Background: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. Methods: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. Results: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. Conclusion: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.
背景：本研究旨在分析基于深度学习（DL）的前列腺癌（PCa）诊断现状，重点关注磁共振（MR）前列腺重建、前列腺癌检测/分层/重建、正电子发射断层扫描/计算机断层扫描（PET/CT）、雄激素剥夺疗法（ADT）、前列腺活检及相关挑战及其临床意义。方法：根据上述领域中DL方法应用的纳入与排除标准，对PubMed数据库进行文献检索。结果：共检索到784篇文献，其中64篇符合纳入标准。分别有21、22、6、7、2和6项研究分析了前列腺重建、前列腺癌检测与分层、前列腺癌重建、PET/CT诊断、ADT及活检。在基于MR的DL应用研究中，使用3T、1.5T及3/1.5T混合磁场强度数据集的研究分别为18/19/5项、0/1/0项和3/2/1项。在6/7项针对PET/CT诊断的DL研究中，均采用单中心数据。放射性示踪剂中，[68Ga]Ga-PSMA-11、[18F]DCFPyl和[18F]PSMA-1007分别应用于5项、1项和1项研究。仅有两项关于ADT背景下DL分析的研究符合纳入标准，均采用单中心数据集且训练数据仅进行人工标注。三项针对前列腺活检的DL研究分别采用单中心与多中心数据集，其中以超声弹性成像（TeUS）、经直肠超声（TRUS）和磁共振成像（MRI）作为输入模态的研究各为2项、3项和1项。结论：深度学习模型在前列腺癌诊断中展现出潜力，但由于方法、标注及评估标准存在差异，目前尚未达到临床应用标准。在充分认识现有局限性的基础上开展进一步研究，对增强DL模型在临床实践中的实用性与有效性至关重要。
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