Analysis of challenging mammographic cases demonstrates subtle reader group discrepancies

IF 2.8 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-08-23 DOI:10.1016/j.radi.2025.103134

N. Clerkin , C. Ski , M. Suleiman , Z. Gandomkar , P. Brennan , R. Strudwick

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引用次数: 0

Abstract

Introduction

High quality image interpretation is essential to detect early abnormalities on mammograms. A better understanding of the types of image characteristics that are most challenging to readers would support future education, as well as underpin advancements in AI modelling. This current work focuses on radiography advanced practitioners (RAP) to establish if RAPs and radiologists are challenged by the same characteristics.

Methods

This was a prospective, comparison study of radiographer and radiologist mammography readings. Using a cloud-based image interpretative platform and a 5 MP display, 16 radiographers and 24 radiologists read a test set of 60 mammograms with 20 confirmed cancer cases. Difficulty indices were calculated for each group based on error rates for each mammographic case. Unpaired Mann–Whitney tests were employed to compare error rates between various image characteristics. Spearman correlation analysis was used to establish if difficulty indices were associated with each cohort.

Results

Strong correlations for cancer and normal cases difficulty indices respectively (r = 0.83 CI:0.61–0.93) and (r = 0.73; CI:0.54–0.85) were shown between both groups. Greatest difficulty scores were shown for cases with soft tissue appearances as opposed calcifications (p = 0.003) and for cases without prior images, compared to those with (p = 0.03). No significant image characteristic differences were noted for the radiologists.

Conclusion

This early study acknowledges a strong correlation between radiologists and radiographers when determining which mammographic cases are difficult to interpret. However, radiographers appear to be more susceptible to varying cancer appearances as well as the non-availability of prior images with normal cases.

Implications for practice

The results should be helpful when tailoring educational strategies and developing augmented artificial intelligence (AI) solutions to support human readers.

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分析具有挑战性的乳房x光病例显示了微妙的读者群体差异

高质量的图像解释对于发现乳房x光检查的早期异常至关重要。更好地理解对读者来说最具挑战性的图像特征类型，将支持未来的教育，并巩固人工智能建模的进步。目前的工作重点是放射学高级从业人员（RAP），以确定RAP和放射科医生是否受到相同特征的挑战。方法这是一项前瞻性的比较研究，放射技师和放射科医生的乳房x光检查读数。16名放射技师和24名放射科医生使用基于云的图像解释平台和500万像素的显示器，阅读了60张乳房x光片，其中有20例确诊的癌症病例。根据每个乳房x光检查病例的错误率计算各组的难度指数。采用非配对曼-惠特尼检验比较不同图像特征之间的错误率。采用Spearman相关分析确定难度指数是否与每个队列相关。结果两组患者的难度指数分别为（r = 0.83, CI:0.61 ~ 0.93）和（r = 0.73, CI:0.54 ~ 0.85）。与有软组织钙化表现的病例相比，有软组织钙化表现的病例（p = 0.003）和没有先前图像的病例（p = 0.03）的难度评分最高。放射科医生的图像特征无显著差异。结论：这项早期研究承认，在确定哪些乳房x光检查病例难以解释时，放射科医生和放射技师之间存在很强的相关性。然而，放射技师似乎更容易受到不同癌症外观的影响，以及无法获得正常病例的先前图像。这些结果应该有助于定制教育策略和开发增强人工智能（AI）解决方案，以支持人类读者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.