Feasibility, Reliability, and Effectiveness of Oral cancer screening in South Asia and Southeast Asian countries: A systematic review and Meta-analysis.

OBJECTIVES: The incidence of oral cancer is significantly high in South Asia and Southeast Asia. Organized screening is an effective approach to early detection. The aim of this systematic review and meta-analysis was to evaluate the reliability, diagnostic accuracy, and effectiveness of visual oral screening by community health workers (CHWs) in identifying oral cancer/oral potentially malignant disorders (OPMDs) in this region. MATERIALS AND METHODS: We conducted a bibliographic search in PubMed, Scopus, the gray literature of Google Scholar, ProQuest dissertations, and additional manual searches. Twelve articles were included for qualitative synthesis and six for meta-analysis. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and forest plot analysis were performed. RESULTS: Meta-analysis showed CHWs identified 8% (n = 6365) as suspicious and 92% (n = 74,140) as normal. The diagnostic accuracy of visual oral screening by CHWs showed a sensitivity of 75% (CI: 74-76) and specificity of 97% (CI: 97-97) in the detection of OPMDs/oral cancer. Forest plots were obtained using a random effects model (DOR: 24.52 (CI: 22.65-26.55)) and SAUC: 0.96 (SE = 0.05). CONCLUSIONS: Oral visual examination by trained CHWs can be utilized for community screenings to detect oral cancer early. This approach can be used in primary healthcare to triage patients for further referral and management.

Inter-observer agreement among specialists in the diagnosis of oral potentially malignant disorders and oral cancer using store-and-forward technology.

OBJECTIVES: Oral cancer is a leading cause of morbidity and mortality. Screening and mobile Health (mHealth)-based approach facilitates early detection remotely in a resource-limited settings. Recent advances in eHealth technology have enabled remote monitoring and triage to detect oral cancer in its early stages. Although studies have been conducted to evaluate the diagnostic efficacy of remote specialists, to our knowledge, no studies have been conducted to evaluate the consistency of remote specialists. The aim of this study was to evaluate interobserver agreement between specialists through telemedicine systems in real-world settings using store-and-forward technology. MATERIALS AND METHODS: The two remote specialists independently diagnosed clinical images (n=822) from image archives. The onsite specialist diagnosed the same participants using conventional visual examination, which was tabulated. The diagnostic accuracy of two remote specialists was compared with that of the onsite specialist. Images that were confirmed histopathologically were compared with the onsite diagnoses and the two remote specialists. RESULTS: There was moderate agreement (k= 0.682) between two remote specialists and (k= 0.629) between the onsite specialist and two remote specialists in the diagnosis of oral lesions. The sensitivity and specificity of remote specialist 1 were 92.7% and 83.3%, respectively, and those of remote specialist 2 were 95.8% and 60%, respectively, each compared with histopathology. CONCLUSION: The diagnostic accuracy of the two remote specialists was optimal, suggesting that "store and forward" technology and telehealth can be an effective tool for triage and monitoring of patients. CLINICAL RELEVANCE: Telemedicine is a good tool for triage and enables faster patient care in real-world settings.

Comparison between fine-needle biopsy and fine-needle aspiration for EUS-guided sampling of subepithelial lesions: a meta-analysis.

BACKGROUND AND AIMS: There is limited evidence on the diagnostic performance of EUS-guided fine-needle biopsy (FNB) sampling in patients with subepithelial lesions. The aim of this meta-analysis was to compare EUS-guided FNB sampling performance with FNA in patients with GI subepithelial lesions. METHODS: A computerized bibliographic search on the main databases was performed through May 2019. The primary endpoint was sample adequacy. Secondary outcomes were diagnostic accuracy, histologic core procurement rate, and mean number of needle passes. Summary estimates were expressed in terms of odds ratio (OR) and 95% confidence interval (CI). RESULTS: Ten studies (including 6 randomized trials) with 669 patients were included. Pooled rates of adequate samples for FNB sampling were 94.9% (range, 92.3%-97.5%) and for FNA 80.6% (range, 71.4%-89.7%; OR, 2.54; 95% CI, 1.29-5.01; P = .007). When rapid on-site evaluation was available, no significant difference between the 2 techniques was observed. Optimal histologic core procurement rate was 89.7% (range, 84.5%-94.9%) with FNB sampling and 65% (range, 55.5%-74.6%) with FNA (OR, 3.27; 95% CI, 2.03-5.27; P < .0001). Diagnostic accuracy was significantly superior in patients undergoing FNB sampling (OR, 4.10; 95% CI, 2.48-6.79; P < .0001) with the need of a lower number of passes (mean difference, -.75; 95% CI, -1.20 to -.30; P = .001). Sensitivity analysis confirmed these findings in all subgroups tested. Very few adverse events were observed and did not impact on patient outcomes. CONCLUSIONS: Our results speak clearly in favor of FNB sampling, which was found to outperform FNA in all diagnostic outcomes evaluated.

