R-3 Repository :: Browsing by Author "Vigneswaran, Nadarajah"

Browsing by Author "Vigneswaran, Nadarajah"

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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.; Bioengineering
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.
Automated frame selection process for high-resolution microendoscopy
(SPIE, 2015) Ishijima, Ayumu; Schwarz, Richard A.; Shin, Dongsuk; Mondrik, Sharon; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca; Bioengineering
We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.
DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology
(Springer Nature, 2024) Jin, Lingbo; Tang, Yubo; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok; Bioengineering; Electrical and Computer Engineering
Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.
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.; Bioengineering
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.
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.; Bioengineering
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.
Interobserver agreement in dysplasia grading: toward an enhanced gold standard for clinical pathology trialsﾠ
(Elsevier, 2015) Speight, Paul M.; Abram, Timothy J.; Floriano, Pierre N.; James, Robert; Vick, Julie; Thornhill, Martin H.; Murdoch, Craig; Freeman, Christine; Hegarty, Anne M.; D’Apice, Katy; Kerr, A. Ross; Phelan, Joan; Corby, Patricia; Khouly, Ismael; Vigneswaran, Nadarajah; Bouquot, Jerry; Demian, Nagi M.; Weinstock, Y. Etan; Redding, Spencer W.; Rowan, Stephanie; Yeh, Chih-Ko; McGuff, H. Stan; Miller, Frank R.; McDevitt, John T.; Bioengineering; Chemistry
Objective: Interobserver agreement in the context of oral epithelial dysplasia (OED) grading has been notoriously unreliable and can impose barriers for developing new molecular markers and diagnostic technologies. This paper aimed to report the details of a 3-stage histopathology review and adjudication process with the goal of achieving a consensus histopathologic diagnosis of each biopsy. Study Design: Two adjacent serial histologic sections of oral lesions from 846 patients were independently scored by 2 different pathologists from a pool of 4. In instances where the original 2 pathologists disagreed, a third, independent adjudicating pathologist conducted a review of both sections. If a majority agreement was not achieved, the third stage involved a face-to-face consensus review. Results: Individual pathologist pair κ values ranged from 0.251 to 0.706 (fair-good) before the 3-stage review process. During the initial review phase, the 2 pathologists agreed on a diagnosis for 69.9% of the cases. After the adjudication review by a third pathologist, an additional 22.8% of cases were given a consensus diagnosis (agreement of 2 out of 3 pathologists). After the face-to-face review, the remaining 7.3% of cases had a consensus diagnosis. Conclusions: The use of the defined protocol resulted in a substantial increase (30%) in diagnostic agreement and has the potential to improve the level of agreement for establishing gold standards for studies based on histopathologic diagnosis.
Mildly dysplastic oral lesions with optically-detectable abnormalities share genetic similarities with severely dysplastic lesions
(Elsevier, 2022) Brenes, David R.; Nipper, Allison J.; Tan, Melody T.; Gleber-Netto, Frederico O.; Schwarz, Richard A.; Pickering, Curtis R.; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Sikora, Andrew G.; Richards-Kortum, Rebecca R.; Bioengineering
Objective Optical imaging studies of oral premalignant lesions have shown that optical markers, including loss of autofluorescence and altered morphology of epithelial cell nuclei, are predictive of high-grade pathology. While these optical markers are consistently positive in lesions with moderate/severe dysplasia or cancer, they are positive only in a subset of lesions with mild dysplasia. This study compared the gene expression profiles of lesions with mild dysplasia (stratified by optical marker status) to lesions with severe dysplasia and without dysplasia. Materials and methods Forty oral lesions imaged in patients undergoing oral surgery were analyzed: nine without dysplasia, nine with severe dysplasia, and 22 with mild dysplasia. Samples were submitted for high throughput gene expression analysis. Results The analysis revealed 116 genes differentially expressed among sites without dysplasia and sites with severe dysplasia; 50 were correlated with an optical marker quantifying altered nuclear morphology. Ten of 11 sites with mild dysplasia and positive optical markers (91%) had gene expression similar to sites with severe dysplasia. Nine of 11 sites with mild dysplasia and negative optical markers (82%) had similar gene expression as sites without dysplasia. Conclusion This study suggests that optical imaging may help identify patients with mild dysplasia who require more intensive clinical follow-up. If validated, this would represent a significant advance in patient care for patients with oral premalignant lesions.
Multimodal optical imaging with real-time projection of cancer risk and biopsy guidance maps for early oral cancer diagnosis and treatment
