Browsing by Author "Yang, Eric C."

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    Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images
    (SPIE, 2020) Yang, Eric C.; Brenes, David R.; Vohra, Imran S.; Schwarz, Richard A.; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.
    Purpose:In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm2, a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.
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    Development of an integrated multimodal optical imaging system with real-time image analysis for the evaluation of oral premalignant lesions
    (SPIE, 2019) Yang, Eric C.; Vohra, Imran S.; Badaoui, Hawraa; Schwarz, Richard A.; Cherry, Katelin D.; Quang, Timothy; Jacob, Justin; Lang, Alex; Bass, Nancy; Rodriguez, Jessica; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.
    Oral premalignant lesions (OPLs), such as leukoplakia, are at risk of malignant transformation to oral cancer. Clinicians can elect to biopsy OPLs and assess them for dysplasia, a marker of increased risk. However, it is challenging to decide which OPLs need a biopsy and to select a biopsy site. We developed a multimodal optical imaging system (MMIS) that fully integrates the acquisition, display, and analysis of macroscopic white-light (WL), autofluorescence (AF), and high-resolution microendoscopy (HRME) images to noninvasively evaluate OPLs. WL and AF images identify suspicious regions with high sensitivity, which are explored at higher resolution with the HRME to improve specificity. Key features include a heat map that delineates suspicious regions according to AF images, and real-time image analysis algorithms that predict pathologic diagnosis at imaged sites. Representative examples from ongoing studies of the MMIS demonstrate its ability to identify high-grade dysplasia in OPLs that are not clinically suspicious, and to avoid unnecessary biopsies of benign OPLs that are clinically suspicious. The MMIS successfully integrates optical imaging approaches (WL, AF, and HRME) at multiple scales for the noninvasive evaluation of OPLs.
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    In Vivo Multimodal Optical Imaging: Improved Detection of Oral Dysplasia in Low-Risk Oral Mucosal Lesions
    (AACR, 2018) Yang, Eric C.; Schwarz, Richard A.; Lang, Alexander K.; Bass, Nancy; Badaoui, Hawraa; Vohra, Imran S.; Cherry, Katelin D.; Williams, Michelle D.; Gillenwater, Ann M.; Vigneswaran, Nadarajah; Richards-Kortum, Rebecca R.
    Early detection of oral cancer and oral premalignant lesions (OPL) containing dysplasia could improve oral cancer outcomes. However, general dental practitioners have difficulty distinguishing dysplastic OPLs from confounder oral mucosal lesions in low-risk populations. We evaluated the ability of two optical imaging technologies, autofluorescence imaging (AFI) and high-resolution microendoscopy (HRME), to diagnose moderate dysplasia or worse (ModDys+) in 56 oral mucosal lesions in a low-risk patient population, using histopathology as the gold standard, and in 46 clinically normal sites. AFI correctly diagnosed 91% of ModDys+ lesions, 89% of clinically normal sites, and 33% of benign lesions. Benign lesions with severe inflammation were less likely to be correctly diagnosed by AFI (13%) than those without (42%). Multimodal imaging (AFI+HRME) had higher accuracy than either modality alone; 91% of ModDys+ lesions, 93% of clinically normal sites, and 64% of benign lesions were correctly diagnosed. Photos of the 56 lesions were evaluated by 28 dentists of varied training levels, including 26 dental residents. We compared the area under the receiver operator curve (AUC) of clinical impression alone to clinical impression plus AFI and clinical impression plus multimodal imaging using k-Nearest Neighbors models. The mean AUC of the dental residents was 0.71 (range: 0.45–0.86). The addition of AFI alone to clinical impression slightly lowered the mean AUC (0.68; range: 0.40–0.82), whereas the addition of multimodal imaging to clinical impression increased the mean AUC (0.79; range: 0.61–0.90). On the basis of these findings, multimodal imaging could improve the evaluation of oral mucosal lesions in community dental settings.
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    Noninvasive diagnostic adjuncts for the evaluation of potentially premalignant oral epithelial lesions: current limitations and future directions
    (Elsevier, 2018) Yang, Eric C.; Tan, Melody T.; Schwarz, Richard A.; Richards-Kortum, Rebecca R.; Gillenwater, Ann M.; Vigneswaran, Nadarajah
    Potentially premalignant oral epithelial lesions (PPOELs) are a group of clinically suspicious conditions, of which a small percentage will undergo malignant transformation. PPOELs are suboptimally diagnosed and managed under the current standard of care. Dysplasia is the most well-established marker to distinguish high-risk PPOELs from low-risk PPOELs, and performing a biopsy to establish dysplasia is the diagnostic gold standard. However, a biopsy is limited by morbidity, resource requirements, and the potential for underdiagnosis. Diagnostic adjuncts may help clinicians better evaluate PPOELs before definitive biopsy, but existing adjuncts, such as toluidine blue, acetowhitening, and autofluorescence imaging, have poor accuracy and are not generally recommended. Recently, in vivo microscopy technologies, such as high-resolution microendoscopy, optical coherence tomography, reflectance confocal microscopy, and multiphoton imaging, have shown promise for improving PPOEL patient care. These technologies allow clinicians to visualize many of the same microscopic features used for histopathologic assessment at the point of care.