A Novel Mobile Health Approach to Early Diagnosis of Oral Cancer.

AIM: The incidence of oral cancer is high in India, which can be reduced by early detection. We aimed to empower frontline health care providers (FHP) for early detection and connect specialist to rural population through mHealth. MATERIALS AND METHODS: We provided training to FHPs in examination of oral cavity, use of mobile phone for image capture, and risk factor analysis. The FHPs were selected from different cohorts in resource-constrained settings. The workflow involved screening of high-risk individuals in door-to-door and workplace settings, and capture of images of suspected lesions. Uploaded data were interpreted and recommendation was sent by specialist from a remote location. Their recommendation was intimated to FHPs who arranged for further action. Two more initiatives, one for multiple dental schools and another for private practitioners, were undertaken. RESULTS: During the period from 2010 to 2018, 42,754 subjects have been screened, and 5,406 subjects with potentially malignant disorders have been identified. The prevalence of potentially malignant disorders varied from 0.8 to 62% at different cohorts; 516 biopsies have been performed at remote locations. CONCLUSION: Connecting specialists to rural population was made possible through the use of mobile health. Trained FHP were able to reach out to the population. Electronic data capture facilitated efficient follow-up. The program was very cost-effective with screening completed under $1 per person. CLINICAL SIGNIFICANCE: In view of the high incidence of oral cancer in India, and the resource-constrained settings, mobile health paves the way for better access to specialist care for the rural population.

Curcumin and metformin-mediated chemoprevention of oral cancer is associated with inhibition of cancer stem cells.

Effective chemoprevention is critical for improving outcomes of oral cancer. As single agents, curcumin and metformin are reported to exhibit chemopreventive properties, in vitro as well as in patients with oral cancer. In this study, the chemopreventive efficacy of this drug combination was tested in a 4-nitro quinoline-1-oxide (4NQO) induced mice oral carcinogenesis model. Molecular analysis revealed a cancer stem cell (CSC)-driven oral carcinogenic progression in this model, wherein a progressive increase in the expression of CSC-specific markers (CD44 and CD133) was observed from 8th to 25th week, at transcript (40-100-fold) and protein levels (P ≤ 0.0001). Chemopreventive treatment of the animals at 17th week with curcumin and metformin indicated that the combination regimen decreased tumor volume when compared to the control arm (0.69+0.03 vs 6.66+2.4 mm3 ; P = 0.04) and improved overall survival of the animals (P = 0.03). Assessment of the molecular status showed an overall downregulation of CSC markers in the treatment arms as compared to the untreated control. Further, in vitro assessment of the treatment on the primary cells generated from progressive stages of 4NQO-induced mice tissue showed a concordant and consistent downregulation of the CSC markers following combination treatment (P < 0.05). The treatment also inhibited the migratory and self-renewal properties of these cells; the effect of which was prominent in the cultures of early dysplastic tissue (P < 0.002). Collectively, our observations suggest that the combination of curcumin and metformin may improve chemopreventive efficacy against oral squamous cell carcinoma through a CSC-associated mechanism.

Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data.

