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

Accuracy of Magnetic Resonance Imaging in Evaluating the Depth and Level of Invasion of Buccal Carcinoma: A Prospective Cohort Study

PURPOSE

This study evaluated the accuracy of magnetic resonance imaging (MRI) in determining the depth and level of invasion of buccal carcinoma.

METHODS

Patients with buccal squamous cell carcinoma diagnosed pathologically from July 2016 to December 2019 were included. The depth of invasion (DOI) and level of invasion (LOI) were evaluated by MRI, intraoperative specimens and pathological sections. Statistical analyses were performed using IBM SPSS software version 25.0 (IBM Corp., Armonk, NY).

RESULTS

Forty-nine patients were ultimately included. The overall difference in DOIs between MRI and pathological sections (DMP) was 5.55 ± 2.40 mm, and T category correlated with the differences in DOI measurement and LOI assessment. The threshold value of DOI by MRI to identify lymph node metastasis was 8.5 mm, and that for OS and disease-specific survival (DSS) was 14.1 mm for both. Buccinator invasion on MRI correlated with OS and DSS.

CONCLUSION

Tumors with MRI-derived DOIs larger than 8.5 mm deserve simultaneous neck dissection at initial surgery. Buccinator invasion was found to be an independent prognostic factor for buccal carcinoma patients.

Multifractal Alterations in Oral Sub-Epithelial Connective Tissue During Progression of Pre-Cancer and Cancer

Bright-field microscopy (BFM) encrypts the optical transillumination profile of the transmitted light attenuated by the complex micro-structural tissue convolutions, manifested by the dense and compact regions of the specimen under examination. The connotations of idiosyncratic tissue interaction dynamics with the onset of pre-cancerous activity are encoded in the BFM acquired oral mucosa histopathological images (OMHI). In the present study, our analysis is focused on the sub-epithelium region of the oral mucosa, which has high clinical significance but sparsely explored in the literature from the textural domain. Histopathology being the gold-standard technique till date, we have used the light microscopic histopathology images for tissue characterization. The tissue-index transmission patches (TITP) from the sub-epithelium region are cropped under the guidance of oral onco-pathologists. After that, the TITPs are characterized for its multi-scale spatial-deformation dynamics, while keeping the intrinsic anisotropic geometry, and local contour connectivity within tolerable limits. With recent studies exhibiting multifractal's potency in diverse biological system analysis, here, we exploit the 2D multifractal detrended fluctuation analysis (2D-MFDFA) on TITPs for exploring a discriminative set of multifractal signatures for healthy, oral potentially malignant disorders and oral cancer tissue sample. The predictive model's competency is validated on an experimentally collected corpus of TITP samples and substantiated via confirmatory data statistics and analysis, showing its inter-class segregation efficacy. Moreover, the 2D-MFDFA analysis evinces the complex multifractal patterns in TITPs, which is due to the presence of composite long-range correlations in the oral mucosa tissue fabric.

Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions. However, this technique is limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the classification accuracy based on textural features in the development of a computer assisted screening of OSF. The approach introduced here is to grade the histopathological tissue sections into normal, OSF without Dysplasia (OSFWD) and OSF with Dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The biopsy sections are stained with H&E. The optical density of the pixels in the light microscopic images is recorded and represented as matrix quantized as integers from 0 to 255 for each fundamental color (Red, Green, Blue), resulting in a M×N×3 matrix of integers. Depending on either normal or OSF condition, the image has various granular structures which are self similar patterns at different scales termed "texture". We have extracted these textural changes using Higher Order Spectra (HOS), Local Binary Pattern (LBP), and Laws Texture Energy (LTE) from the histopathological images (normal, OSFWD and OSFD). These feature vectors were fed to five different classifiers: Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (K-NN), Radial Basis Probabilistic Neural Network (RBPNN) to select the best classifier. Our results show that combination of texture and HOS features coupled with Fuzzy classifier resulted in 95.7% accuracy, sensitivity and specificity of 94.5% and 98.8% respectively. Finally, we have proposed a novel integrated index called Oral Malignancy Index (OMI) using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number. We hope that this OMI can help the clinicians in making a faster and more objective detection of benign/malignant oral lesions.

Automated Diagnosis of Oral Cancer Using Higher Order Spectra Features and Local Binary Pattern: A Comparative Study

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The objective of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, the approach introduced is used to grade the histopathological tissue sections into normal, OSF without dysplasia (OSFWD) and OSF with dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The main objective of this work is to evaluate the use of Higher Order Spectra (HOS) features and Local Binary Pattern (LBP) features extracted from the epithelial layer in classifying normal, OSFWD and OSFD. For this purpose, we extracted twenty three HOS features and nine LBP features and fed them to a Support Vector Machine (SVM) for automated diagnosis. One hundred and fifty eight images (90 normal, 42 OSFWD and 26 OSFD images) were used for analysis. LBP features provide a good sensitivity of 82.85% and specificity of 87.84%, and the HOS features provide higher values of sensitivity (94.07%) and specificity (93.33%) using SVM classifier. The proposed system, can be used as an adjunct tool by the onco-pathologists to cross-check their diagnosis.

Computer vision approach to morphometric feature analysis of basal cell nuclei for evaluating malignant potentiality of oral submucous fibrosis

This research work presents a quantitative approach for analysis of histomorphometric features of the basal cell nuclei in respect to their size, shape and intensity of staining, from surface epithelium of Oral Submucous Fibrosis showing dysplasia (OSFD) to that of the Normal Oral Mucosa (NOM). For all biological activity, the basal cells of the surface epithelium form the proliferative compartment and therefore their morphometric changes will spell the intricate biological behavior pertaining to normal cellular functions as well as in premalignant and malignant status. In view of this, the changes in shape, size and intensity of staining of the nuclei in the basal cell layer of the NOM and OSFD have been studied. Geometric, Zernike moments and Fourier descriptor (FD) based as well as intensity based features are extracted for histomorphometric pattern analysis of the nuclei. All these features are statistically analyzed along with 3D visualization in order to discriminate the groups. Results showed increase in the dimensions (area and perimeter), shape parameters and decreasing mean nuclei intensity of the nuclei in OSFD in respect to NOM. Further, the selected features are fed to the Bayesian classifier to discriminate normal and OSFD. The morphometric and intensity features provide a good sensitivity of 100%, specificity of 98.53% and positive predicative accuracy of 97.35%. This comparative quantitative characterization of basal cell nuclei will be of immense help for oral onco-pathologists, researchers and clinicians to assess the biological behavior of OSFD, specially relating to their premalignant and malignant potentiality. As a future direction more extensive study involving more number of disease subjects is observed.