Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung, 81362, Taiwan.
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
Sci Rep. 2023 Dec 9;13(1):21849. doi: 10.1038/s41598-023-49159-1.
Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.
早期发现前列腺癌 (PCa) 和良性前列腺增生 (BPH) 对于维护老年男性的健康和福祉至关重要。本研究旨在评估卷积神经网络 (CNNs) 的迁移学习在经直肠超声 (TRUS) 图像中对 PCa 和 BPH 进行有效分类的性能。本研究采用回顾性实验设计，共纳入 1380 张 PCa 患者的 TRUS 图像和 1530 张 BPH 患者的 TRUS 图像。采用七种最先进的深度学习 (DL) 方法作为分类器，并应用迁移学习到流行的 CNN 架构中。采用敏感性、特异性、准确性、阳性预测值 (PPV)、阴性预测值 (NPV)、Kappa 值和 Hindex (Youden 指数) 等性能指标评估 CNN 方法的可行性和效果。采用迁移学习的 CNN 方法对 TRUS 图像进行分类的性能较高，所有准确性、特异性、敏感性、PPV、NPV、Kappa 和 Hindex 值均超过 0.9400。使用两倍交叉验证评估，最佳准确性、敏感性和特异性分别达到 0.9987、0.9980 和 0.9980。研究中采用的具有迁移学习的 CNN 方法展示了其在 TRUS 图像中对 PCa 和 BPH 进行分类的效率和能力。值得注意的是，带有迁移学习的 EfficientNetV2 在区分 PCa 和 BPH 方面表现出很高的有效性，是未来诊断应用的有前途的工具。
