论文标题

荧光分子验光特征改善了头颈标本中肿瘤的鉴定

Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens

论文作者

Chen, Yao, Streeter, Samuel S., Hunt, Brady, Sardar, Hira S., Gunn, Jason R., Tafe, Laura J., Paydarfar, Joseph A., Pogue, Brian W., Paulsen, Keith D., Samkoe, Kimberley S.

论文摘要

在这项研究中,将放射线方法扩展到用于组织分类的光学荧光分子成像数据,称为“验光”。在头部和颈部鳞状细胞癌(HNSCC)切除过程中,出现了荧光分子成像,以进行精确的手术指导。然而,肿瘤到正常的组织对比与靶分子表皮生长因子受体(EGFR)的异质表达的内在生理局限性混淆。验光学试图通过探测荧光传达的EGFR表达中的质地模式差异来改善肿瘤识别。从荧光图像样品中提取了总共1,472个标准化的验光特征。涉及支持向量机分类器的监督机器学习管道接受了25个最高冗余最大相关标准的最高排名功能的培训。通过将切除组织的图像贴片分类为组织学确认的恶性肿瘤状态,将模型预测性能与荧光强度阈值方法进行了比较。与荧光强度阈值方法相比,验光方法在所有测试集样品上提供了一致的预测准确性(平均精度为89%vs. 81%; P = 0.0072)。改进的性能表明,将放射线学方法扩展到荧光分子成像数据为荧光引导手术中的癌症检测提供了有希望的图像分析技术。

In this study, a radiomics approach was extended to optical fluorescence molecular imaging data for tissue classification, termed 'optomics'. Fluorescence molecular imaging is emerging for precise surgical guidance during head and neck squamous cell carcinoma (HNSCC) resection. However, the tumor-to-normal tissue contrast is confounded by intrinsic physiological limitations of heterogeneous expression of the target molecule, epidermal growth factor receptor (EGFR). Optomics seek to improve tumor identification by probing textural pattern differences in EGFR expression conveyed by fluorescence. A total of 1,472 standardized optomic features were extracted from fluorescence image samples. A supervised machine learning pipeline involving a support vector machine classifier was trained with 25 top-ranked features selected by minimum redundancy maximum relevance criterion. Model predictive performance was compared to fluorescence intensity thresholding method by classifying testing set image patches of resected tissue with histologically confirmed malignancy status. The optomics approach provided consistent improvement in prediction accuracy on all test set samples, irrespective of dose, compared to fluorescence intensity thresholding method (mean accuracies of 89% vs. 81%; P = 0.0072). The improved performance demonstrates that extending the radiomics approach to fluorescence molecular imaging data offers a promising image analysis technique for cancer detection in fluorescence-guided surgery.

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