Photiou Christos, Thrapp Andrew, Tearney Guillermo, Pitris Costas
KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
Harvard Medical School, Massachusetts General Hospital, Wellman Center for Photomedicine, Boston, MA 02114, USA.
Biomed Opt Express. 2025 May 30;16(6):2543-2554. doi: 10.1364/BOE.555185. eCollection 2025 Jun 1.
Colorectal cancer (CRC) is the second leading cause of cancer-related morbidity and mortality in both men and women globally. CRC predominantly arises from dysplastic polyps that, over time, progressively evolve into malignancies. Population-wide screening through colonoscopy remains the cornerstone of CRC prevention. Optical coherence tomography (OCT) has the potential to increase the effectiveness and reduce the cost associated with colonoscopic screening. However, conclusive evidence that OCT can effectively detect pre-cancerous changes is still lacking. This study introduces a novel framework to address this challenge by extracting additional features, which can serve as biomarkers of disease, from ex vivo OCT images of colon polyps. These include first and second-order intensity and fractal statistics, as well as spectral characteristics and scatterer size, which depend on sub-cellular and biochemical tissue variations. Feature-enhanced images derived from these biomarkers were combined with intensity images and integrated into a deep-learning classification model decision-level fusion. This approach achieved 88.3% accuracy, 93.5% sensitivity, 77.9% specificity, and an AUC of 0.857 in distinguishing benign (normal and hyperplastic) polyps from cases with malignant potential (adenoma and sessile serrated adenoma), demonstrating the potential of this novel approach to enhance the role of OCT in improving CRC screening outcomes.
结直肠癌（CRC）是全球男性和女性中与癌症相关的发病率和死亡率的第二大主要原因。CRC主要起源于发育异常的息肉，随着时间的推移，这些息肉会逐渐演变成恶性肿瘤。通过结肠镜检查进行全人群筛查仍然是CRC预防的基石。光学相干断层扫描（OCT）有潜力提高结肠镜筛查的有效性并降低其成本。然而，仍然缺乏确凿的证据表明OCT能够有效检测癌前病变。本研究引入了一个新框架，通过从结肠息肉的离体OCT图像中提取额外的特征（这些特征可作为疾病的生物标志物）来应对这一挑战。这些特征包括一阶和二阶强度及分形统计量，以及取决于亚细胞和生化组织变化的光谱特征和散射体大小。从这些生物标志物中导出的特征增强图像与强度图像相结合，并集成到深度学习分类模型的决策级融合中。在区分良性（正常和增生性）息肉与具有恶性潜能的病例（腺瘤和无蒂锯齿状腺瘤）时，该方法的准确率达到88.3%，灵敏度达到93.5%，特异性达到77.9%，曲线下面积（AUC）为0.857，证明了这种新方法在增强OCT在改善CRC筛查结果方面作用的潜力。
