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NIH National Institute of Dental and Craniofacial Research (NIDCR) Home page
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Advancing Precision Imaging for Enhanced Diagnosis and Treatment for Oral Lesions

Background

Cancer in the mouth and back of the throat is notoriously difficult to detect. The suspicious tissues, or oral lesions, often resemble mouth sores or patches from other common or harmless conditions, posing a challenge for clinicians to identify. The gold standard for screening is to biopsy the lesions, a technique that has low specificity and sensitivity and that can cause pain and dysfunction in patients. These limitations can delay detection and diagnosis. As a result, oral cancer remains one of the deadliest forms of cancer, with a five-year survival rate of less than 50%.

NIDCR’s Response

In response, the institute issued the Precision Imaging of Oral Lesions notice of funding opportunity, or NOFO (formerly known as a funding opportunity announcement), in 2018. The NOFO invited projects aimed at improving the detection, diagnosis, and personalized treatment of oral lesions through advanced imaging techniques. These techniques include those aided by artificial intelligence, machine learning, and digital pathology. The goal is to standardize quantitative imaging metrics, integrate imaging into clinical trials, and use imaging data to profile oral lesions, ultimately improving patient outcomes.

In 2024, NIDCR issued the Advancing Precision Imaging for Enhanced Diagnosis and Treatment for Oral Lesions notice of special interest, or NOSI, to update the initiative to include projects that characterize inflammatory processes of oral lesions using advanced imaging technology.

NIDCR-Funded Awards

To date, NIDCR has issued 14 awards totaling approximately $21 million.

Year First IssuedAward NumberPrincipal Investigator(s)InstitutionAward Title
2020R01-DE029590

Rebecca Richards-Kortum

Ann M. Gillenwater

Richard Alan Schwarz

Rice UniversityPrecision Optical Guidance for Oral Biopsy Based on Next-Generation Hallmarks of Cancer
2020R21-DE029005

Hsun-Liang Chan

Oliver D. Kripfgans

University of Michigan at Ann ArborQuantitative Estimation of Periodontal Inflammation and Tissue Destruction with High-Resolution Ultrasound-Based Imaging
2021R01-DE030169Stella KangNew York University School of MedicineOptimizing Oral Cancer Screening and Precision Management of Potentially Malignant Oral Lesions
2021R01-DE030656

Curtis Pickering

Guanghua Xiao

University of Texas MD Anderson Cancer CenterDeep Learning Image Analysis Algorithms to Improve Oral Cancer Risk Assessment for Oral Potentially Malignant Disorders
2021R21-DE029603Hope AmmUniversity of Alabama at BirminghamNon-Invasive Imaging of Ameloblastomas
2021R21-DE031095Ryan Joseph HalterDartmouth CollegeClassifying Oral Lesions with Chip-on-tip Electrical Impedance Sensing
2021R56-DE030872

Oliver D. Kripfgans

Hsun-Liang Chan

University of Michigan at Ann ArboryUltrasonic Imaging of Bone Graft Healing in Extraction Sockets for Precise and Personalized Implant Therapy
2021R56-DE030962Otto Z. ZhouUniversity of North Carolina Chapel HillImprove The Diagnostic Accuracy of CBCT for Oral Lesions
2022R01-DE030894

Ruijiang Li

Quynh-Thu Xuan Le

Stanford UniversityPrecision Imaging for Risk Stratification and Personalized Therapy of Oropharyngeal Cancer
2022R01-DE031319John T. McDevittNew York UniversityOral Dysplasia and Oral Cavity Cancer Risk in Dental and Medical Surveillance Settings Using a Chairside Chip-Based Cytopathology Tool
2022R01-DE032033Matthew SpitzerUniversity of California, San FranciscoImmune Determinants of Progression from Oral Epithelial Dysplasia to Oral Squamous Cell Carcinoma by Precision Multiplexed Imaging
2023R01-DE031998Caigang ZhuUniversity of KentuckyPoint-Of-Care Optical Spectroscopy Platform and Novel Ratio-Metric Algorithms for Rapid and Systematic Functional Characterization of Biological Models In Vivo
2023R01-DE033427Anand T.N. KumarMassachusetts General HospitalFluorescence Lifetime-Based Tumor Contrast Enhancement Using Exogenous Probes
2023R21-DE032560

Rong Wang

Yong Wang

University of Missouri, Kansas CityInfrared Spectroscopic Imaging and Machine Learning for Risk Stratification of Oral Epithelial Dysplasia
Last Reviewed
October 2024
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