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

TL;DR

This study introduces 'optomics', a radiomics-based method using fluorescence molecular imaging data to improve tumor identification during head and neck cancer surgery, outperforming traditional intensity thresholding.

Contribution

The paper extends radiomics to fluorescence molecular imaging, demonstrating improved tumor detection accuracy in head and neck cancer specimens using machine learning.

Findings

Optomics achieved 89% accuracy versus 81% with thresholding.

Support vector machine classifier with selected features improved prediction.

Method showed consistent performance across different doses.

Abstract

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.