Feliciani Giacomo, Celli Monica, Ferroni Fabio, Menghi Enrico, Azzali Irene, Caroli Paola, Matteucci Federica, Barone Domenico, Paganelli Giovanni, Sarnelli Anna
Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", 47014 Meldola, Italy.
Nuclear Medicine and Radiometabolic Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", 47014 Meldola, Italy.
Cancers (Basel). 2022 Apr 8;14(8):1888. doi: 10.3390/cancers14081888.
Prostate cancer (PCa) risk categorization based on clinical/PSA testing results in a substantial number of men being overdiagnosed with indolent, early-stage PCa. Clinically non-significant PCa is characterized as the presence of ISUP grade one, where PCa is found in no more than two prostate biopsy cores.MRI-ADC and [Ga]Ga-PSMA-11 PET have been proposed as tools to predict ISUP grade one patients and consequently reduce overdiagnosis. In this study, Radiomics analysis is applied to MRI-ADC and [Ga]Ga-PSMA-11 PET maps to quantify tumor characteristics and predict histology-proven ISUP grades. ICC was applied with a threshold of 0.6 to assess the features' stability with variations in contouring. Logistic regression predictive models based on imaging features were trained on 31 lesions to differentiate ISUP grade one patients from ISUP two+ patients. The best model based on [Ga]Ga-PSMA-11 PET returned a prediction efficiency of 95% in the training phase and 100% in the test phase whereas the best model based on MRI-ADC had an efficiency of 100% in both phases. Employing both imaging modalities, prediction efficiency was 100% in the training phase and 93% in the test phase. Although our patient cohort was small, it was possible to assess that both imaging modalities add information to the prediction models and show promising results for further investigations.
基于临床/前列腺特异抗原（PSA）检测结果对前列腺癌（PCa）进行风险分类，导致大量男性被过度诊断为惰性早期PCa。临床意义不显著的PCa的特征是国际泌尿病理学会（ISUP）1级，即在不超过两个前列腺活检组织芯中发现PCa。磁共振成像表观扩散系数（MRI-ADC）和镓[Ga]镓前列腺特异性膜抗原（PSMA）-11正电子发射断层显像（PET）已被提议作为预测ISUP 1级患者从而减少过度诊断的工具。在本研究中，将影像组学分析应用于MRI-ADC和[Ga]Ga-PSMA-11 PET图像，以量化肿瘤特征并预测经组织学证实的ISUP分级。应用组内相关系数（ICC），阈值为0.6，以评估特征在轮廓变化时的稳定性。基于影像特征的逻辑回归预测模型在31个病灶上进行训练，以区分ISUP 1级患者和ISUP 2级及以上患者。基于[Ga]Ga-PSMA-11 PET的最佳模型在训练阶段的预测效率为95%，在测试阶段为100%；而基于MRI-ADC的最佳模型在两个阶段的效率均为100%。采用两种成像方式时，训练阶段的预测效率为100%，测试阶段为93%。尽管我们的患者队列规模较小，但可以评估这两种成像方式都为预测模型增加了信息，并显示出进一步研究的良好前景。