(SPIE, 2023) Coole, Jackson B.; Brenes, David R.; Mitbander, Ruchika; Vohra, Imran S.; Hou, Huayu; Kortum, Alex; Tang, Yubo; Maker, Yajur; Schwarz, Richard A.; Carns, Jennifer L.; Badaoui, Hawraa; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca; Bioengineering
Significance: Despite recent advances in multimodal optical imaging, oral imaging systems often do not provide real-time actionable guidance to the clinician who is making biopsy and treatment decisions. Aim: We demonstrate a low-cost, portable active biopsy guidance system (ABGS) that uses multimodal optical imaging with deep learning to directly project cancer risk and biopsy guidance maps onto oral mucosa in real time. Approach: Cancer risk maps are generated based on widefield autofluorescence images and projected onto the at-risk tissue using a digital light projector. Microendoscopy images are obtained from at-risk areas, and multimodal image data are used to calculate a biopsy guidance map, which is projected onto tissue.ResultsRepresentative patient examples highlight clinically actionable visualizations provided in real time during an imaging procedure. Results show multimodal imaging with cancer risk and biopsy guidance map projection offers a versatile, quantitative, and precise tool to guide biopsy site selection and improve early detection of oral cancers. Conclusions: The ABGS provides direct visible guidance to identify early lesions and locate appropriate sites to biopsy within those lesions. This represents an opportunity to translate multimodal imaging into real-time clinically actionable visualizations to help improve patient outcomes.
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; Bioengineering
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.
Oral cancer point of care diagnostics
(2017-01-03) McDevitt, John T.; Christodoulides, Nicolaos; Floriano, Pierre N.; Thornhill, Martin; Redding, Spencer; Vigneswaran, Nadarajah; Murdoch, Craig; Speight, Paul; Rice University; United States Patent and Trademark Office
A point of care diagnostic test, device and disposables for determining a patient risk for oral cancer in the same visit that a sample is collected.
Programmable bio-nanochip-based cytologic testing of oral potentially malignant disorders in Fanconi anemia
(Wiley, 2015) Floriano, Pierre; Abram, Tim; Taylor, Leander; Le, Cathy; Talavera, Humberto; Nguyen, Michael; Raja, Rameez; Gillenwater, Ann; McDevitt, John; Vigneswaran, Nadarajah
Fanconi anemia (FA) is caused by mutations of DNA repair genes. The risk of oral squamous cell carcinoma (OSCC) among FA patients is 800-folds higher than in the general population. Early detection of OSCC, preferably at it precursor stage, is critical in FA patients to improve their survival. In an ongoing clinical trial, we are evaluating the effectiveness of the programmable bio-nanochip (p-BNC)-based oral cytology test in diagnosing oral potentially malignant disorders (OPMD) in non-FA patients. We used this test to compare cytomorphometric and molecular biomarkers in OSCC cell lines derived from FA and non-FA patients to brush biopsy samples of a FA patient with OPMD and normal mucosa of healthy volunteers. Our data showed that expression patterns of molecular biomarkers were not notably different between sporadic and FA-OSCC cell lines. The p-BNC assay revealed significant differences in cytometric parameters and biomarker MCM2 expression between cytobrush samples of the FA patient and cytobrush samples of normal oral mucosa obtained from healthy volunteers. Microscopic examination of the FA patient's OPMD confirmed the presence of dysplasia. Our pilot data suggests that the p-BNC brush biopsy test recognized dysplastic oral epithelial cells in a brush biopsy sample of a FA patient.
Vital-dye-enhanced multimodal imaging of neoplastic progression in a mouse model of oral carcinogenesis
(SPIE, 2013) Hellebust, Anne; Rosbach, Kelsey; Wu, Jessica Keren; Nguyen, Jennifer; Gillenwater, Ann; Vigneswaran, Nadarajah; Richards-Kortum, Rebecca; Bioengineering
In this longitudinal study, a mouse model of 4-nitroquinoline 1-oxide chemically induced tongue carcinogenesis was used to assess the ability of optical imaging with exogenous and endogenous contrast to detect neoplastic lesions in a heterogeneous mucosal surface. Widefield autofluorescence and fluorescence images of intact 2-NBDG-stained and proflavine-stained tissues were acquired at multiple time points in the carcinogenesis process. Confocal fluorescence images of transverse fresh tissue slices from the same specimens were acquired to investigate how changes in tissue microarchitecture affect widefield fluorescence images of intact tissue. Widefield images were analyzed to develop and evaluate an algorithm to delineate areas of dysplasia and cancer. A classification algorithm for the presence of neoplasia based on the mean fluorescence intensity of 2-NBDG staining and the standard deviation of the fluorescence intensity of proflavine staining was found to separate moderate dysplasia, severe dysplasia, and cancer from non-neoplastic regions of interest with 91% sensitivity and specificity. Results suggest this combination of noninvasive optical imaging modalities can be used in vivo to discriminate non-neoplastic from neoplastic tissue in this model with the potential to translate this technology to the clinic.

Browsing by Author "Vigneswaran, Nadarajah"

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