Ranjan Rashmi Paul

A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions.

Quantitative Analysis of Histopathological Features of Precancerous Lesion and Condition Using Image Processing Technique

This paper aims at quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. The algorithm involves median and low pass filtering, segmentation by adaptive region growing, optimal and local thresholding, morphological operations such as opening and closing of gray scale and binary images and some numerical methods. Differentiation on the basis of type and level of precancerous type or condition is carried out based on image marker, defined as a vector of cancer related features viz. length and curvature of radius of rete-ridges and papillae, population density of cells within epithelium, etc. Implementation of presented algorithms is done in MATLAB. The results support quantitative analysis of pathological condition in respect with progression towards malignancy. This analysis may help in developing automated analysis tool

Assessment of malignant potential of oral submucous fibrosis through evaluation of p63, E-cadherin and CD105 expression

Background The assessment of malignant potential of oral submucous fibrosis grades vis-à-vis their progression towards malignancy is associated with expression of possible multiple molecular markers. Aims To analyse p63, E-cadherin and CD105 expression in this premalignant pathosis with a view to unravel and understand the expression of these molecules as markers. Methods The oral mucosal biopsies (normal, oral submucous fibrosis with and without dysplasia) were studied with routine H&E, and by immunohistochemistry for p63, E-cadherin and CD105 expression. p63 was assessed as percentage of positive nuclei. E-cadherin expression was estimated through (i) distance between basement membrane and E-cadherin expression initiation point, (ii) ratio between epithelial thickness and epithelial thickness displaying E-cadherin, and (iii) E-cadherin intensity variation along the expression path. CD105 expression was assessed qualitatively. Results The p63+ cells were highest in severely dysplastic tissues followed by other dysplastic grades, normal oral mucosa and non-dysplastic conditions. However, the p63+ cells displayed the feature of progressive maturation only in normal mucosa. There was a loss of membranous E-cadherin in basal layers of all diseased conditions; it was highest in severe dysplasia. There was significant variation (p<0.0001) in E-cadherin intensity within and between the tissues (normal and diseased). CD105 expression increased abruptly in dysplasia. Conclusions The malignant potential of this pre-cancerous condition is likely to be correlated with an increase in p63 and CD105 expression and a concomitant loss of membranous E-cadherin. This may lead to marker identification through greater validation.

Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-An SVM based approach

Quantitative evaluation of histopathological features is not only vital for precise characterization of any precancerous condition but also crucial in developing automated computer aided diagnostic system. In this study segmentation and classification of sub-epithelial connective tissue (SECT) cells except endothelial cells in oral mucosa of normal and OSF conditions has been reported. Segmentation has been carried out using multi-level thresholding and subsequently the cell population has been classified using support vector machine (SVM) based classifier. Moreover, the geometric features used here have been observed to be statistically significant, which enhance the statistical learning potential and classification accuracy of the classifier. Automated classification of SECT cells characterizes this precancerous condition very precisely in a quantitative manner and unveils the opportunity to understand OSF related changes in cell population having definite geometric properties. The paper presents an automated classification method for understanding the deviation of normal structural profile of oral mucosa during precancerous changes.

HnRNP E2 is downregulated in human oral cancer cells and the overexpression of hnRNP E2 induces apoptosis.

Human hnRNP genes have been reported to be involved in human malignancies and several hnRNPs are promising biomarkers of lung, head and neck, colon, breast, and pancreatic cancers. The present study investigated the clinicopathologic and biological significance of hnRNP E2 gene expression in oral cancer. Human hnRNP E2 was significantly downregulated in oral cancer tissues compared to normal one (P < 0.0001) as determined by quantitative real‐time reverse transcription PCR. The expression of hnRNP E2 is correlated with histology, being lower in moderate and poorly differentiated squamous cell carcinoma (SCC) compared to well‐differentiated SCC. Transient transfection of hnRNP E2 in cancerous cell lines resulted in reduced cell viability and increased apoptotic nuclei. Compared to control transfectants, cells with higher expression showed an increase in the number of apoptotic cells by annexin‐PI staining and an increase in caspase activity. The present study thus implicates downregulation of hnRNP E2 as a novel mechanism to enhance the resistance of cancer cells to apoptosis.

A quest for miRNA bio-marker: a track back approach from gingivo buccal cancer to two different types of precancers.

Deregulation of miRNA expression may contribute to tumorigenesis and other patho-physiology associated with cancer. Using TLDA, expression of 762 miRNAs was checked in 18 pairs of gingivo buccal cancer-adjacent control tissues. Expression of significantly deregulated miRNAs was further validated in cancer and examined in two types of precancer (leukoplakia and lichen planus) tissues by primer-specific TaqMan assays. Biological implications of these miRNAs were assessed bioinformatically. Expression of hsa-miR-1293, hsa-miR-31, hsa-miR-31* and hsa-miR-7 were significantly up-regulated and those of hsa-miR-206, hsa-miR-204 and hsa-miR-133a were significantly down-regulated in all cancer samples. Expression of only hsa-miR-31 was significantly up-regulated in leukoplakia but none in lichen planus samples. Analysis of expression heterogeneity divided 18 cancer samples into clusters of 13 and 5 samples and revealed that expression of 30 miRNAs (including the above-mentioned 7 miRNAs), was significantly deregulated in the cluster of 13 samples. From database mining and pathway analysis it was observed that these miRNAs can significantly target many of the genes present in different cancer related pathways such as “proteoglycans in cancer”, PI3K-AKT etc. which play important roles in expression of different molecular features of cancer. Expression of hsa-miR-31 was significantly up-regulated in both cancer and leukoplakia tissues and, thus, may be one of the molecular markers of leukoplakia which may progress to gingivo-buccal cancer.

