  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.
