工作流程
人工智能
计算机科学
前列腺
前列腺癌
磁共振成像
神经组阅片室
医学物理学
医学
机器学习
放射科
数据库
癌症
神经学
精神科
内科学
作者
Mohammed R. S. Sunoqrot,Anindo Saha,Matin Hosseinzadeh,Mattijs Elschot,Henkjan Huisman
标识
DOI:10.1186/s41747-022-00288-8
摘要
Abstract Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T ( n = 3,018; 89.6%) or 1.5 T ( n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets ( e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization ( e.g. , follow-up, prognosis, interventions, and focal treatment).
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