Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients.

OBJECTIVES: Matching treatment intensity to tumor biology is critical to precision oncology for head and neck squamous cell carcinoma (HNSCC) patients. We sought to identify biological features of tumor cell multinucleation, previously shown by us to correlate with survival in oropharyngeal (OP) SCC using a machine learning approach. MATERIALS AND METHODS: Hematoxylin and eosin images from an institutional OPSCC cohort formed the training set (DTr). TCGA HNSCC patients (oral cavity, oropharynx and larynx/hypopharynx) formed the validation set (DV). Deep learning models were trained in DTr to calculate a multinucleation index (MuNI) score. Gene set enrichment analysis (GSEA) was then used to explore correlations between MuNI and tumor biology. RESULTS: MuNI correlated with overall survival. A multivariable nomogram that included MuNI, age, race, sex, T/N stage, and smoking status yielded a C-index of 0.65, and MuNI was prognostic of overall survival (2.25, 1.07-4.71, 0.03), independent of the other variables. High MuNI scores correlated with depletion of effector immunocyte subsets across all HNSCC sites independent of HPV and TP53 mutational status although the correlations were strongest in wild-type TP53 tumors potentially due to aberrant mitotic events and activation of DNA-repair mechanisms. CONCLUSION: MuNI is associated with survival in HNSCC across subsites. This may be driven by an association between high levels of multinucleation and a suppressive (potentially exhausted) tumor immune microenvironment. Mechanistic studies examining the link between multinucleation and tumor immunity will be required to characterize biological drivers of multinucleation and their impact on treatment response and outcomes.

Image analysis reveals differences in tumor multinucleations in Black and White patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma.

BACKGROUND: Understanding biological differences between different racial groups of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) patients, who have differences in terms of incidence, survival, and tumor morphology, can facilitate accurate prognostic biomarkers, which can help develop personalized treatment strategies. METHODS: This study evaluated whether there were morphologic differences between HPV-associated tumors from Black and White patients in terms of multinucleation index (MuNI), an image analysis-derived metric that measures density of multinucleated tumor cells within epithelial regions on hematoxylin-eosin images and previously has been prognostic in HPV-associated OPSCC patients. In this study, the authors specifically evaluated whether the same MuNI cutoff that was prognostic of overall survival (OS) and disease-free survival in their previous study, TTR , is valid for Black and White patients, separately. We also evaluated population-specific cutoffs, TB for Blacks and TW for Whites, for risk stratification. RESULTS: MuNI was statistically significantly different between Black (mean, 3.88e-4; median, 3.67e-04) and White patients (mean, 3.36e-04; median, 2.99e-04), with p = .0078. Using TTR , MuNI was prognostic of OS in the entire population with hazard ratio (HR) of 1.71 (p = .002; 95% confidence interval [CI], 1.21-2.43) and in White patients with HR of 1.72 (p = .005; 95% CI, 1.18-2.51). Population-specific cutoff, TW , yielded improved HR of 1.77 (p = .003; 95% CI, 1.21-2.58) for White patients, whereas TB did not improve risk-stratification in Black patients with HR of 0.6 (p = .3; HR, 0.6; 95% CI, 0.2-1.80). CONCLUSIONS: Histological difference between White and Black patient tumors in terms of multinucleated tumor cells suggests the need for considering population-specific prognostic biomarkers for personalized risk stratification strategies for HPV-associated OPSCC patients.

Characterization of alginate-capped nanosilver by fractal and entropy analysis on its transmission electron microphotographs.

The study employs conventional techniques and quantitative image analysis tools to characterize alginate-capped nanosilver synthesized by green methods. Sodium Alginate (0.5 %, 1 % and 2 %) was used as a reducing and stabilizing agent. Presence of particles was confirmed by UV-vis Spectroscopy, with absorbance maxima of 412-413 nm for 0.5 %, 1 % and 2 % of polymer. Hydrodynamic sizes of particles recorded for 0.5 %, 1 % and 2 % polymer were 128.4 ± 1.5, 129.9 ± 3.6 and 148.6 ± 1.0 nm by DLS. TEM revealed roughly spherical to cuboidal particles ranging from 15-20 nm and clusters of 100 nm and Energy Dispersive X-ray Spectroscopy confirmed the presence of silver in the particles. Analysis of the TEM images was done in MATLAB R2016b using histogram equalisation for image enhancement and entropy filtering for image segmentation. These techniques revealed the surface pores and polymer distribution around the particle. Statistical analysis (ANOVA) was performed for the measured fractal dimensions of nanoparticles with polymer coating, width of particle together with polymer coating, and thickness of only polymer coating around the particle for various study groups. Significant differences (p < 0.05) were found both between and within the study groups for fractal dimensions of nanoparticles with polymer coating, width of nanoparticles and thickness of polymer coating alone. The analysis was successful in confirming presence and thickness of polymer layer on particles.