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors during the multi-step diagnostic process, and results may have significant inter-observer variability. Chemical imaging (CI) offers a promising alternative, wherein label-free imaging is used to record both the morphology and the composition of tissue and artificial intelligence (AI) is used to objectively assign histologic information. Here, we employ quantum cascade laser (QCL)-based discrete frequency infrared (DFIR) chemical imaging to record data from oral tissues. In this proof-of-concept study, we focused on achieving tissue segmentation into three classes (connective tissue, dysplastic epithelium, and normal epithelium) using a convolutional neural network (CNN) applied to three bands of label-free DFIR data with paired darkfield visible imaging. Using pathologist-annotated H&E images as the ground truth, we demonstrate results that are 94.5% accurate with the ground truth using combined information from IR and darkfield microscopy in a deep learning framework. This chemical-imaging-based workflow for OPMD classification has the potential to enhance the efficiency and accuracy of clinical oral precancer diagnosis.

Holistic Approach for the Early Detection of Oral Cancer: A Comprehensive Training Module.

Objectives: Oral cancer is significantly high in India, and screening is an effective approach to downstage the disease. Educating Community Health Workers (CHWs) on early oral cancer detection is an effective step toward reducing the burden and serves as a first step toward facilitating the transfer of knowledge. Therefore, the purpose of this hands-on education was to equip CHWs with insight on the advanced diagnostics, preventive techniques, and innovations for the early detection of oral cancer. Materials and Methods: A total of 178 participants were trained in two groups: Group 1 received training for screening and primary prevention, while group 2 received training on updates in recent diagnostic adjuncts and innovations, AI-enabled point-of-care diagnostics, and essential patient care in management of Oral Potentially Malignant Disorders (OPMDs). Pre- and post-assessment questionnaires were used to evaluate the participants. Results: The knowledge assessment scores between the pre- and post-tests showed a statistically significant difference (p < 0.001), with rise in mean score of 3.99 from baseline. Six months following training, knowledge retention revealed a statistically significant difference (p < 0.001) in the participants' ability to recall the information. Conclusion: A well-structured training module can create awareness, impart knowledge and upskill the CHWs for early detection of oral cancer. Retraining of CHWs is required for knowledge retention post-training.

CD44-SNA1 integrated cytopathology for delineation of high grade dysplastic and neoplastic oral lesions.

The high prevalence of oral potentially-malignant disorders exhibits diverse severity and risk of malignant transformation, which mandates a Point-of-Care diagnostic tool. Low patient compliance for biopsies underscores the need for minimally-invasive diagnosis. Oral cytology, an apt method, is not clinically applicable due to a lack of definitive diagnostic criteria and subjective interpretation. The primary objective of this study was to identify and evaluate the efficacy of biomarkers for cytology-based delineation of high-risk oral lesions. A comprehensive systematic review and meta-analysis of biomarkers recognized a panel of markers (n: 10) delineating dysplastic oral lesions. In this observational cross sectional study, immunohistochemical validation (n: 131) identified a four-marker panel, CD44, Cyclin D1, SNA-1, and MAA, with the best sensitivity (>75%; AUC>0.75) in delineating benign, hyperplasia, and mild-dysplasia (Low Risk Lesions; LRL) from moderate-severe dysplasia (High Grade Dysplasia: HGD) along with cancer. Independent validation by cytology (n: 133) showed that expression of SNA-1 and CD44 significantly delineate HGD and cancer with high sensitivity (>83%). Multiplex validation in another cohort (n: 138), integrated with a machine learning model incorporating clinical parameters, further improved the sensitivity and specificity (>88%). Additionally, image automation with SNA-1 profiled data set also provided a high sensitivity (sensitivity: 86%). In the present study, cytology with a two-marker panel, detecting aberrant glycosylation and a glycoprotein, provided efficient risk stratification of oral lesions. Our study indicated that use of a two-biomarker panel (CD44/SNA-1) integrated with clinical parameters or SNA-1 with automated image analysis (Sensitivity >85%) or multiplexed two-marker panel analysis (Sensitivity: >90%) provided efficient risk stratification of oral lesions, indicating the significance of biomarker-integrated cytopathology in the development of a Point-of-care assay.

Inter-observer agreement among specialists in the diagnosis of Oral Potentially Malignant Disorders and Oral Cancer using Store-and-Forward technology.

