Widaatalla Yousif, Wolswijk Tom, Khan Muhammad Danial, Halilaj Iva, Mosterd Klara, Woodruff Henry C, Lambin Philippe
The D-Lab, Department of Precision Medicine, Maastricht University, 6200 MD Maastricht, The Netherlands.
GROW Research Institute for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands.
Cancers (Basel). 2025 Feb 24;17(5):768. doi: 10.3390/cancers17050768.
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.
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.
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.
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：放射组学在医学成像领域已取得显著发展；然而，其在光学相干断层扫描（OCT）中的潜力尚未得到广泛探索。我们系统地评估了从良性痣的OCT扫描中提取的手工制作放射组学特征（HRF）的可重复性和再现性，并研究了箱宽（BW）选择对HRF稳定性的影响。还评估了使用稳定特征对放射组学分类模型的影响。
在这项前瞻性研究中，20名志愿者对40个良性痣进行了重复测试的OCT成像，共获得80次扫描。使用一致性相关系数（CCC）评估从手动勾勒的感兴趣区域（ROI）中提取的HRF在5至50的不同箱宽范围内的可重复性和再现性。在去除高度相关特征以消除冗余后，在每个箱宽下确定了一组独特的稳定HRF。这些稳健特征被纳入一个多类放射组学分类器中，该分类器经过训练以区分良性痣、基底细胞癌（BCC）和鲍恩病。
在所有箱宽下共识别出6个稳定的HRF，25的箱宽成为最佳选择，它在可重复性和捕捉有意义纹理细节的能力之间取得了平衡。此外，中等箱宽（20 - 25）产生了53个可重复特征。与传统特征选择方法的76%准确率以及0.86和0.80的曲线下面积（AUC）相比，使用6个稳定特征训练的分类器对BCC和鲍恩病的准确率分别达到90%，AUC分别为0.96和0.94。
本研究强调了箱宽选择在增强HRF稳定性方面的关键作用，并为优化OCT放射组学中的预处理提供了一个方法框架。通过展示将稳定的HRF整合到诊断模型中，我们确立了OCT放射组学作为一种有前途的工具，可辅助皮肤科的无创诊断。
