Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 17 de 17
Filtrar
1.
Clin Oral Investig ; 27(12): 7575-7581, 2023 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-37870594

RESUMEN

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.


Asunto(s)
Enfermedades de la Boca , Neoplasias de la Boca , Telemedicina , Humanos , Variaciones Dependientes del Observador , Neoplasias de la Boca/diagnóstico , Neoplasias de la Boca/patología , Telemedicina/métodos , Tecnología
2.
PLoS One ; 18(9): e0291972, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37747904

RESUMEN

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.


Asunto(s)
Bioensayo , Receptores de Hialuranos , Humanos , Hiperplasia/diagnóstico , Automatización , Biopsia , Glicosilación , Estudios Observacionales como Asunto
3.
Res Sq ; 2023 Apr 05.
Artículo en Inglés | MEDLINE | ID: mdl-37066209

RESUMEN

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.

4.
Cancers (Basel) ; 15(5)2023 Feb 23.
Artículo en Inglés | MEDLINE | ID: mdl-36900210

RESUMEN

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.

5.
J Biomed Opt ; 27(11)2022 11.
Artículo en Inglés | MEDLINE | ID: mdl-36329004

RESUMEN

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.


Asunto(s)
Neoplasias de la Boca , Semántica , Humanos , Incertidumbre , Teorema de Bayes , Reproducibilidad de los Resultados , Redes Neurales de la Computación , Procesamiento de Imagen Asistido por Computador/métodos , Neoplasias de la Boca/diagnóstico por imagen
6.
Indian J Cancer ; 59(3): 442-453, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-36412324

RESUMEN

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.


Asunto(s)
Carcinoma de Células Escamosas , Neoplasias de Cabeza y Cuello , Enfermedades de la Boca , Neoplasias de la Boca , Lesiones Precancerosas , Humanos , Neoplasias de la Boca/diagnóstico , Neoplasias de la Boca/terapia , Neoplasias de la Boca/patología , Carcinoma de Células Escamosas/diagnóstico , Carcinoma de Células Escamosas/terapia , Carcinoma de Células Escamosas/patología , Lesiones Precancerosas/diagnóstico , Lesiones Precancerosas/terapia , Enfermedades de la Boca/patología , Carcinoma de Células Escamosas de Cabeza y Cuello
7.
J Biomed Opt ; 27(1)2022 01.
Artículo en Inglés | MEDLINE | ID: mdl-35023333

RESUMEN

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.


Asunto(s)
Aprendizaje Profundo , Neoplasias de la Boca , Atención , Humanos , Neoplasias de la Boca/diagnóstico por imagen , Redes Neurales de la Computación , Reproducibilidad de los Resultados
8.
Biomed Opt Express ; 12(10): 6422-6430, 2021 Oct 01.
Artículo en Inglés | MEDLINE | ID: mdl-34745746

RESUMEN

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.

9.
J Biomed Opt ; 26(10)2021 10.
Artículo en Inglés | MEDLINE | ID: mdl-34689442

RESUMEN

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.


Asunto(s)
Neoplasias de la Boca , Redes Neurales de la Computación , Algoritmos , Detección Precoz del Cáncer , Humanos , Aprendizaje Automático , Neoplasias de la Boca/diagnóstico por imagen
10.
Cancers (Basel) ; 13(14)2021 Jul 17.
Artículo en Inglés | MEDLINE | ID: mdl-34298796

RESUMEN

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.

11.
J Biomed Opt ; 26(6)2021 06.
Artículo en Inglés | MEDLINE | ID: mdl-34164967

RESUMEN

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.


Asunto(s)
Neoplasias de la Boca , Sistemas de Atención de Punto , Detección Precoz del Cáncer , Humanos , Neoplasias de la Boca/diagnóstico por imagen , Sensibilidad y Especificidad , Teléfono Inteligente
12.
Indian J Cancer ; 56(2): 107-113, 2019.
Artículo en Inglés | MEDLINE | ID: mdl-31062727

RESUMEN

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.


Asunto(s)
Agentes Comunitarios de Salud , Detección Precoz del Cáncer , Neoplasias de la Boca/diagnóstico , Telemedicina , Adolescente , Adulto , Femenino , Humanos , Masculino , Persona de Mediana Edad , Mucosa Bucal/patología , Neoplasias de la Boca/patología , Encuestas y Cuestionarios , Adulto Joven
13.
J Contemp Dent Pract ; 19(9): 1122-1128, 2018 Sep 01.
Artículo en Inglés | MEDLINE | ID: mdl-30287715

RESUMEN

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.


