Canellas Rodrigo, Kohli Marc D, Westphalen Antonio C
Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.
Clinical Informatics, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA.
Curr Oncol Rep. 2023 Apr;25(4):243-250. doi: 10.1007/s11912-023-01371-y. Epub 2023 Feb 7.
The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging.
Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.
本综述旨在总结人工智能应用于前列腺癌磁共振成像的现状。
人工智能已应用于前列腺癌磁共振成像，以提高其诊断准确性和解读的可重复性。已经测试了多种模型用于腺体分割和体积计算、自动病变检测、定位和特征描述，以及肿瘤侵袭性和肿瘤复发的预测。例如，研究表明可以实现非常稳健的自动腺体分割和体积计算，并且可以检测病变并准确描述其特征。尽管结果很有前景，但我们应谨慎看待这些结果。大多数研究纳入了来自单一机构的少量患者样本，并且大多数模型未经过适当的外部验证。需要开展更大规模且设计良好的研究，以开发可靠的人工智能工具。
