Talyshinskii Ali, Hameed B M Zeeshan, Ravinder Prajwal P, Naik Nithesh, Randhawa Princy, Shah Milap, Rai Bhavan Prasad, Tokas Theodoros, Somani Bhaskar K
Department of Urology and Andrology, Astana Medical University, Astana 010000, Kazakhstan.
Department of Urology, KMC Manipal Hospitals, Mangalore 575001, India.
Cancers (Basel). 2024 May 9;16(10):1809. doi: 10.3390/cancers16101809.
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.
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.
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, [Ga]Ga-PSMA-11, [F]DCFPyl, and [F]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.
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.
目的是分析基于深度学习（DL）的前列腺癌（PCa）诊断的现状，重点关注磁共振（MR）前列腺重建；PCa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；相关挑战及其临床意义。
根据上述领域中DL方法使用的纳入和排除标准，对PubMed数据库进行检索。
共找到784篇文章，其中64篇被纳入。分别在21、22、6、7、2和6项研究中分析了前列腺重建、前列腺癌检测与分层、前列腺癌重建以及PET/CT、ADT和活检诊断。在描述基于MR使用DL的研究中，分别有18/19/5、0/1/0和3/2/1项研究使用了磁场强度为3 T、1.5 T和3/1.5 T的数据集，在分析基于PET/CT诊断使用DL的6/7项研究中，有0/1/0项研究使用了来自单一机构的数据。在放射性示踪剂中，分别有5、1和1项研究使用了[Ga]Ga-PSMA-11、[F]DCFPyl和[F]PSMA-1007。只有两项在DT背景下分析DL的研究符合纳入标准。两项研究均使用单一机构数据集，且训练数据仅进行手动标注。三项分析DL用于前列腺活检的研究分别使用了单一机构和多机构数据集。在两项、三项和一项研究中，分别将经直肠超声（TeUS）、经直肠超声（TRUS）和磁共振成像（MRI）用作输入模态。
前列腺癌诊断中的DL模型显示出前景，但由于方法、标签和评估标准的差异，尚未准备好用于临床。在认识到所有上述局限性的同时进行更多研究，对于增强基于DL的模型在临床环境中的实用性和有效性至关重要。
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