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.

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.

Contrasting clinical outcomes and socio-economic impact of young versus elderly-onset oral squamous cell carcinoma, a novel health economic analysis.

OBJECTIVES: The incidence of young-onset oral squamous cell carcinoma (OSCC) is growing, even among non-smokers/drinkers. The effects of adverse histopathological features on long-term oncologic outcomes between the young and old are controversial and confounded by significant heterogeneity. Few studies have evaluated the socio-economic impact of premature mortality from OSCC. Our study seeks to quantify these differences and their economic impact on society. MATERIALS AND METHODS: Four hundred and seventy-eight young (<45 years) and 1660 old patients (≥45 years) with OSCC were studied. Logistic regression determined predictors of recurrence and death. Survival analysis was calculated via the Kaplan-Meier method. A separate health economic analysis was conducted for India and Singapore. Years of Potential Productive Life Lost (YPPLL) were estimated with the Human Capital Approach, and premature mortality cost was derived using population-level data. RESULTS: Adverse histopathological features were seen more frequently in young OSCC: PNI (42.9% vs. 35%, p = 0.002), LVI (22.4% vs. 17.3%, p = 0.013) and ENE (36% vs. 24.5%, p < 0.001). Although 5-year OS/DSS were similar, the young cohort had received more intensive adjuvant therapy (CCRT 26.9% vs. 16.6%, p < 0.001). Among Singaporean males, the premature mortality cost per death was US $396,528, and per YPPLL was US $45,486. This was US $397,402 and US $38,458 for females. Among Indian males, the premature mortality cost per death was US $30,641, and per YPPLL was US $595. This was US $ 21,038 and US $305 for females. CONCLUSION: Young-onset OSCC is an aggressive disease, mitigated by the ability to receive intensive adjuvant treatment. From our loss of productivity analysis, the socio-economic costs from premature mortality are substantial. Early cancer screening and educational outreach campaigns should be tailored to this cohort. Alongside, more funding should be diverted to genetic research, developing novel biomarkers and improving the efficacy of adjuvant treatment in OSCC.

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.

Extended Level IV Neck Dissection for Limited Prophylactic Clearance of Level V in Node-Positive Papillary Thyroid Carcinoma: a Homogenous Study Population.

There is near consensus that prophylactic lateral neck dissection has no role in the management of differentiated thyroid cancer, but the extent of lateral neck dissection in differentiated thyroid cancer remains controversial, especially whether level V should be addressed or not. There is lot of heterogeneity in reporting of the management of level V in papillary thyroid cancer. We at our Institute address the lateral neck positive papillary thyroid cancer with selective neck dissection involving levels II-IV, performing extended level IV dissection with inclusion of the triangular area delineated by the sternocleidomastoid muscle, the clavicle, and the perpendicular line drawn to the clavicle from the point where the horizontal line at the level of cricoid cuts the posterior border of sternocleidomastoid muscle. Retrospective analysis of the departmental data set related to thyroidectomy with lateral neck dissection from 2013 to mid-2019 for papillary thyroid cancer, was carried out. Patients with recurrent papillary thyroid cancer were excluded as were patients with involvement of level V. Data related to the demography of patients, histological diagnosis, and postoperative complications were compiled and summarized. Note was made of the incidence of ipsilateral neck recurrence and the neck level involved with recurrence noted. Data was analyzed for fifty-two patients of non-recurrent papillary thyroid cancer who had undergone total thyroidectomy and lateral neck dissection involving levels II-IV, with extended dissection at level IV. It should be noted that none of the patients had clinical involvement of level V. Only two patients had lateral neck recurrence, both the recurrences were in level III, one on the ipsilateral side and the other on the contralateral side. Recurrence in the central compartment was noted in two patients, with one of these patients also having ipsilateral level III recurrence. One of the patients had distal metastasis to the lungs. Transient paresis of the unilateral vocal cords was noted in seven patients which got resolved within 2 months in all of them. Transient hypocalcemia was noted in four patients. Although our series has a small sample size with limited follow-up, it is one of the few studies in which prophylactic level V dissection has been studied in a homogenous study population of non-recurrent papillary thyroid cancer. Our study has shown that prophylactic dissection of level V may have a limited role, but further large multi-institutional studies need to be carried out to come up with a definite answer.

BCA101 Is a Tumor-Targeted Bifunctional Fusion Antibody That Simultaneously Inhibits EGFR and TGFß Signaling to Durably Suppress Tumor Growth.

