论文标题
基于卷积神经网络的深度学习结构，用于原发性前列腺癌患者PSMA PET图像的肉体内肿瘤轮廓
Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
论文作者
论文摘要
准确描述肉眼内肿瘤体积（GTV）是原发性前列腺癌（PCA）患者治疗方法的先决条件。前列腺特异性膜抗原正电子发射断层扫描（PSMA-PET）在GTV检测中的表现可能优于MRI。但是，视觉GTV描述是观察者间异质性的基础，并且耗时。这项研究的目的是开发一种卷积神经网络（CNN），用于在PSMA-PET中自动分割肉眼内肿瘤（GTV-CNN）。 方法：对来自两个不同机构的152名患者的[68GA] PSMA-PET图像进行了CNN（3D U-NET）的培训，并使用经过验证的技术手动生成训练标签。在两个独立的内部（1：[68GA] PSMA-PET，n = 18和队列2：[18F] PSMA-PET，n = 19）和一个外部（[68GA] PSMA-PET，n = 20）测试数据上，对CNN进行了测试。用骰子 - sørensen系数（DSC）评估了手动轮廓和GTV-CNN之间的规定。通过使用整个安装组织学计算两个内部测试数据集的灵敏度和特异性。 结果：队列1-3的中位DSC为0.84（范围：0.32-0.95），0.81（范围：0.28-0.93）和0.83（范围：0.32-0.93）。 GTV-CNN的敏感性和特异性与手动专家轮廓相当：分别为0.98和0.76（队列1）和1和0.57（队列2）。标准数据集的计算时间约为6秒。 结论：在[68GA] PSMA-和[18F] PSMA-PET图像中，CNN在肉体内GTV的自动轮廓中的应用与专家轮廓和与组织学相比的高灵敏度和特异性具有很高的一致性。这种强大，准确和快速的技术可以用于主要PCA中的治疗概念。训练有素的模型和研究的源代码可在开源存储库中获得。
Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET. Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) and one external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance between manual contours and GTV-CNN was assessed with Dice-Sørensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal test-datasets by using whole-mount histology. Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion: The application of a CNN for automated contouring of intraprostatic GTV in [68Ga]PSMA- and [18F]PSMA-PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.
