1. University of Arizona Receives NIH Grant for Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting

    University of Arizona Receives NIH Grant for Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting

    University of Arizona Receives a 2021 NIH Grant for $693,571 for Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting. The principal investigator is Rongguang Liang. Below is a summary of the proposed work.

    Oral and oropharyngeal squamous cell carcinoma (OSCC) together rank as the sixth most common cancer worldwide, accounting for 400,000 new cancer cases each year. Two-thirds of these cancers occur in low- and middle-income countries (LMICs). While the 5-year survival rate in the U.S. is 62%, the survival rate is only 10- 40% and cure rate around 30% in the developing world. The poor survival rate in LMICs is mainly due to late diagnosis and the resultant progression of disease to an advanced stage at diagnosis. Therefore, it is imperative to diagnose precursor and malignant lesions in LRS early and expeditiously. To meet the need for technologies that enable comprehensive oral cancer screening and diagnosis in low resource settings (LRS) to identify the suspicious lesions, triage the high-risk subjects and thereby enable appropriate treatment management and follow up, this project brings together an interdisciplinary team with complementary expertise in optical imaging, oncology, deep learning, technology translation, and commercialization. The team will develop, validate, and clinically translate a multimodal intraoral imaging system for oral cancer detection and diagnosis with better sensitivity and specificity. This work will address key barriers to adopting optical imaging techniques for oral cancer in LRS by building on the team’s experience in 1) developing and evaluating dual-mode (polarized white light imaging [pWLI] and autofluorescence imaging [AFI]) mobile imaging probes; 2) evaluating a low-cost, portable optical coherence tomography (OCT) system for oral cancer detection and diagnosis in a nodal center setting in India; and 3) developing and evaluating deep learning-based image classification algorithms for clinical decision-making guidance. As each of these key techniques has been demonstrated separately for oral cancer imaging in LRS, the potential of successfully developing a multimodal intraoral imaging system for accurate, objective and location-resolved diagnosis of oral cancer and transitioning to a new capability to medical professionals in LRS is very high. To achieve the project objective, the team proposes three Aims: 1) develop a portable, semi-flexible, and compact multimodal intraoral imaging system; 2) evaluate the clinical feasibility of the prototyped intraoral imaging system and develop deep learning-based image processing algorithms for early detection, diagnosis, and mapping of oral dysplastic and malignant lesions; and 3) validate the capability of the prototyped intraoral imaging system for diagnosing oral dysplasia and malignant lesions. Successful completion of this project will lead to the transition of a multimodal intraoral imaging system and deep learning image classification that leverage the individual strengths of multiple technologies and deliver new and urgently-needed capabilities to the end users in LRS. This integrated system will 1) detect suspicious regions with high sensitivity and specificity; 2) triage the high-risk subjects; and 3) guide the selection of biopsy sites and map lesion heterogeneity to improve treatment planning and intra-operative guidance.

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