Evaluation of EGFR in prognostic characterization in histopathological variants of oral squamous cell carcinoma.

AIM OF THE STUDY: Epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor of the Erb B family contributing to proliferation, invasion, and metastasis. EGFR overexpression is frequently associated with poor clinical outcome in malignant neoplasms.• To evaluate and compare immunoexpression of EGFR in histopathological variants of oral squamous cell carcinoma (OSCC).• To hypothesize the role of EGFR in determining biological behavior and prognostic course of histopathological variants of OSCC. MATERIALS AND METHODS: The study comprised a total of 40 cases including 10 cases each of Squamous cell carcinoma, Verrucous carcinoma, Adenosquamous cell carcinoma, and Adenoid squamous cell carcinoma. EGFR immunoexpression was observed qualitatively as low (1), moderate (2) and strong (3) and quantitatively as score 1 for <10%, 2 for 10%-50%, and 3 for >50% positive cells. The resulting data were analyzed using SPSS software version 19. Data have been expressed as mean and standard deviation. Differences between the different variables were analyzed using ANOVA, and Pearson's Chi-square. (p ≤ 0.05). RESULT: The study results revealed that the EFGR immunoexpression was highest in adenosquamous cell carcinoma followed by adenoid squamous cell carcinoma then conventional squamous cell carcinoma followed by lowest immunoexpression in verrucous carcinoma. The results were statistically significant. (p ≤ 0.05). CONCLUSION: Expression of EGFR could be established as a valuable biomarker with significant association in predicting aggressive potential and treatment response in various histopathological variants of OSCC. Further studies where EGFR could be linked to predictive indicators and tumor prognosis could be undertaken.

Luteinizing Hormone Dynamics in Menstruation.

Menstruation is a finely-controlled cycle that responds to the prevailing endocrine and paracrine environment. However, social stigma has led to inadequate menstrual literacy, both among academics and the larger public. The poorly understood mechanisms of menstruation ultimately lead to suboptimal healthcare treatment and services for biological females, culminating in a physical, financial, and emotional burden. Various hormones signal the beginning and end of each stage of menstruation. In particular, luteinizing hormone (LH) is a major player in ovulation, corpus luteum function, and the stimulation of other key hormones. A LH model could be used to understand the larger control system of menstruation if analyzed in conjunction with models for other major hormones (e.g., FSH, progesterone, and GnRH). Thus, exploring a smaller subsection of LH dynamics within the larger control system of menstruation can lead to a greater understanding of menstruation, contributing towards therapeutics and research for women's health. Using parameters and kinetic equations in the existing body of literature, a transfer function was derived to model LH dynamics. Analysis of system stability reveals overdamped dynamics in LH sensitization at baseline, and underdamped mildly resonant dynamics at the peak of the menstrual cycle, the strength of which depends on the values of the rate constants of LH receptor formation, binding, and desensitization.

Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET/CT Radiomics.

Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting nontumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating nontumor radiomics, including whole-body features, to predict treatment outcomes in patients with previously untreated locoregionally advanced cervical cancer. Methods: We analyzed 127 cervical cancer patients treated definitively with chemoradiotherapy and intracavitary brachytherapy. All patients underwent pretreatment whole-body 18F-FDG PET/CT. To quantify effects due to the tumor itself, the gross tumor volume (GTV) was directly contoured on the PET/CT image. Meanwhile, to quantify effects arising from the rest of the body, the planning target volume (PTV) was deformably registered from each planning CT to the PET/CT scan, and a semiautomated approach combining seed-growing and manual contour review generated whole-body muscle, bone, and fat segmentations on each PET/CT image. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. Ninety-five patients were used to train a Cox model of disease recurrence including both radiomic and clinical features (age, stage, tumor grade, histology, and baseline complete blood cell counts), using bagging and split-sample-validation for feature reduction and model selection. To further avoid overfitting, the resulting models were tested for generalization on the remaining 32 patients, by calculating a risk score based on Cox regression and evaluating the c-index (c-index > 0.5 indicates predictive power). Results: Optimal performance was seen in a Cox model including 1 clinical biomarker (whether or not a tumor was stage III-IVA), 2 GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), 1 PTV radiomic biomarker (major axis length), and 1 whole-body radiomic biomarker (CT bone root mean square). In particular, stratification into high- and low-risk groups, based on the linear risk score from this Cox model, resulted in a hazard ratio of 0.019 (95% CI, 0.004, 0.082), an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 (95% CI, 0.16, 0.83). Conclusion: Incorporating nontumor radiomic biomarkers can improve the performance of prognostic models compared with using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multiinstitutional cohorts.

Molecular Analysis as a Guide to Determining the Extent and Pathophysiology of Perilesional Tissues in Oral Epithelial Dysplasias.

INTRODUCTION: Clinically and histologically normal appearing perilesional mucosa of epithelial dysplasia may harbor early genetic changes. Hence, the present study is designed to determine the early molecular changes in the form of p16 and EGFR immunoexpressions in perilesional tissues. OBJECTIVES: To analyze immunohistochemical expressions of p16 and EGFR individually and percentage change of immunoexpressions in oral dysplastic lesions and their perilesional tissues. MATERIALS AND METHODS: Forty formalin-fixed paraffin-embedded tissues of oral epithelial dysplasia with perilesional tissue marked by India ink were included in this study. Immunohistochemical staining was performed using anti-p16 and anti-EGFR monoclonal antibodies (BioGenex) using squamous cell carcinoma of uterine cervix and breast carcinoma as the positive controls, respectively. RESULTS: p16 and EGFR expressions were assessed based on the presence, intensity, extent and immunolocalization of positive cells. Out of 40 cases, p16 immunoexpression was positive in 82.5% cases of lesional tissues and in 62.5% cases of perilesional tissues (p ≤ 0.05); however, EGFR immunoexpression was positive in 90% cases of both lesional and perilesional tissues (p > 0.05). CONCLUSION: The disease status and progression based on p16 and EGFR expressions and co-expressions can be used as an effective guide to evaluating the progression of normal epithelium to dysplastic epithelium in otherwise clinically normal mucosa.

Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients.

The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3-7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.