Oral Cancer is one of the most common causes of morbidity and mortality. Screening and mobile Health (mHealth) based approach facilitates remote early detection of Oral cancer in a resource-constrained settings. The emerging eHealth technology has aided specialist reach to rural areas enabling remote monitoring and triaging to downstage Oral cancer. Though the diagnostic accuracy of the remote specialist has been evaluated, there are no studies evaluating the consistency among the remote specialists, to the best of our knowledge. The purpose of the study was to evaluate the interobserver agreement between the specialists through telemedicine systems in real-world settings using store and forward technology. Two remote specialists independently diagnosed the clinical images from image repositories, and the diagnostic accuracy was compared with onsite specialist and histopathological diagnosis when available. Moderate agreement (k = 0.682) between two remote specialists and (k = 0.629) between the onsite specialist and two remote specialists in diagnosing oral lesions. The sensitivity and specificity of remote specialist 1 were 92.7% and 83.3%, whereas remote specialist 2 was 95.8% and 60%, respectively, compared to histopathology. The store and forward technology and telecare can be effective tools in triaging and surveillance of patients.

Non-invasive imaging of oral potentially malignant and malignant lesions: A systematic review and meta-analysis.

Non-invasive (NI) imaging techniques have been developed to overcome the limitations of invasive biopsy procedures, which is the gold standard in diagnosis of oral dysplasia and Oral Squamous Cell Carcinoma (OSCC). This systematic review and meta- analysis was carried out with an aim to investigate the efficacy of the NI-imaging techniques in the detection of dysplastic oral potentially malignant disorders (OPMDs) and OSCC. Records concerned in the detection of OPMDs, Oral Cancer were identified through search in PubMed, Science direct, Cochrane Library electronic database (January 2000 to October 2020) and additional manual searches. Out of 529 articles evaluated for eligibility, 56 satisfied the pre-determined inclusion criteria, including 13 varying NI-imaging techniques. Meta-analysis consisted 44 articles, wherein majority of the studies reported Autofluorescence (AFI-38.6%) followed by Chemiluminescence (CHEM), Narrow Band Imaging (NBI) (CHEM, NBI-15.9%), Fluorescence Spectroscopy (FS), Diffuse Reflectance Spectroscopy (DRS), (FS, DRS-13.6%) and 5aminolevulinic acid induced protoporphyrin IX fluorescence (5ALA induced PPIX- 6.8%). Higher sensitivities (Sen) and specificities (Spe) were obtained using FS (Sen:74%, Spe:96%, SAUC=0.98), DRS (Sen:79%, Spe:86%, SAUC = 0.91) and 5 ALA induced PPIX (Sen:91%, Spe:78%, SAUC = 0.98) in the detection of dysplastic OPMDs from non-dysplastic lesions(NDLs). AFI, FS, DRS, NBI showed higher sensitivities and SAUC (>90%) in differentiating OSCC from NDLs. Analysed NI-imaging techniques suggests the higher accuracy levels in the diagnosis of OSCC when compared to dysplastic OPMDs. 5 ALA induced PPIX, DRS and FS showed evidence of superior accuracy levels in differentiation of dysplastic OPMDs from NDLs, however results need to be validated in a larger number of studies.

Systematic Review and Meta-Analysis to Identify the Immunocytochemical Markers Effective in Delineating Benign from Malignant Thyroid Lesions in FNAC Samples.