Epithelio-mesenchymal transitional attributes in oral sub-mucous fibrosis.

Evaluating molecular attributes in association with its epithelial and sub-epithelial changes of oral sub-mucous fibrosis is meaningful in exploring the plausibility of an epithelio-mesenchymal transition (EMT) and malignant potentiality of this pathosis. In this study histopathological and histochemical attributes for basement membrane and connective tissue in biopsies of oral sub-mucous fibrosis (n = 55) and normal oral mucosa (n = 16) were assessed and expressions of p63, E-cadherin, β-catenin, N-cadherin and TWIST were analyzed immunohistochemically. The p63 and its isoforms (TA and ∆N), PARD3, E-cadherin and β-catenin were also assessed transcriptomically by q-PCR and EMT players like TWIST1, ZEB1, MMP9 and micro-RNA 205 were searched in gene expression microarrays. Oral epithelium demonstrating impairment in progressive maturation in oral sub-mucous fibrosis concomitantly experienced an increase in basement membrane thickness and collagen deposition along with alteration in target molecular expressions. In comparison to non-dysplastic conditions dysplastic stages exhibited significant increase in p63 and p63∆N expressions whereas, E-cadherin and β-catenin exhibited loss from the membrane with concurrent increase in cytoplasm. Further the N-cadherin and TWIST were gained remarkably along with the appearance of nuclear accumulation features of β-catenin. The microarray search had noticed the up-regulation of TWIST1, ZEB1 and MMP9 along with down regulation of micro-RNA 205. The simultaneous increase in basement membrane thickness and sub-epithelial collagen deposition were the plausible indicators for increased matrix stiffness with expected impact on oral epithelial functional homoeostasis. This was corroborated with the increase in expressions of epithelial master regulator p63 and its oncogenic isoform (∆N) along with membranous loss of E-cadherin (EMT hallmark) and its associate β-catein and gain of mesenchymal markers like N-cadherin and TWIST. These also became indicative for the induction of epithelial to mesenchymal transitional mechanism in oral sub-mucous fibrosis when connoted here with the relevant modulation in expressions of EMT regulators.

Hybrid segmentation, characterization and classification of basal cell nuclei from histopathological images of normal oral mucosa and oral submucous fibrosis

This work presents a quantitative microscopic approach for discriminating oral submucous fibrosis (OSF) from normal oral mucosa (NOM) in respect to morphological and textural properties of the basal cell nuclei. Practically, basal cells constitute the proliferative compartment (called basal layer) of the epithelium. In the context of histopathological evaluation, the morphometry and texture of basal nuclei are assumed to vary during malignant transformation according to onco-pathologists. In order to automate the pathological understanding, the basal layer is initially extracted from histopathological images of NOM (n=341) and OSF (n=429) samples using fuzzy divergence, morphological operations and parabola fitting followed by median filter-based noise reduction. Next, the nuclei are segmented from the layer using color deconvolution, marker-controlled watershed transform and gradient vector flow (GVF) active contour method. Eighteen morphological, 4 gray-level co-occurrence matrix (GLCM) based texture features and 1 intensity feature are quantized from five types of basal nuclei characteristics. Afterwards, unsupervised feature selection method is used to evaluate significant features and hence 18 are obtained as most discriminative out of 23. Finally, supervised and unsupervised classifiers are trained and tested with 18 features for the classification between normal and OSF samples. Experimental results are obtained and compared. It is observed that linear kernel based support vector machine (SVM) leads to 99.66% accuracy in comparison with Bayesian classifier (96.56%) and Gaussian mixture model (90.37%).

Structural markers for normal oral mucosa and oral sub-mucous fibrosis

This article presents a quantitative approach for the characterization of normal oral mucosa (NOM) in respect to thickness and textural properties of its entire epithelial layer. Histological images of oral mucosa depict that both thickness and tissue architecture at cellular and tissue level undergo change, as mucosa converts from normal to precancerous or cancerous state. In this study the thickness and fractal dimension of the mucosal epithelium of NOM and oral sub-mucous fibrosis (OSF) condition have been computed using 83 normal and 29 OSF images of oral mucosa. The result shows significant delineation between NOM and OSF in respect of both the epithelial thickness (in microm) and fractal dimensions. This quantitative characterization of oral epithelium will be of immense help for oral onco-pathologists and researchers to assess the biological nature of normal and diseased (OSF) mucosa with higher accuracy. Moreover, further differential applications may enable them to find out newer accurate quantitative diagnostic procedures to that of the usual histopathological gold standard for the assessment of malignant potentiality.

Altered elemental profile as indicator of homeostatic imbalance in pathogenesis of oral submucous fibrosis

Oral submucous fibrosis (OSF) is a potential precancerous condition of the oral cavity and oropharynx. The etiopathogenesis of this complex precancerous condition is still obscure. In addition to deleterious oral habits, malnutrition, and possible genetic predisposition, altered bioelemental status is also likely to play an important role in its pathogenesis. The present study analyzed 68 elements by inductively coupled plasma-mass spectroscopy in oral mucosa of normal and OSF individuals and some interesting alterations in elemental profile in the diseased tissue have been noted, indicating a homeostatic imbalance. These bioelemental alterations leading to homeostatic imbalance might be considered as an important biological event in the pathogenesis of OSF.