A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.

Somphone Siviengphanom, Patrick C Brennan, Sarah J Lewis, Phuong Dung Trieu, Ziba Gandomkar
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Abstract

This study aims to investigate whether global mammographic radiomic features (GMRFs) can distinguish hardest- from easiest-to-interpret normal cases for radiology trainees (RTs). Data from 137 RTs were analysed, with each interpreting seven educational self-assessment test sets comprising 60 cases (40 normal and 20 cancer). The study only examined normal cases. Difficulty scores were computed based on the percentage of readers who incorrectly classified each case, leading to their classification as hardest- or easiest-to-interpret based on whether their difficulty scores fell within and above the 75th or within and below the 25th percentile, respectively (resulted in 140 cases in total used). Fifty-nine low-density and 81 high-density cases were identified. Thirty-four GMRFs were extracted for each case. A random forest machine learning model was trained to differentiate between hardest- and easiest-to-interpret normal cases and validated using leave-one-out-cross-validation approach. The model's performance was evaluated using the area under receiver operating characteristic curve (AUC). Significant features were identified through feature importance analysis. Difference between hardest- and easiest-to-interpret cases among 34 GMRFs and in difficulty level between low- and high-density cases was tested using Kruskal-Wallis. The model achieved AUC = 0.75 with cluster prominence and range emerging as the most useful features. Fifteen GMRFs differed significantly (p < 0.05) between hardest- and easiest-to-interpret cases. Difficulty level among low- vs high-density cases did not differ significantly (p = 0.12). GMRFs can predict hardest-to-interpret normal cases for RTs, underscoring the importance of GMRFs in identifying the most difficult normal cases for RTs and facilitating customised training programmes tailored to trainees' learning needs.

基于全局乳腺放射学特征的机器学习模型可预测放射学受训者认为最困难的正常乳腺病例。
本研究旨在探讨全局乳腺放射学特征(GMRFs)是否能区分放射学受训者(RTs)最难解读和最易解读的正常病例。研究分析了 137 名放射学受训者的数据,每位受训者负责解释 7 个由 60 个病例(40 个正常病例和 20 个癌症病例)组成的教育性自我评估测试集。本研究只对正常病例进行了检查。根据每个病例中错误分类的读者比例计算难度分数,并根据难度分数是在第75百分位及以上,还是在第25百分位及以下,分别将其归类为最难或最易解读的病例(结果共使用了140个病例)。最终确定了 59 个低密度案例和 81 个高密度案例。每个病例提取了 34 个 GMRF。对随机森林机器学习模型进行了训练,以区分最难解读和最易解读的正常病例,并采用 "留一 "交叉验证方法进行了验证。模型的性能使用接收者工作特征曲线下面积(AUC)进行评估。通过特征重要性分析确定了重要特征。使用 Kruskal-Wallis 检验了 34 个 GMRF 中最难解读和最易解读病例之间的差异,以及低密度和高密度病例之间的难度差异。该模型的 AUC = 0.75,集群突出度和范围成为最有用的特征。有 15 个 GMRF 存在显著差异(p
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