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1.
J Oral Maxillofac Pathol ; 25(2): 258-265, 2021.
Article in English | MEDLINE | ID: mdl-34703119

ABSTRACT

INTRODUCTION: Pattern of invasion (POI) in scoring system of oral squamous cell carcinoma (OSCC) can predict local recurrence and overall survival rate. Argyrophilic nucleolar organizer region (AGNOR) counts are considered to reflect the biosynthetic and nucleolar activity of a cell and thus serve as an indicator of the rapidity of the cell cycle thereby indicating the proliferative index of the tumor. It is implied that higher tumor associated tissue eosinophilia (TATE) showed lesser venous invasion, lymph node metastasis and clinical recurrence. The aim of the study was to assess and evaluate the following criteria's: POI-1 to POI-4 as defined by Bryne et al. in OSCC, proliferative index by AgNOR stain and TATE with carbol chromotrope stain in OSCC, validity of POI by correlating the AgNOR proliferative index and TATE. MATERIALS AND METHODS: Forty samples of formalin fixed paraffin embedded tissue blocks diagnosed of OSCC were taken for the study. Three sections were taken from a single block and then the tissues were stained differently with H & E Stain, AgNOR stain and Carbol chromotrope stain. First section stained with H & E was observed for POI and grading was done according to Bryne's criteria. The second and third sections were stained with AgNOR stain and Carbol chromotrope stain for proliferative index and TATE. One way analysis of variance was used to test the significance. RESULTS: Mean AgNORs count increases gradually from type 1 to type 4, depicting the increase in the nucleolar proliferative index of the cells and was statistically significant. In the case of the mean eosinophilic count, type 1 shows the highest mean eosinophilic count and the count shows drastic decrease till type 3 and from type 3 to type 4 the decrease is more gradual and was statistically significant. CONCLUSION: The study validated that POI is a good predictor for prognosis and also can be included in grading OSCC along with routine histopathological criteria.

3.
J Cancer Res Ther ; 14(2): 361-367, 2018.
Article in English | MEDLINE | ID: mdl-29516920

ABSTRACT

INTRODUCTION: Oral squamous cell carcinoma (OSCC) accounts 94% of all malignant lesions in the oral cavity. In the assessment of OSCC, nowadays the WHO grading system has been followed widely but due to its subjectivity, investigators applied the sophisticated technique of computer-assisted image analysis in the grading of carcinoma in larynx, lungs, esophagus, and cervix to make it more objective. AIMS AND OBJECTIVES: Access, analyze, and compare the cellular area (CA); cytoplasmic area (Cyt A); nuclear area (NA); nuclear perimeter (NP); nuclear form factor (NF); and nuclear-cytoplasmic ratio (N/C) of the cells in different grades of OSCC. MATERIALS AND METHODS: Fifty OSCC cases were obtained and stained with hematoxylin and eosin which were graded according to the WHO classification. The sections were subjected to morphometric analysis to analyze all the morphometric parameters in different grades of OSCC and subjected to one-way ANOVA statistical analysis. RESULTS: CA and Cyt A decreased from normal mucosa with dedifferentiation of OSCC. The NA and NP increased in carcinoma group when compared to normal mucosa but decreased with dedifferentiation of OSCC (P < 0.05). NF had no significance with normal mucosa and different grades of OSCC (P > 0.05), while N/C ratio increased from normal mucosa through increasing grades of OSCC, reaching the highest value in poorly differentiated squamous cell carcinoma (P < 0.05). CONCLUSION: Both cellular and nuclear variables provide a more accurate indication of tumor aggressiveness than any single parameter. Morphometric analysis can be a reliable tool to determine objectively the degree of malignancy at the invasive tumor front.


Subject(s)
Carcinoma, Squamous Cell/diagnostic imaging , Carcinoma, Squamous Cell/pathology , Epithelial Cells/pathology , Image Processing, Computer-Assisted/methods , Mouth Mucosa/pathology , Mouth Neoplasms/diagnostic imaging , Mouth Neoplasms/pathology , Biomarkers, Tumor , Cell Nucleus/pathology , Humans , Image Interpretation, Computer-Assisted/methods , Immunohistochemistry , Microscopy/methods , Neoplasm Grading
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