Conventional cytology-based diagnosis for thyroid cancer is limited with more than 30-45% of nodules categorized as indeterminate, necessitating surgery for confirming or refuting the diagnosis. This systematic review and meta-analysis were aimed at identifying immunocytochemical markers effective in delineating benign from malignant thyroid lesions in fine needle aspiration cytology (FNAC) samples, thereby improving the accuracy of cytology diagnosis. A systematic review of relevant articles (2000-2021) from online databases was carried out and the search protocol registered in PROSPERO database (CRD42021229121). The quality of studies was assessed using QUADAS-2. Review Manager 5.4.1 from Cochrane collaboration and MetaDisc Version 1.4 was used to conduct the meta-analysis. Bias in the studies were visually analyzed using funnel plots, and statistical significance was evaluated by Egger's test. Systematic review identified 64 original articles, while meta-analysis in eligible articles (n = 41) identified a panel of 5 markers, Galectin-3, HBME-1, CK-19, CD-56, and TPO. Assessment of the diagnostic performance revealed that Gal-3 (sensitivity: 0.81; CI: 0.79-0.83; specificity: 0.84; CI: 0.82-0.85) and HBME-1 (sensitivity: 0.83; Cl: 0.81-0.86; specificity: 0.85; CI: 0.83-0.86) showed high accuracy in delineating benign from malignant thyroid nodules. Efficacy analysis in indeterminate nodules showed Gal-3 and HBME-1 have high specificity of 0.86 (CI 0.84-0.89) and 0.82 (CI 0.78-0.86), respectively, and low sensitivity of 0.76 (CI 0.72-0.80) and 0.75 (CI 0.70-0.80), respectively. Diagnostic odds ratio (DOR) of Galectin-3 and HBME-1 were 39.18 (CI 23.38-65.65) and 24.44 (CI 11.16-53.54), respectively. Significant publication bias was observed for the markers Galectin-3 and CK-19 (p < 0.05). The panel of 5 markers identified from this meta-analysis are high-confidence candidates that need to be validated in thyroid cytology to establish their efficacy and enable clinical applicability.

Oral epithelial cell segmentation from fluorescent multichannel cytology images using deep learning.

BACKGROUND AND OBJECTIVES: Cytology is a proven, minimally-invasive cancer screening and surveillance strategy. Given the high incidence of oral cancer globally, there is a need to develop a point-of-care, automated, cytology-based screening tool. Oral cytology image analysis has multiple challenges such as, presence of debris, blood cells, artefacts, and clustered cells, which necessitate a skilled expertise for single-cell detection of atypical cells for diagnosis. The main objective of this study is to develop a semantic segmentation model for Single Epithelial Cell (SEC) separation from fluorescent, multichannel, microscopic oral cytology images and classify the segmented images. METHODS: We have used multi-channel, fluorescent, microscopic images (number of images; n = 2730), which were stained differentially for cytoplasm and nucleus. The cytoplasmic and cell membrane markers used in the study were Mackia Amurensis Agglutinin (MAA; n: 2364) and Sambucus Nigra Agglutinin-1 (SNA-1; n: 366) with a nuclear stain DAPI. The cytology images were labelled for SECs, cluster of cells, artefacts, and blood cells. In this study, we used encoder-decoder models based on the well-established U-Net architecture, modified U-Net and ResNet-34 for multi-class segmentation. The experiments were performed with different class combinations of data to reduce imbalance. The derived MAA dataset (n: 14,706) of SEC, cluster, and artefacts/blood cells were used for developing a classification model. InceptionV3 model and a new custom Convolutional-Neural-Network (CNN) model (Artefact-Net) were trained to classify SNA-1 marker stained segmented images (n:6101). For segmentation models, Intersection Over Union (IoU) and F1 score were used as the evaluation matrices, while the classification models were evaluated using the conventional classification metrics like precision, recall and F1-Score. RESULTS: The U-Net and the modified U-Net models gave the best IoU overall (0.73-0.76) as well as for SEC segmentation (079). The images segmented using the modified U-Net model were classified by Artefact-Net and Inception V3 model with F1 scores of 0.96 and 0.95 respectively. The Artefact-Net, when compared to InceptionV3, provided a better precision and F1 score in classifying clusters (Precision: 0.91 vs 0.80; F1: 0.91 vs 0.86). CONCLUSION: This study establishes a pipeline for SEC segmentation with the segmented component containing only single cells. The pipline will enable automated, cytology-based early detection with reduced bias.

Consensus guidelines on management of oral potentially malignant disorders.

Oral cancer is usually preceded by oral potentially malignant disorders (OPMDs) and early detection can downstage the disease. The majority of OPMDs are asymptomatic in early stages and can be detected on routine oral examination. Though only a proportion of OPMDs may transform to oral squamous cell carcinoma (OSCC), they may serve as a surrogate clinical lesion to identify individuals at risk of developing OSCC. Currently, there is a scarcity of scientific evidence on specific interventions and management of OPMDs and there is no consensus regarding their management. A consensus meeting with a panel of experts was convened to frame guidelines for clinical practices and recommendations for management strategies for OPMDs. A review of literature from medical databases was conducted to provide the best possible evidence and provide recommendations in management of OPMDs.

Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings.

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer.

Salivary proteins from dysplastic leukoplakia and oral squamous cell carcinoma and their potential for early detection.

Dysplastic leukoplakia (LP) of the oral cavity is a potentially malignant condition for oral squamous cell carcinoma (OSCC), early detection of which remains an unmet clinical need. In an effort to develop non-invasive biomarker based method for early detection of the disease, differential proteomic profiling was carried out with the saliva from patients with risk habits and diagnosed with LP and those with lymph node negative and positive OSCC in comparison to healthy controls with risk habits. Ninety three proteins were observed at elevated level (≥1.5 fold), and 30 were prioritized based on a scoring system comprising of confidence of identification, presence in the various specimen groups, functional relevance, and their secretory potential. Verification was carried out in independent patient cohorts for 8 selected, representative, upregulated proteins using ELISA. Three of them CD44, S100A7, and S100P were significantly altered in patients with LP as well as OSCC and can be regarded as a panel of biomarker candidates for early detection of the malignancy. Other members may also be investigated in a targeted manner to expand the portfolio of biomarkers for early detection. The mass spectrometry data are available via ProteomeXchange with identifier PXD015722. SIGNIFICANCE: There is an unmet clinical need for non-invasive, biomarker based methods for the improved early detection and the subsequent management of oral cancer. The study represents differential proteome profiling of the saliva of patients with oral dysplastic leukoplakia (LP) - a potentially malignant lesion, patients diagnosed with oral squamous cell carcinoma (OSCC), and healthy controls to identify potential markers for the purpose of early detection of malignancy. From among the matched and prioritized proteins with elevated levels in the saliva of patients with LP and those with OSCC, eight were verified. Three of them - CD44, S100A7 and S100P appeared promising candidates as biomarkers for early detection of the neoplastic predisposition and may form the basis of clinical assays for this purpose.

Validation of the use of a fluorescent PARP1 inhibitor for the detection of oral, oropharyngeal and oesophageal epithelial cancers.

For oral, oropharyngeal and oesophageal cancer, the early detection of tumours and of residual tumour after surgery are prognostic factors of recurrence rates and patient survival. Here, we report the validation, in animal models and a human, of the use of a previously described fluorescently labelled small-molecule inhibitor of the DNA repair enzyme poly(ADP-ribose) polymerase 1 (PARP1) for the detection of cancers of the oral cavity, pharynx and oesophagus. We show that the fluorescent contrast agent can be used to quantify the expression levels of PARP1 and to detect oral, oropharyngeal and oesophageal tumours in mice, pigs and fresh human biospecimens when delivered topically or intravenously. The fluorescent PARP1 inhibitor can also detect oral carcinoma in a patient when applied as a mouthwash, and discriminate between fresh biopsied samples of the oral tumour and the surgical resection margin with more than 95% sensitivity and specificity. The PARP1 inhibitor could serve as the basis of a rapid and sensitive assay for the early detection and for the surgical-margin assessment of epithelial cancers of the upper intestinal tract.

Role of community health worker in a mobile health program for early detection of oral cancer.

