Wright Cameron, Mäkelä Pietari, Bigot Alexandre, Anttinen Mikael, Boström Peter J, Blanco Sequeiros Roberto
Department of Urology, University of Turku and Turku University Hospital, Turku, Finland.
Department of Diagnostic Radiology, University of Turku and Turku University Hospital, Turku, Finland.
Biomed Eng Lett. 2022 Nov 8;13(1):31-40. doi: 10.1007/s13534-022-00250-y. eCollection 2023 Feb.
The non-perfused volume (NPV) is an important indicator of treatment success immediately after prostate ablation. However, visualization of the NPV first requires an injection of MRI contrast agents into the bloodstream, which has many downsides. Purpose of this study was to develop a deep learning model capable of predicting the NPV immediately after prostate ablation therapy without the need for MRI contrast agents. A modified 2D deep learning UNet model was developed to predict the post-treatment NPV. MRI imaging data from 95 patients who had previously undergone prostate ablation therapy for treatment of localized prostate cancer were used to train, validate, and test the model. Model inputs were T1/T2-weighted and thermometry MRI images, which were always acquired without any MRI contrast agents and prior to the final NPV image on treatment-day. Model output was the predicted NPV. Model accuracy was assessed using the Dice-Similarity Coefficient (DSC) by comparing the predicted to ground truth NPV. A radiologist also performed a qualitative assessment of NPV. Mean (std) DSC score for predicted NPV was 85% ± 8.1% compared to ground truth. Model performance was significantly better for slices with larger prostate radii (> 24 mm) and for whole-gland rather than partial ablation slices. The predicted NPV was indistinguishable from ground truth for 31% of images. Feasibility of predicting NPV using a UNet model without MRI contrast agents was clearly established. If developed further, this could improve patient treatment outcomes and could obviate the need for contrast agents altogether. Three studies were used to populate the data: NCT02766543, NCT03814252 and NCT03350529.
The online version contains supplementary material available at 10.1007/s13534-022-00250-y.
无灌注体积（NPV）是前列腺消融术后治疗成功的重要指标。然而，要可视化NPV首先需要向血液中注射MRI造影剂，这有许多缺点。本研究的目的是开发一种深度学习模型，能够在无需MRI造影剂的情况下预测前列腺消融治疗后的NPV。开发了一种改进的二维深度学习U-Net模型来预测治疗后的NPV。使用95例先前接受前列腺消融治疗以治疗局限性前列腺癌患者的MRI成像数据来训练、验证和测试该模型。模型输入为T1/T2加权和温度MRI图像，这些图像始终在无任何MRI造影剂的情况下且在治疗当天最终NPV图像之前获取。模型输出为预测的NPV。通过将预测的NPV与真实NPV进行比较，使用骰子相似系数（DSC）评估模型准确性。一名放射科医生也对NPV进行了定性评估。与真实情况相比，预测NPV的平均（标准差）DSC评分为85%±8.1%。对于前列腺半径较大（>24mm）的切片以及全腺而非部分消融切片，模型性能明显更好。31%的图像中预测的NPV与真实情况难以区分。明确证实了使用无MRI造影剂的U-Net模型预测NPV的可行性。如果进一步开发，这可以改善患者的治疗结果，并且完全无需造影剂。三项研究用于填充数据：NCT02766543、NCT03814252和NCT03350529。
在线版本包含可在10.1007/s13534-022-00250-y获取的补充材料。
