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.

We welcome you to join 2022 Diversity in Radiology and Molecular Imaging: Artificial Intelligence in Cancer, a one-day conference that will take place as a hybrid event on June 20, 2022. The conference will provide keynote lectures, scientific presentations and educational lectures from leaders and pioneers in the field, who will discuss important topics related to recognizing biases and promoting inclusive approaches towards artificial intelligence research in cancer molecular imaging. We will also offer virtual and in-person workshops and networking opportunities. This conference is free of charge and will provide CME credits. Call for Abstracts: We are soliciting abstracts for 6-8 minute presentations about research and education related to diversity in STEM.

Generating comprehensive molecular images of organs and tumors in living organisms can be performed at ultra-fast speed using a new deep learning approach to image reconstruction developed by researchers at Rensselaer Polytechnic Institute. The research team's new technique has the potential to vastly improve the quality and speed of imaging in live subjects and was the focus of an article recently published in Light: Science and Applications, a Nature journal. Compressed sensing-based imaging is a signal processing technique that can be used to create images based on a limited set of point measurements. Recently, a Rensselaer research team proposed a novel instrumental approach to leverage this methodology to acquire comprehensive molecular data sets, as reported in Nature Photonics. While that approach produced more complete images, processing the data and forming an image could take hours. This latest methodology developed at Rensselaer builds on the previous advancement and has the potential to produce real-time images, while also improving the quality and usefulness of the images produced.

Generating comprehensive molecular images of organs and tumors in living organisms can be performed at ultra-fast speed using a new deep learning approach to image reconstruction developed by researchers at Rensselaer Polytechnic Institute. The research team's new technique has the potential to vastly improve the quality and speed of imaging in live subjects and was the focus of an article recently published in Light: Science and Applications, a Nature journal. Compressed sensing-based imaging is a signal processing technique that can be used to create images based on a limited set of point measurements. Recently, a Rensselaer research team proposed a novel instrumental approach to leverage this methodology to acquire comprehensive molecular data sets, as reported in Nature Photonics. While that approach produced more complete images, processing the data and forming an image could take hours.

Of all the areas in which artificial intelligence can benefit the world, healthcare, in my opinion, is at the top. In the simplest possible terms there are two main reasons why artificial intelligence can be transformational in healthcare. First, there is a lot that is broken and fixing that can help millions of people, reduce the cost of healthcare, improve patient care, and help fight disease. Second, we need to accelerate our efforts in finding new and more effective cures for a long list of diseases – cancer, Alzheimer to name a few. Besides being the editor of the AI Post, I also serve as the Executive Director of Society of Artificial Intelligence in Medicine and Healthcare.