BACKGROUND: The global incidence of oral cancer occurs in low-resource settings. Community-based oral screening is a strategic step toward downstaging oral cancer by early diagnosis. The mobile health (mHealth) program is a technology-based platform, steered with the aim to assess the use of mHealth by community health workers (CHWs) in the identification of oral mucosal lesions. MATERIALS AND METHODS: mHealth is a mobile phone-based oral cancer-screening program in a workplace setting. The participants were screened by two CHWs, followed by an assessment by an oral medicine specialist. A mobile phone-based questionnaire that included the risk assessment was distributed among participants. On specialist recommendation an oral surgeon performed biopsy on participants. The diagnosis by onsite specialist that was confirmed by histopathology was considered as gold standard. All individuals received the standard treatment protocol. A remote oral medicine specialist reviewed the uploaded data in Open Medical Record System. Sensitivity, specificity, positive and negative predictive values were calculated. Inter-rater agreement was analyzed with Cohen's kappa coefficient (κ) test, and the diagnostic ability of CHWs, onsite specialist, and remote specialist was illustrated using receiver operating characteristic curve. RESULTS: CHWs identified oral lesions in 405 (11.8%) individuals; the onsite specialist identified oral lesions in 394 (11.4%) individuals; and the remote specialist diagnosed oral lesions in 444 (13%). The inter-rater agreement between the CHW and the onsite specialist showed almost perfect agreement with the κ score of 0.92, and a substantial agreement between CHW and remote specialist showed a score of 0.62. The sensitivity, specificity, positive and negative predictive values of CHWs in the identification of oral lesion were 84.7, 97.6, 84.8, and 97.7%, respectively. CONCLUSION: The trained CHWs can aid in identifying oral potentially malignant disorders and they can be utilized in oral cancer-screening program mHealth effectively.

Intra-operative point-of-procedure delineation of oral cancer margins using optical coherence tomography.

OBJECTIVES: Surgical margin status is a significant determinant of treatment outcome in oral cancer. Negative surgical margins can decrease the loco-regional recurrence by five-fold. The current standard of care of intraoperative clinical examination supplemented by histological frozen section, can result in a risk of positive margins from 5 to 17 percent. In this study, we attempted to assess the utility of intraoperative optical coherence tomography (OCT) imaging with automated diagnostic algorithm to improve on the current method of clinical evaluation of surgical margin in oral cancer. MATERIALS AND METHODS: We have used a modified handheld OCT device with automated algorithm based diagnostic platform for imaging. Intraoperatively, images of 125 sites were captured from multiple zones around the tumor of oral cancer patients (n = 14) and compared with the clinical and pathologic diagnosis. RESULTS: OCT showed sensitivity and specificity of 100%, equivalent to histological diagnosis (kappa, ĸ = 0.922), in detection of malignancy within tumor and tumor margin areas. In comparison, for dysplastic lesions, OCT-based detection showed a sensitivity of 92.5% and specificity of 68.8% and a moderate concordance with histopathology diagnosis (ĸ = 0.59). Additionally, the OCT scores could significantly differentiate squamous cell carcinoma (SCC) from dysplastic lesions (mild/moderate/severe; p ≤ 0.005) as well as the latter from the non-dysplastic lesions (p ≤ 0.05). CONCLUSION: The current challenges associated with clinical examination-based margin assessment could be improved with intra-operative OCT imaging. OCT is capable of identifying microscopic tumor at the surgical margins and demonstrated the feasibility of mapping of field cancerization around the tumor.

Mobile microscopy as a screening tool for oral cancer in India: A pilot study.

Oral cancer is the most common type of cancer among men in India and other countries in South Asia. Late diagnosis contributes significantly to this mortality, highlighting the need for effective and specific point-of-care diagnostic tools. The same regions with high prevalence of oral cancer have seen extensive growth in mobile phone infrastructure, which enables widespread access to telemedicine services. In this work, we describe the evaluation of an automated tablet-based mobile microscope as an adjunct for telemedicine-based oral cancer screening in India. Brush biopsy, a minimally invasive sampling technique was combined with a simplified staining protocol and a tablet-based mobile microscope to facilitate local collection of digital images and remote evaluation of the images by clinicians. The tablet-based mobile microscope (CellScope device) combines an iPad Mini with collection optics, LED illumination and Bluetooth-controlled motors to scan a slide specimen and capture high-resolution images of stained brush biopsy samples. Researchers at the Mazumdar Shaw Medical Foundation (MSMF) in Bangalore, India used the instrument to collect and send randomly selected images of each slide for telepathology review. Evaluation of the concordance between gold standard histology, conventional microscopy cytology, and remote pathologist review of the images was performed as part of a pilot study of mobile microscopy as a screening tool for oral cancer. Results indicated that the instrument successfully collected images of sufficient quality to enable remote diagnoses that show concordance with existing techniques. Further studies will evaluate the effectiveness of oral cancer screening with mobile microscopy by minimally trained technicians in low-resource settings.