He Mingze, Cao Yu, Chi Changliang, Yang Xinyi, Ramin Rzayev, Wang Shuowen, Yang Guodong, Mukhtorov Otabek, Zhang Liqun, Kazantsev Anton, Enikeev Mikhail, Hu Kebang
Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
Front Oncol. 2023 Jun 13;13:1189370. doi: 10.3389/fonc.2023.1189370. eCollection 2023.
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
多参数磁共振成像（mpMRI）已成为前列腺癌的一线筛查和诊断工具，有助于治疗选择和无创放射治疗引导。然而，MRI数据的人工解读具有挑战性且耗时，这可能会影响敏感性和特异性。随着最近的技术进步，基于MRI数据的计算机辅助诊断（CAD）形式的人工智能（AI）已应用于前列腺癌的诊断和治疗。在AI技术中，涉及卷积神经网络的深度学习有助于前列腺癌的检测、分割、评分、分级和预后评估。CAD系统具有自动操作、快速处理和准确性，将前列腺多参数MRI数据的多个序列纳入深度学习模型。因此，它们已成为一个备受关注的研究方向，尤其是在智能医疗领域。本综述重点介绍了深度学习技术在基于MRI的前列腺癌诊断和治疗中的当前进展。简要描述了CAD系统中基于深度学习的MRI图像处理以及前列腺癌放射治疗的关键要素，使其不仅对放射科医生，而且对没有专门影像解读培训的普通医生也易于理解。深度学习技术能够实现前列腺癌的病变识别、检测和分割、分级和评分，以及术后复发和预后结果的预测。通过优化模型和算法、扩展医学数据库资源以及结合多组学数据和对各种形态学数据的综合分析，可以提高深度学习的诊断准确性。深度学习有可能成为未来前列腺癌诊断和治疗的关键诊断方法。
