Siviengphanom Somphone, Brennan Patrick C, Lewis Sarah J, Trieu Phuong Dung, Gandomkar Ziba
Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Faculty of Medicine and Health, Sydney School of Health Sciences, Susan Wakil Health Building D18, the University of Sydney, Sydney, NSW, 2006, Australia.
School of Health Sciences, Western Sydney University, Sydney, NSW, 2751, Australia.
J Imaging Inform Med. 2025 Jun;38(3):1904-1913. doi: 10.1007/s10278-024-01291-8. Epub 2024 Oct 15.
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.
本研究旨在调查全局乳腺钼靶影像组学特征（GMRFs）能否区分放射科住院医师（RTs）最难解读与最易解读的正常病例。分析了137名RTs的数据，每人解读7套教育自我评估测试集，每套包含60个病例（40个正常病例和20个癌症病例）。本研究仅检查正常病例。根据将每个病例错误分类的读者百分比计算难度分数，根据难度分数是否分别落在第75百分位数及以上或第25百分位数及以下，将病例分类为最难解读或最易解读（总共使用了140个病例）。识别出59个低密度病例和81个高密度病例。为每个病例提取34个GMRFs。训练了一个随机森林机器学习模型，以区分最难解读与最易解读的正常病例，并使用留一法交叉验证方法进行验证。使用受试者操作特征曲线下面积（AUC）评估模型性能。通过特征重要性分析识别显著特征。使用Kruskal-Wallis检验34个GMRFs中最难解读与最易解读病例之间的差异以及低密度与高密度病例之间难度水平的差异。该模型的AUC为0.75，聚类突出度和范围是最有用的特征。最难解读与最易解读病例之间有15个GMRFs存在显著差异（p<0.05）。低密度与高密度病例之间的难度水平无显著差异（p=0.12）。GMRFs可以预测RTs最难解读的正常病例，强调了GMRFs在识别RTs最难的正常病例以及促进根据学员学习需求定制培训计划方面的重要性。