Epithelial Distribution of E-Cadherin, p63, and Mitotic Figures in ApoTome Images to Determine the Oncogenic Potentiality of Oral Submucous Fibrosis.

The exact process of the malignant conversion of oral submucous fibrosis (OSF) to oral cancer is not fully understood. This study aimed to detect and analyze E-cadherin expression, p63 expression, and number of mitotic figures, all correlated to cancer development, in ApoTome images of oral tissues to determine the oncogenic potentiality of OSF. ApoTome images of the study groups (6 normal, 16 OSF with dysplasia, and 10 OSF without dysplasia) were recorded. Cytoplasmic and membranous E-cadherin expression, breakages of the cell membrane, and p63 expression were detected in MATLAB 2016b. The number of mitotic figures detected by MATLAB was correlated with the number of chromosomes detected by ImageJ. A Mann­Whitney U test was done to determine a significant difference between the study groups for cytoplasmic and membranous E-cadherin distribution points. Statistical significant differences were found for cytoplasmic E-cadherin distribution between normal and OSF (with dysplasia) (p = 0.0278). There was an increase in mitotic figures, p63 expression, and cytoplasmic E-cadherin expression and a decrease in membranous E-cadherin expression from normal to diseased condition. Hence, automated detection and quantification of E-cadherin, p63, and mitotic figures in ApoTome images of oral biopsies can help in determining the oncogenic potentiality of OSF.

Segmentation and analysis of surface characteristics of oral tissues obtained by scanning electron microscopy to differentiate normal and oral precancerous condition.

Abnormal epithelial stratification is a sign of oral dysplasia and hence evaluation of surface characteristics of oral epithelial region can help in detection of cancerous progression. Surface characteristics can be better visualised by Scanning Electron Microscopy (SEM) in comparison to light microscopy. In our study we have developed automated image processing algorithms i.e. Gaussian with median filtering and Gradient filtering, using MATLAB 2016b, to segment the surface characteristics i.e. the ridges and pits in the SEM images of oral tissue of normal (13 samples) and Oral Submucous Fibrosis (OSF) (36 samples) subjects. After segmentation, quantitative measurement of the parameters like area, thickness and textural features like entropy, contrast and range filter of ridges as well as area of pit and the ratio of area of ridge vs. area of pit was done. Statistical significant differences were obtained in between normal and OSF study groups for thickness (p=0.0107), entropy (p<0.00001) and contrast of ridge (p<0.00001) for Gaussian with median filtering and for all the parameters except thickness of the ridge(p=1.386), for Gradient filtering. Thus, computer aided image processing by Gradient filter followed by quantitative measurement of the surface characteristics provided precise differentiation between normal and precancerous oral condition.

Analysis of images for detection of oral epithelial dysplasia: A review.

This paper provides a review of various image analysis approaches that have been previously used for recognition of dysplasia in images of the epithelium of the oral cavity. This domain has become especially admissible with the uncovering of the importance of image analysis which can probably be an aid to subjective diagnosis by histopathologists. Oral malignancy is a rampant form of cancer found among people of the Indian subcontinent due to various deleterious habits like consumption of tobacco, areca nut, betel leaf etc. Oral Submucous Fibrosis, a precancer, whose pathological category falls between normal epithelium and epithelial dysplasia, is caused because of these habits and can ultimately lead to oral cancer. Hence early detection of this condition is necessary. Image analysis methods for this purpose have an enormous potential which can also reduce the heavy workload of pathologists and to refine the criterion of interpretation. This paper starts with a critique of statistics of oral carcinoma in India and distribution of cancer in intra-oral sites and moves on to its causes and diagnostic approaches including causative agents, problems in curative approach and importance of image analysis in cancer detection. The various image analysis methods to appraise the cytological and architectural changes accompanied by Oral Epithelial Dysplasia in the images of the oral epithelial region have been described in relation to 2005 WHO Classification System and it was found that in future, analysis of images based on the mentioned methods has the potential in better interpretation and diagnosis of oral carcinoma.