18F-DCFPyL PET/MRI Radiomics for Intraprostatic Prostate Cancer Detection and Metastases Prediction Using Whole-Gland Segmentation.

IF 1.8 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Seyed Ali Mirshahvalad, Adriano Basso Dias, Claudia Ortega, Jorge Andres Abreu Gomez, Satheesh Krishna, Nathan Perlis, Alejandro Berlin, Theodorus van der Kwast, Kartik Jhaveri, Sangeet Ghai, Ur Metser, Anna Theresa Santiago, Patrick Veit-Haibach
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引用次数: 0

Abstract

Objectives: To evaluate 18F-DCFPyL-PET/MRI whole-gland-derived radiomics for detecting clinically significant (cs) prostate cancer (PCa) and predicting metastasis.

Methods: 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.

Results: 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).

Conclusions: 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.

Advances in knowledge: Whole-gland PET/MRI-radiomics, a less operator-dependent segmentation method, can be potentially used for treatment personalization in PCa patients.

18F-DCFPyL PET/MRI放射组学在前列腺内前列腺癌检测和转移预测中的应用
目的:评价18F-DCFPyL-PET/MRI全腺体放射组学检测临床显著性前列腺癌(PCa)和预测转移的价值。方法:Therapy-naïve接受18F-DCFPyL PET/MRI检查的PCa患者。进行全前列腺分割。对每个模态进行特征提取。通过正则化二项逻辑回归对潜在变量进行选择。使用过采样训练数据对每个结果进行二项逻辑回归训练。计算了模型的估计值,并报告了平均精度。训练后的模型在测试数据上进行评估,对性能进行比较评价。结果:共纳入103例患者,平均年龄65岁,平均PSA=23.4。其中89例患有csPCa, 20例有转移性疾病。从T2w、ADC和PET中选择5个ISUP-GG≥2的放射组学变量。为了检测N1,从T2w和PET中选择了5个放射组学变量。对于M1,从T2w和ADC中选择四个放射组学变量。在预测csPCa的模型中,基于成像的混合模型(T2w+PET)的AUC最高(0.98)。N1模型表现出T2w+PET的最高AUC(0.80)。为了预测M1, T2w+ADC模型的AUC最高(0.93)。结论:全腺体PET/ mri放射组学可为预测csPCa提供可靠的模型。此外,在我们有限的人群中,预测转移性疾病的表现也达到了可接受的水平。我们的研究结果可能支持全腺体放射组学在无创csPCa检测和转移性疾病预测中的价值。知识进展:全腺体PET/ mri放射组学是一种较少依赖操作者的分割方法,可以潜在地用于PCa患者的治疗个性化。
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来源期刊
British Journal of Radiology
British Journal of Radiology 医学-核医学
CiteScore
5.30
自引率
3.80%
发文量
330
审稿时长
2-4 weeks
期刊介绍: BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences. Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896. Quick Facts: - 2015 Impact Factor – 1.840 - Receipt to first decision – average of 6 weeks - Acceptance to online publication – average of 3 weeks - ISSN: 0007-1285 - eISSN: 1748-880X Open Access option
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