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.

Evaluation of a Lectin-Based Imaging System for the Chairside Detection of Oral Dysplasia and Malignancy.

PURPOSE: Currently available oral cancer screening adjuncts have not enhanced clinical screening methods because of high false positives and negatives, highlighting the need for a molecularly specific technique for accurate screening of suspicious oral lesions. The purpose of this study was to evaluate the in vivo screening accuracy of an oral lesion identification system that evaluates aberrant glycosylation patterns using a fluorescently labeled lectin (wheat germ agglutinin and fluorescein isothiocyanate [WGA-FITC]). MATERIALS AND METHODS: The authors designed and implemented a prospective cohort study at 3 institutions composed of patients with and without suspicious oral lesions. Oral cavities were screened by clinical examination and with the oral lesion identification system according to a stepwise procedure that included the topical application and fluorescence visualization of a fluorescent nuclear stain and WGA-FITC. Tissue samples were obtained from all enrolled patients for histopathological diagnosis and were used to calculate sensitivity and specificity metrics (primary outcome variable) irrespective of the oral lesion identification system result. RESULTS: The sample was composed of 97 patients; 86 had 100 clinically suspicious lesions and 11 without such lesions were included as a control group. Use of the oral lesion identification system resulted in 100, 100, and 74% sensitivity for cancer, high-grade dysplasia, and low-grade dysplasia, respectively, and a specificity of 80%. Clinical diagnosis yielded similar sensitivity values of 84, 100, and 88% for cancer, high-grade dysplasia, and low-grade dysplasia, respectively, and a specificity of 76%. Use of the oral lesion identification system enhanced the visualization of lesion dimensionality and borders. CONCLUSIONS: The results of this study suggest the oral lesion identification system was a beneficial adjunct to standard clinical examination, because the system provided sensitivity and specificity values similar to or greater than clinical diagnosis.

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.

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.

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.

Two by Two Factorial Design using Metformin and Curcumin for Second Primary Head and Neck Cancer Prevention Trial.

OBJECTIVE: The 2x2 factorial design is an effective method that allows for multiple comparisons, especially in the context of interactions between different interventions, without substantially increasing the required sample size. In view of the considerable preclinical evidence for Curcumin and Metformin in preventing the development and progression of head and neck squamous cell carcinoma (HNSCC), this study describes the protocol of the clinical trial towards applying the drug combination in prevention of second primary tumors. METHODS: We have applied the trial design to a large phase IIB/III double-blind, multi-centric, placebo-controlled, randomized clinical trial to determine the safety and efficacy of Metformin and Curcumin in the prevention of second primary tumours (SPT) of the aerodigestive tract following treatment of HNSCC (n=1,500) [Clinical Registry of India, CTRI/2018/03/012274]. Patients recruited in this trial will receive Metformin (with placebo), Curcumin (with placebo), Metformin, and Curcumin or placebo alone for a period of 36 months. The primary endpoint of this trial is the development of SPT, while the secondary endpoints are toxicities associated with the agents, incidence of recurrence, and identifying potential biomarkers. In this article, we discuss the 2x2 factorial design and how it applies to the head and neck cancer chemoprevention trial. CONCLUSION: 2x2 factorial design is an effective trial design for chemoprevention clinical trials where the effectiveness of multiple interventions needs to be tested parallelly.

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.

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.

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.

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.

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.

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.

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.

Mobile-based oral cancer classification for point-of-care screening.

SIGNIFICANCE: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. AIM: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. APPROACH: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. RESULTS: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. CONCLUSIONS: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.