The EGFR and TGFß signaling pathways are important mediators of tumorigenesis, and cross-talk between them contributes to cancer progression and drug resistance. Therapies capable of simultaneously targeting EGFR and TGFß could help improve patient outcomes across various cancer types. Here, we developed BCA101, an anti-EGFR IgG1 mAb linked to an extracellular domain of human TGFßRII. The TGFß "trap" fused to the light chain in BCA101 did not sterically interfere with its ability to bind EGFR, inhibit cell proliferation, or mediate antibody-dependent cellular cytotoxicity. Functional neutralization of TGFß by BCA101 was demonstrated by several in vitro assays. BCA101 increased production of proinflammatory cytokines and key markers associated with T-cell and natural killer-cell activation, while suppressing VEGF secretion. In addition, BCA101 inhibited differentiation of naïve CD4+ T cells to inducible regulatory T cells (iTreg) more strongly than the anti-EGFR antibody cetuximab. BCA101 localized to tumor tissues in xenograft mouse models with comparable kinetics to cetuximab, both having better tumor tissue retention over TGFß "trap." TGFß in tumors was neutralized by approximately 90% in animals dosed with 10 mg/kg of BCA101 compared with 54% in animals dosed with equimolar TGFßRII-Fc. In patient-derived xenograft mouse models of head and neck squamous cell carcinoma, BCA101 showed durable response after dose cessation. The combination of BCA101 and anti-PD1 antibody improved tumor inhibition in both B16-hEGFR-expressing syngeneic mouse models and in humanized HuNOG-EXL mice bearing human PC-3 xenografts. Together, these results support the clinical development of BCA101 as a monotherapy and in combination with immune checkpoint therapy. SIGNIFICANCE: The bifunctional mAb fusion design of BCA101 targets it to the tumor microenvironment where it inhibits EGFR and neutralizes TGFß to induce immune activation and to suppress tumor growth.

Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.

Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-making procedure. Additionally, reliability is also a significant challenge for CNN based approaches. In this study, we proposed a neural network called the attention branch network (ABN), which combines the visual explanation and attention mechanisms to improve the recognition performance and interpret the decision-making simultaneously. We also embedded expert knowledge into the network by having human experts manually edit the attention maps for the attention mechanism. Our experiments have shown that ABN performs better than the original baseline network. By introducing the Squeeze-and-Excitation (SE) blocks to the network, the cross-validation accuracy increased further. Furthermore, we observed that some previously misclassified cases were correctly recognized after updating by manually editing the attention maps. The cross-validation accuracy increased from 0.846 to 0.875 with the ABN (Resnet18 as baseline), 0.877 with SE-ABN, and 0.903 after embedding expert knowledge. The proposed method provides an accurate, interpretable, and reliable oral cancer computer-aided diagnosis system through visual explanation, attention mechanisms, and expert knowledge embedding.

Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images.

Significance: Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output. Aim: We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions. Approach: This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists. Results: The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings. Conclusions: Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model's prediction can be improved.

A Distributed Cancer Care Model with a Technology-Driven Hub-and-Spoke and further Spoke Hierarchy: Findings from a Pilot Implementation Programme in Kerala, India.

BACKGROUND: The technology enabled distributed model in Kerala is based on an innovative partnership model between Karkinos Healthcare and private health centers. The model is designed to address the barriers to cancer screening by generating demand and by bringing together the private health centers and service providers at various levels to create a network for continued care. This paper describes the implementation process and presents some preliminary findings.  Methods: The model follows the hub-and-spoke and further spoke framework. In the pilot phases, from July 2021 to December 2021, five private health centers (partners) collaborated with Karkinos Healthcare across two districts in Kerala. Screening camps were organized across the districts at the community level where the target groups were administered a risk assessment questionnaire followed by screening tests at the spoke hospitals based on a defined clinical protocol. The screened positive patients were examined further for confirmatory diagnosis at the spoke centers. Patients requiring chemotherapy or minor surgeries were treated at the spokes. For radiation therapy and complex surgeries the patients were referred to the hubs. RESULTS: A total of 2,459 individuals were screened for cancer at the spokes and 299 were screened positive. Capacity was built at the spokes for cancer surgery and chemotherapy. A total of 189 chemotherapy sessions and 17 surgeries were performed at the spokes for cancer patients. 70 patients were referred to the hub. CONCLUSION: Initial results demonstrate the ability of the technology Distributed Cancer Care Network (DCCN) system to successfully screen and detect cancer and to converge the actions of various private health facilities towards providing a continuum of cancer care. The lessons learnt from this study will be useful for replicating the process in other States.

Risk Stratification in Oral Cancer: A Novel Approach.

Background: Oral squamous cell carcinoma (OSCC) is a common head and neck cancer with high morbidity and mortality. Currently, treatment decisions are guided by TNM staging, which omits important negative prognosticators such as lymphovascular invasion, perineural invasion (PNI), and histologic differentiation. We proposed nomogram models based on adverse pathological features to identify candidates suitable for treatment escalation within each risk group according to the National Comprehensive Cancer Network (NCCN) guidelines. Methods: Anonymized clinicopathologic data of OSCC patients from 5 tertiary healthcare institutions in Asia were divided into 3 risk groups according to the NCCN guidelines. Within each risk group, nomograms were built to predict overall survival based on histologic differentiation, histologic margin involvement, depth of invasion (DOI), extranodal extension, PNI, lymphovascular, and bone invasion. Nomograms were internally validated with precision-recall analysis and the Kaplan-Meier survival analysis. Results: Low-risk patients with positive pathological nodal involvement and/or positive PNI should be considered for adjuvant radiotherapy. Intermediate-risk patients with gross bone invasion may benefit from concurrent chemotherapy. High-risk patients with positive margins, high DOI, and a high composite score of histologic differentiation, PNI, and the American Joint Committee on Cancer (AJCC) 8th edition T staging should be considered for treatment escalation to experimental therapies in clinical trials. Conclusion: Nomograms built based on prognostic adverse pathological features can be used within each NCCN risk group to fine-tune treatment decisions for OSCC patients.