Asunto(s)
Teléfono Celular , Detección Precoz del Cáncer , Neoplasias de la Boca/diagnóstico , Población Rural , Telemedicina/tendencias , Femenino , Humanos , Incidencia , India/epidemiología , Masculino , Neoplasias de la Boca/diagnóstico por imagen , Neoplasias de la Boca/epidemiología , Neoplasias de la Boca/prevención & control , Prevalencia , Consulta Remota/métodos , Consulta Remota/tendencias , Factores de Riesgo , Telemedicina/métodos
14.
Indian J Dent Res ; 28(1): 66-70, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-28393820

RESUMEN

PURPOSE: Hounsfield unit (HU) provides a quantitative evaluation of bone density. The assessment of bone density is essential for successful treatment plan. Although, multislice computed tomography (MSCT) is considered as gold standard in evaluating bone density, cone-beam computed tomography (CBCT) is frequently used in dentomaxillofacial imaging due to lower radiation dose, less complex device, and images with satisfactory resolution. AIMS AND OBJECTIVES: The aim of this study is to determine and compare the gray value and HU value of hypodense and hyperdense structures on CBCT and MSCT, respectively. The study also evaluated and compared the gray values in different field of views within CBCT. MATERIALS AND METHODS: A total of 20 dry human mandibles were obtained. The gray values and HU values of hypodense structures (extraction socket, inferior alveolar canal, and mental foramen) and hyperdense structures (enamel, cancellous, and cortical bone) were evaluated and compared between CBCT and MSCT images, respectively. The obtained data were statistically analyzed. STATISTICAL ANALYSIS: One-way analyses of variance, ANOVA F-test. RESULTS: The gray value for hypodense structures in large volume CBCT scans resembled the HU value. The study showed statistically significant difference (P < 0.001) in gray values for all the hyperdense structures in CBCT when compared to HU values of MSCT scans. CONCLUSION: The gray value for hypodense structures in large volume CBCT scan was more reliable and analogous to HU value in MSCT. The determination of grey values in CBCT may not be as accurate as HU value in CT for hyperdense structures.


Asunto(s)
Tomografía Computarizada de Haz Cónico , Tomografía Computarizada Multidetector , Dosis de Radiación , Técnicas In Vitro
15.
Dent Res J (Isfahan) ; 11(3): 345-50, 2014 May.
Artículo en Inglés | MEDLINE | ID: mdl-25097644

RESUMEN

BACKGROUND: The present study was undertaken to estimate and compare the levels of plasma total cholesterol (TC), low density lipoprotein (LDL), high density lipoprotein (HDL), very low density lipoprotein (VLDL) and triglycerides in patients with oral precancerous lesions/conditions, oral cancer and normal subjects. MATERIALS AND METHODS: The study comprised of 60 patients with oral precancerous lesions/conditions, 60 patients with oral cancer and a control group of 60 healthy individuals. The diagnosis of oral precancerous lesions/conditions and oral cancer was confirmed histopathologically. Under aseptic condition 5 ml venous blood of overnight fasting patient was withdrawn from each individual. Serum was separated by centrifugation and plasma levels of TC, LDL, HDL, VLDL and triglycerides were estimated. Descriptive statistical analysis has been carried out in the present study. Analysis of variance has been used to find the significance of study parameters between three or more groups of patients, Post-hoc test as Tukey has been used to find the pair wise significance. Significance is assessed at 5% level of significance. RESULTS: Statistically significant decrease in levels of plasma TC, LDL, HDL, VLDL and triglycerides was observed in the precancerous and cancerous groups as compared to the control group. On comparison between precancerous and cancerous groups, significant decrease was observed in cancerous group. CONCLUSION: The change in lipid levels may have an early diagnostic or prognostic role in the oral premalignant lesions/conditions and oral cancer. The presence of decreased plasma lipid profile should increase the suspicion of these lesions to be investigated further.

16.
J Nat Sci Biol Med ; 5(1): 190-4, 2014 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-24678226

RESUMEN

Cemento-ossifying fibroma (COF) is classified as a fibro-osseous neoplasm and included among the non-odontogenic tumors derived from the mesenchymal blast cells of the periodontal ligament, with a potential for forming fibrous tissue, cementum and bone, or a combination of such elements. These are slow-growing lesions, and are more frequent in women between the third and fourth decades of life. Case reports of massive expansile COF, measuring more than 10 cm are rarely reported in the literature. We report a case of giant cemento-ossifying fibroma of the mandible in a 34 year old female patient.

17.
Asian Pac J Cancer Prev ; 13(9): 4409-12, 2012.
Artículo en Inglés | MEDLINE | ID: mdl-23167351

RESUMEN

Present study was undertaken to estimate and compare erythrocyte superoxide dismutase (E-SOD) and Glutathione peroxidase (GPx) levels in oral submucous fibrosis, oral leukoplakia and oral cancer patients and age/sex matched healthy subjects, 25 in each group. Statistically significant (P<0.001) decrease in E-SOD and GPx levels were observed in OSF, oral leukoplakia and oral cancer groups as compared to the control group. Oral leukoplakia group showed lower levels in comparison with OSF (P>0.05). Oral cancer group had the lowest levels amongst the study groups. Imbalance in antioxidant enzyme status may be considered as one of the factors responsible for the pathogenesis of cancer and may serve as a potential biomarker and therapeutic target to reduce the malignant transformation in oral premalignant lesions/conditions.


Asunto(s)
Biomarcadores de Tumor/sangre , Glutatión Peroxidasa/sangre , Leucoplasia Bucal/enzimología , Neoplasias de la Boca/enzimología , Fibrosis de la Submucosa Bucal/enzimología , Superóxido Dismutasa/sangre , Adulto , Estudios de Casos y Controles , Eritrocitos/enzimología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Adulto Joven
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...