Mirshahvalad Seyed Ali, Basso Dias Adriano, Ortega Claudia, Abreu Gomez Jorge Andres, Krishna Satheesh, Perlis Nathan, Berlin Alejandro, van der Kwast Theodorus, Jhaveri Kartik, Ghai Sangeet, Metser Ur, Santiago Anna Theresa, Veit-Haibach Patrick
Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Br J Radiol. 2025 Jan 23. doi: 10.1093/bjr/tqaf014.
To evaluate 18F-DCFPyL-PET/MRI whole-gland-derived radiomics for detecting clinically significant (cs) prostate cancer (PCa) and predicting metastasis.
Therapy-naïve PCa patients who underwent 18F-DCFPyL PET/MRI were included. Whole-prostate-segmentation was performed. Feature extraction from each modality was done. The selection of potential variables was made through regularized binomial logistic regression. The oversampled training data were used to train binomial logistic regression for each outcome. The estimates of the models were calculated, and the mean accuracy was reported. The trained models were assessed on the test data for comparative evaluation of performance.
A total of 103 patients (mean age=65;mean PSA=23.4) were studied. Among them, 89 had csPCa, and 20 had metastatic disease. There were 5 radiomics variables selected for ISUP-GG≥2 from T2w, ADC and PET. To detect N1, five radiomics variables were selected from the T2w and PET. For M1, four radiomics variables were selected from T2w and ADC. Regarding the performance of models for the prediction of csPCa, the imaging-based hybrid model (T2w+PET) provided the highest AUC(0.98). The performance of N1 models showed the highest AUC(0.80) for T2w+PET. To predict M1, the T2w+ADC model showed the highest AUC(0.93).
Whole-gland PET/MRI-radiomics may provide a reliable model to predict csPCa. Also, acceptable performance was reached for predicting metastatic disease in our limited population. Our findings may support the value of whole-gland radiomics for non-invasive csPCa detection and prediction of metastatic disease.
Whole-gland PET/MRI-radiomics, a less operator-dependent segmentation method, can be potentially used for treatment personalization in PCa patients.
评估18F-DCFPyL-PET/MRI全腺体衍生的放射组学用于检测临床显著性（cs）前列腺癌（PCa）及预测转移情况。
纳入未接受过治疗的接受18F-DCFPyL PET/MRI检查的PCa患者。进行全前列腺分割。从每种模态中提取特征。通过正则化二项逻辑回归进行潜在变量的选择。使用过采样的训练数据针对每个结局训练二项逻辑回归。计算模型的估计值，并报告平均准确率。在测试数据上评估训练好的模型以进行性能的比较评估。
共研究了103例患者（平均年龄 = 65岁；平均前列腺特异性抗原 = 23.4）。其中，89例患有csPCa，20例患有转移性疾病。从T2加权成像（T2w）、表观扩散系数（ADC）和PET中为国际泌尿病理学会分级（ISUP-GG）≥2选择了5个放射组学变量。为检测N1，从T2w和PET中选择了5个放射组学变量。对于M1，从T2w和ADC中选择了4个放射组学变量。关于预测csPCa的模型性能，基于影像的混合模型（T2w + PET）提供了最高的曲线下面积（AUC）（0.98）。N1模型的性能显示T2w + PET的AUC最高（0.80）。为预测M1，T2w + ADC模型显示出最高的AUC（0.93）。
全腺体PET/MRI放射组学可能提供一个可靠的模型来预测csPCa。此外，在我们有限的人群中预测转移性疾病也达到了可接受的性能。我们的研究结果可能支持全腺体放射组学在非侵入性csPCa检测和转移性疾病预测方面的价值。
全腺体PET/MRI放射组学，一种较少依赖操作者的分割方法，可能潜在地用于PCa患者的治疗个性化。
