析共纳入了两项前瞻性临床试验中经确诊或疑似PCa的患者（n = 105），其中进行了[18F]-DCFPyL PET/MRI检查。图像标记标准化倡议(IBSI)指南被采用来提取PET/MRI检查后处理后的图像组学特征。PET/MRI检测到的病变部位的组织学结果被当作参考标准。病理组织学分级被分为ISUP GG 1-2和ISUP GG ≥ 3两个分类。不同的单模态模型被定义用于特征提取，包括PET和MRI衍生的放射组学特征。临床模型包括年龄、PSA和病变的PROMISE分类。单一模型以及它们的组合被生成以计算它们的性能。交叉验证方法被用来评估模型的内部有效性。 所有组学模型的表现均优于临床模型。最好的分级预测模型是PET + ADC + T2w放射组学特征的组合，显示出灵敏度、特异度、准确度和AUC分别为0.85、0.83、0.84和0.85。MRI衍生的(ADC + T2w)特征的灵敏度、特异度、准确度和AUC分别为0.88、0.78、0.83和0.84。PET衍生的特征的灵敏度、特异度、准确度和AUC分别为0.83、0.68、0.76和0.79。基线临床模型的表现分别为0.73、0.44、0.60和0.58。将临床模型添加到最佳组学模型并没有提高诊断表现。根据交叉验证方法，MRI和PET/MRI组学模型的准确率达到0.80 (AUC = 0.79)，而临床模型的准确率为0.60 (AUC = 0.60)。 综合来看，[18F]-DCFPyL PET/MRI组学模型是表现最佳的模型，并且比临床模型更有效，可用于非侵入性风险分层的医学应用。需要进一步的前瞻性研究来验证这一方法的可重复性和临床实用性。© 2023作者授权Springer-Verlag GmbH Germany part of Springer Nature使用。

To evaluate the diagnostic accuracy of [18F]-DCFPyL PET/MRI radiomics for the prediction of pathological grade group in prostate cancer (PCa) in therapy-naïve patients.Patients with confirmed or suspected PCa, who underwent [18F]-DCFPyL PET/MRI (n = 105), were included in this retrospective analysis of two prospective clinical trials. Radiomic features were extracted from the segmented volumes following the image biomarker standardization initiative (IBSI) guidelines. Histopathology obtained from systematic and targeted biopsies of the PET/MRI-detected lesions was the reference standard. Histopathology patterns were dichotomized as ISUP GG 1-2 vs. ISUP GG ≥ 3 categories. Different single-modality models were defined for feature extraction, including PET- and MRI-derived radiomic features. The clinical model included age, PSA, and lesions' PROMISE classification. Single models, as well as different combinations of them, were generated to calculate their performances. A cross-validation approach was used to evaluate the internal validity of the models.All radiomic models outperformed the clinical models. The best model for grade group prediction was the combination of PET + ADC + T2w radiomic features, showing sensitivity, specificity, accuracy, and AUC of 0.85, 0.83, 0.84, and 0.85, respectively. The MRI-derived (ADC + T2w) features showed sensitivity, specificity, accuracy, and AUC of 0.88, 0.78, 0.83, and 0.84, respectively. PET-derived features showed 0.83, 0.68, 0.76, and 0.79, respectively. The baseline clinical model showed 0.73, 0.44, 0.60, and 0.58, respectively. The addition of the clinical model to the best radiomic model did not improve the diagnostic performance. The performances of MRI and PET/MRI radiomic models as per the cross-validation scheme yielded an accuracy of 0.80 (AUC = 0.79), whereas clinical models presented an accuracy of 0.60 (AUC = 0.60).The combined [18F]-DCFPyL PET/MRI radiomic model was the best-performing model and outperformed the clinical model for pathological grade group prediction, indicating a complementary value of the hybrid PET/MRI model for non-invasive risk stratification of PCa. Further prospective studies are required to confirm the reproducibility and clinical utility of this approach.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.