Development of a Low-cost NGS Test for the Evaluation of Thyroid Nodules.

Ultrasound-guided fine needle aspiration cytology (FNAC) is the preferred method of identifying malignancy in palpable thyroid nodules using the Bethesda reporting system. However, in around 30-40% of FNACs (Bethesda categories III, IV, and V), the results are indeterminate and surgery is required to confirm malignancy. Out of those who undergo surgery, only 10-40% of patients in these categories are found to have malignancies, thus proving surgery to be unnecessary for some patients or to be incomplete in others. While molecular testing on thyroid FNAC material is part of the American Thyroid Association (ATA) guidelines in evaluating thyroid nodules, it is currently unavailable in India due to cost constraints. In this study, we prospectively collected FNAC samples from sixty-nine patients who presented with palpable thyroid nodules. We designed a cost-effective next-generation sequencing (NGS) test to query multiple variants in the DNA and RNA isolated from the fine needle aspirate. The identification of oncogenic variants was considered to be indicative of malignancy, and confirmed by surgical histopathology. The panel showed an overall sensitivity of 81.25% and a specificity of 100%, while in the case of Bethesda categories III, IV, and V, the sensitivity was higher (87.5%) and the specificity was established at 100%. The panel could thereby serve as a rule-in test for the diagnosis of thyroid cancer and therefore help identify patients who require surgery, especially in the indeterminate Bethesda categories III, IV, and V.

Incorporation of adverse features in advanced oral cancer improves precision in staging and patient prognostication.

BACKGROUND: Despite revised staging criteria, stratification of patients with advanced oral squamous cell carcinoma (OSCC) remains difficult. Well-established features like perineural invasion (PNI), differentiation, and lymphovascular-invasion (LVI) are controversial, and hence omitted from staging. We endeavor to better stratify this cohort by identifying predictors of survival in advanced OSCC (T3-4). METHODS: Seven hundred and forty-two patients with T3-4 OSCC underwent surgery from 2006 to 2013. Cox regression was performed to determine predictors of overall survival (OS). RESULTS: OS was adversely impacted by PNI (p = 0.046), LVI (p = 0.038), moderate/poor differentiation (p = 0.001), close/involved surgical margins (p = 0.002), pT (p = 0.034), and pN (p < 0.001). The cumulative number of adverse histopathological features predicted poorer OS; HR 2.64 (CI 1.42-4.90) for one adverse feature and HR 4.23 (CI 2.34-7.67) for ≥2. CONCLUSION: In advanced OSCC, stratification with histopathologic risk factors can predict survival even in maximally treated patients; adjuvant therapies are unable to entirely mitigate this risk. Incorporation of adverse features into future editions of TNM can improve precision in staging and identify candidates for treatment escalation.

Interpretable deep learning approach for oral cancer classification using guided attention inference network.

SIGNIFICANCE: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network's attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. AIM: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. APPROACH: We utilized Selvaraju et al.'s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.'s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. RESULTS: The network's attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. CONCLUSIONS: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.

Bayesian deep learning for reliable oral cancer image classification.

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.

Classification of imbalanced oral cancer image data from high-risk population.

SIGNIFICANCE: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. AIM: To reduce the class bias caused by data imbalance. APPROACH: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. RESULTS: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of "premalignancy" class is also increased, which is ideal for screening applications. CONCLUSIONS: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Cost-effectiveness of oral cancer screening approaches by visual examination: Systematic review.

The present study is the first systematic review of papers that have performed a full economic evaluation on oral cancer screening strategies using visual oral examination. The review questions were (1) Is screening a cost-effective strategy in oral cancer? (2) What is the most cost-effective strategy among the different screening approaches in oral cancer? The main outcome measure was the incremental cost-effectiveness ratio. The study identifies and reviews seven full economic evaluations. The included studies scored 75%-100% on the methodological appraisal. Majority of the studies reports that oral cancer screening is a cost-effective strategy, especially in an opportunistic setting and high-risk subset of patients. The results were sensitive to cost and effectiveness parameters. Oral cancer screening, though found cost-effective, the uncertainty around these parameters necessitates additional studies that include better estimates in the modeling assessments. The heterogeneity in studies limited comparison and generalization.

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.

A smart tele-cytology point-of-care platform for oral cancer screening.

Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.

Small form factor, flexible, dual-modality handheld probe for smartphone-based, point-of-care oral and oropharyngeal cancer screening.

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.