Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
contextflow GmbH, Vienna, Austria.
Eur Radiol. 2023 Jan;33(1):360-367. doi: 10.1007/s00330-022-08973-3. Epub 2022 Jul 2.
Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD).
A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses).
Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available.
The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice.
• A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083).
基于内容的图像检索系统（CBIRS）是放射学报告的一种新的、具有潜在影响力的工具，但对其临床评估还很缺乏。本研究旨在评估 CBIRS 对疑似弥漫性实质性肺疾病（DPLD）患者胸部 CT 扫描的解读效果。
共纳入 108 例回顾性胸部 CT 扫描，22 例具有 22 种独特的临床和/或组织病理学诊断，由 8 名放射科医生（4 名住院医生，4 名主治医生，分别阅读胸部 CT 扫描的中位数年限为 2.1±0.7 年和 12±1.8 年）阅读并提供疑似诊断，在认证的放射工作站上模拟临床常规。一半的阅读是在没有 CBIRS 的情况下进行的，另一半则是在 CBIRS 的额外支持下进行的。CBIRS 从 6542 例薄层 CT 扫描的大型数据库中检索出 19 种最可能的肺部特异性模式，并提供相关信息（例如，潜在鉴别诊断列表）。
尽管当 CBIRS 可用时，放射科医生更频繁地搜索额外信息（154[72%]比 95[43%]，p<0.001），但阅读时间减少了 31.3%（p<0.001）。当 CBIRS 可用时，整体诊断准确性呈上升趋势（42.2%比 34.7%，p=0.083）。
在 DPLD 病例中，使用 CBIRS 对胸部 CT 扫描的阅读时间有有益的影响。此外，如果放射科医生可以使用 CBIRS，住院医生和主治医生更有可能查阅信息资源。还需要进一步的研究来证实使用 CBIRS 实践中观察到的诊断准确性提高的趋势。
支持阅读胸部 CT 扫描的诊断过程的基于内容的图像检索系统可以将阅读时间减少 31.3%（p<0.001）。
尽管频繁使用基于内容的图像检索系统，但阅读时间仍有所减少。
当使用基于内容的图像检索系统时，观察到诊断准确性呈上升趋势（42.2%比 34.7%，p=0.083）。
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