Department of Radiology, Box 3808, Duke University Medical Center, Durham, NC 27710, USA.
Acad Radiol. 2012 Jul;19(7):865-71. doi: 10.1016/j.acra.2012.01.012. Epub 2012 Mar 27.
The objective of this study is to test the hypothesis that there are patterns in erroneous assessment of BI-RADS features among radiology trainees when interpreting mammographic masses and that these patterns can be captured in individualized statistical user models. Identifying these patterns could be useful in personalizing and adapting educational material to complement the individual weaknesses of each trainee during his or her mammography education.
Reading data of 33 mammographic cases containing masses was used. The cases were individually described by 10 radiology residents using four BI-RADS features: mass shape, mass margin, mass density and parenchyma density. For each resident, an individual model was automatically constructed that predicts likelihood (HIGH or LOW) of erroneously assigning each BI-RADS descriptor by the resident. Error was defined as deviation of the resident's assessment from the expert assessments. We evaluated the predictive performance of the models using leave-one-out crossvalidation.
The user models were able to predict which assessments have higher likelihood of error. The proportion of actual errors to the number of situations in which these errors could potentially occur was significantly higher (P < .05) when user-model assigned HIGH likelihood of error than when LOW likelihood of error was assigned for three of the four BI-RADS features. Overall, the difference between the HIGH and LOW likelihood of error groups was statistically significant (P < .0001) combining all four features.
Error making in BI-RADS descriptor assessment appears to follow patterns that can be captured with statistical pattern recognition-based user models.
本研究的目的是检验以下假设，即在解释乳腺肿块的乳腺 X 线摄影时，放射科受训者对 BI-RADS 特征的错误评估存在模式，并且这些模式可以在个体化的统计用户模型中捕捉到。识别这些模式可能有助于个性化和调整教育材料，以补充每个受训者在乳腺 X 线摄影教育中的个体弱点。
使用了 33 个包含肿块的乳腺 X 线摄影病例的阅读数据。这些病例由 10 名放射科住院医师分别使用 4 个 BI-RADS 特征进行描述：肿块形状、肿块边缘、肿块密度和实质密度。对于每个住院医师，自动构建一个个体模型，预测住院医师错误分配每个 BI-RADS 描述符的可能性（高或低）。错误定义为住院医师评估与专家评估的偏差。我们使用留一法交叉验证评估模型的预测性能。
用户模型能够预测哪些评估具有更高的错误可能性。在四个 BI-RADS 特征中的三个特征中，当用户模型分配高错误可能性时，实际错误的比例与这些错误可能发生的情况数之比显著更高（P <.05）。总体而言，将高和低错误可能性组之间的差异结合所有四个特征进行统计学分析，差异具有统计学意义（P <.0001）。
BI-RADS 描述符评估中的错误似乎遵循可以通过基于统计模式识别的用户模型捕捉到的模式。
