Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications

作者：Xuetong Tao, Ziba Gandomkar, Tong Li, Warren Reed, Patrick Brennan · 年份：2022 · DOI：10.1117/12.2611342 · 被引用次数：4 · 研究领域：AI in cancer detection、Radiology practices and education、Radiomics and Machine Learning in Medical Imaging

This study investigated whether radiologists from different countries share the same sensitivity to certain mammographic features. Retrospective data were collected from Chinese and Australian radiologists reading a high-density test set which contained 40 normal and 20 cancerous mammographic cases. Sixteen Australian radiologists, and 30 Chinese radiologists, including 18 from Nanchang and 12 from Hong Kong SAR/Shenzhen, were asked to read all images in this test set using the Royal Australian and New Zealand College of Radiologists (RANZCR) rating system and annotate the suspicious lesion(s). For each case and each radiologist group, the percentage of radiologists making the correct diagnoses was calculated. For cancer cases, we also calculated the percentage of radiologists who located the lesion correctly. Spearman correlation coefficient was used to explore the association between two radiologist groups. Data demonstrated a high correlation between Chinese and Australian radiologists in identifying cancer cases (r=0.839, p<0.0001), and locating lesions (r=0.802, p<0.0001), but no statistically significant relationship in identifying normal cases (r=0.236, p=0.142). However, between radiologists from two geographic regions of China, strong correlations were found in detecting cancer cases (r=0.686, p=0.0008), marking lesions (r=0.803, p<0.0001) and recognizing normal cases (r=0.562, p=0.0002). In conclusion, although Chinese and Australian radiologists may share ...