Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, 5612 AP, Eindhoven, The Netherlands.
Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Eur Radiol. 2020 Feb;30(2):806-815. doi: 10.1007/s00330-019-06436-w. Epub 2019 Oct 10.
The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.
This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.
The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.
In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.
• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.
本研究旨在评估基于 B 模式、剪切波弹性成像（SWE）和动态对比增强超声（DCE-US）放射组学的机器学习在经直肠超声引导下定位前列腺癌（PCa）病变的潜力。
本研究经机构审查委员会批准，纳入 50 名经活检证实患有 PCa 并接受根治性前列腺切除术的男性患者。在手术前，患者接受经直肠超声（TRUS）、SWE 和 DCE-US 检查三个成像平面。图像自动分割和配准。首先，从 DCE-US 视频中提取与对比灌注和弥散相关的基于模型的特征。随后，从所有模态中提取放射组学特征。通过随机森林分类算法应用机器学习，使用根治性前列腺切除标本的配准组织病理学作为参考，绘制良性和恶性感兴趣区。为避免过拟合，通过留一患者交叉验证评估多参数分类器的性能。
多参数分类器在区域接收器工作特征曲线（ROC-AUC）上的 PCa 和 Gleason >3+4 显著 PCa 的区域分别达到 0.75 和 0.90，优于最佳表现的单参数（即对比速度），ROC-AUC 分别为 0.69 和 0.76。机器学习表明，灌注、弥散和弹性相关特征的组合是有利的。
本文证明了多参数机器学习在改善 PCa 定位方面优于单一 US 模式的技术可行性。扩展的训练和测试数据集可能会确定自动多参数 US 分类在 PCa 早期诊断中的临床价值。
• 基于机器学习的 B 模式超声、剪切波弹性成像和对比超声放射组学的组合在技术上是可行的。• 多参数超声在定位前列腺癌方面的能力优于单一超声模式。• 计算机辅助多参数超声有助于临床医生进行活检靶向。
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