Background/Objectives: Radiomics has seen substantial growth in medical imaging; however, its potential in optical coherence tomography (OCT) has not been widely explored. We systematically evaluate the repeatability and reproducibility of handcrafted radiomics features (HRFs) from OCT scans of benign nevi and examine the impact of bin width (BW) selection on HRF stability. The effect of using stable features on a radiomics classification model was also assessed. Methods: In this prospective study, 20 volunteers underwent test–retest OCT imaging of 40 benign nevi, resulting in 80 scans. The repeatability and reproducibility of HRFs extracted from manually delineated regions of interest (ROIs) were assessed using concordance correlation coefficients (CCCs) across BWs ranging from 5 to 50. A unique set of stable HRFs was identified at each BW after removing highly correlated features to eliminate redundancy. These robust features were incorporated into a multiclass radiomics classifier trained to distinguish benign nevi, basal cell carcinoma (BCC), and Bowen’s disease. Results: Six stable HRFs were identified across all BWs, with a BW of 25 emerging as the optimal choice, balancing repeatability and the ability to capture meaningful textural details. Additionally, intermediate BWs (20–25) yielded 53 reproducible features. A classifier trained with six stable features achieved a 90% accuracy and AUCs of 0.96 and 0.94 for BCC and Bowen’s disease, respectively, compared to a 76% accuracy and AUCs of 0.86 and 0.80 for a conventional feature selection approach. Conclusions: This study highlights the critical role of BW selection in enhancing HRF stability and provides a methodological framework for optimizing preprocessing in OCT radiomics. By demonstrating the integration of stable HRFs into diagnostic models, we establish OCT radiomics as a promising tool to aid non-invasive diagnosis in dermatology.
背景/目的：影像组学在医学影像领域发展迅速，但其在光学相干断层扫描（OCT）中的应用潜力尚未得到广泛探索。本研究系统评估了良性痣OCT图像中手工提取影像组学特征（HRFs）的重复性与再现性，并分析了灰度分级宽度（BW）选择对特征稳定性的影响，同时评估了使用稳定特征构建影像组学分类模型的效果。方法：在这项前瞻性研究中，20名志愿者的40个良性痣接受了重复OCT扫描，共获得80幅图像。通过手动勾画感兴趣区域（ROIs）提取HRFs，在5至50的BW范围内使用一致性相关系数（CCCs）评估特征的重复性与再现性。为消除冗余，在去除高相关性特征后，为每个BW确定了一组独特的稳定HRFs。将这些稳健特征纳入多类影像组学分类器，用于区分良性痣、基底细胞癌（BCC）和鲍恩病。结果：在所有BW中识别出6个稳定HRFs，其中BW=25在重复性与纹理细节表征能力间达到最佳平衡，被确定为最优参数。此外，中等BW范围（20-25）可产生53个再现性良好的特征。使用6个稳定特征训练的分类器对BCC和鲍恩病的诊断准确率达90%，曲线下面积（AUC）分别为0.96和0.94；而传统特征选择方法的准确率仅为76%，AUC分别为0.86和0.80。结论：本研究揭示了BW选择对提升HRFs稳定性的关键作用，为优化OCT影像组学预处理提供了方法学框架。通过将稳定HRFs整合至诊断模型，证实OCT影像组学有望成为皮肤科无创诊断的重要辅助工具。
肿瘤(癌症)患者之家