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

Xuetong Tao; Ziba Gandomkar; Tong Li; Warren M. Reed; Patrick C. Brennan

doi:10.1117/12.2611342

4 April 2022 Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications

Xuetong Tao, Ziba Gandomkar, Tong Li, Warren M. Reed, Patrick C. Brennan

Author Affiliations +

Proceedings Volume 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment; 1203517 (2022) https://doi.org/10.1117/12.2611342
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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 the same difficulty in diagnosing and locating cancers, a difference in the challenge of identifying normal cases between them was shown. However, the performance by radiologists within China, although from different regions, remained consistent when reading high-density mammograms.

Citation Download Citation

Xuetong Tao, Ziba Gandomkar, Tong Li, Warren M. Reed, and Patrick C. Brennan "Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 1203517 (4 April 2022); https://doi.org/10.1117/12.2611342

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