Alis Deniz, Onay Aslihan, Colak Evrim, Karaarslan Ercan, Bakir Baris
Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, 34750 Istanbul, Atasehir, Turkey.
Department of Radiology, Faculty of Medicine, TOBB University of Economics and Technology, Beştepe Mah Yasam Cad No. 5, 06510 Ankara, Yenimahalle, Turkey.
Diagnostics (Basel). 2025 May 26;15(11):1342. doi: 10.3390/diagnostics15111342.
: Magnetic resonance imaging (MRI) is crucial in detecting suspicious lesions and diagnosing clinically significant prostate cancer (csPCa). However, variability in MRI-targeted diagnostic pathways arises due to factors such as patient characteristics, imaging protocols, and radiologist expertise. Artificial intelligence (AI) offers potential solutions to these challenges by enhancing diagnostic accuracy and efficiency. : This narrative review explores AI techniques, particularly machine learning and deep learning, in the context of prostate cancer diagnosis. It examines their application in improving MRI scan quality, detecting artifacts, and assisting radiologists in lesion detection and interpretation. It also considers how AI helps to reduce reading time and inter-reader variability. : AI has demonstrated sensitivity that is generally comparable to experienced radiologists, although specificity tends to be lower, potentially increasing false-positive rates. The clinical impact of these results requires validation in larger, prospective multicenter studies. AI is effective in identifying artifacts, assessing MRI quality, and assisting in diagnostic efficiency by providing second opinions and automating lesion detection. However, variability in study methodologies, datasets, and imaging protocols can impact AI's generalizability, limiting its broader clinical application. : While AI shows significant promise in enhancing diagnostic accuracy and efficiency for csPCa detection, challenges remain, particularly with the generalizability of AI models. To improve AI robustness and integration into clinical practice, multicenter datasets and transparent reporting are essential. Further development, validation, and standardization are required for AI's widespread clinical adoption.
磁共振成像（MRI）在检测可疑病变和诊断具有临床意义的前列腺癌（csPCa）方面至关重要。然而，由于患者特征、成像方案和放射科医生专业知识等因素，MRI靶向诊断途径存在变异性。人工智能（AI）通过提高诊断准确性和效率，为这些挑战提供了潜在的解决方案。
本叙述性综述探讨了在前列腺癌诊断背景下的人工智能技术，特别是机器学习和深度学习。它研究了它们在提高MRI扫描质量、检测伪影以及协助放射科医生进行病变检测和解读方面的应用。它还考虑了人工智能如何有助于减少阅读时间和阅片者之间的变异性。
人工智能已显示出的敏感性通常与经验丰富的放射科医生相当，尽管特异性往往较低，可能会增加假阳性率。这些结果的临床影响需要在更大规模的前瞻性多中心研究中进行验证。人工智能在识别伪影、评估MRI质量以及通过提供第二意见和自动进行病变检测来协助提高诊断效率方面是有效的。然而，研究方法、数据集和成像方案的变异性会影响人工智能的可推广性，限制其更广泛的临床应用。
虽然人工智能在提高csPCa检测的诊断准确性和效率方面显示出巨大潜力，但挑战依然存在，特别是在人工智能模型的可推广性方面。为了提高人工智能的稳健性并将其整合到临床实践中，多中心数据集和透明报告至关重要。人工智能的广泛临床应用需要进一步的开发、验证和标准化。
