Morphological, quantitative image analysis and molecular biology of oesophageal squamous cell carcinoma (ESCC), with special reference to tumor progression and human papillomavirus (HPV) involvement

By, Lutfi ALIA

Abstract

It is an urgent need to improve early diagnosis of ESCC, beacauses is one of most agressive cancer. The quantitative image analysis, can aid in the identification of ESCC. Despite much research effort, the major prognostic factor of ESCC remains the pathological stage of the disease as defined by the TNM classification, whereas tumour grading is of limited value in this respect, mainly due to its low reproducibility. A better means for disease prognostication based on improved understanding of the pathogenetic mechanisms is urgently required. Materials and methods: The material of the present study was derived from a series of 1876 oesophageal surgical specimens taken from a total of 700 patients, who underwent oesophageal resection for an invasive ESCC in Anyang Tumour Hospital, Henan Province of China. Among. The cases of ESCC, previously subjected to extensive testing for Human Papillomavirus (HPV) involvement and expression of p53 gene. All cases are analysed by histopathology and by in situ hybridisation (ISH) and PCR, and a group of 272 patients was randomly selected for analysis of the primary tumour, adjacent mucosa and regional lymph nodes, in the quantitative image analysis. All cases and HPV data were subjected to extensive univariate and multivariate analysis to disclose independent predictors of progressive disease. Results: For the analyses, the ESCCs were graded into two categories: well – moderately and poorly-differentiated. HPV DNA was detected in 116 (18.9 %) of the carcinomas by ISH and in 15.2 % by PCR. In univariate analysis, lymph node status was significantly (p < 0.01) predicted by the following nuclear parameters: nuclear area, G0/G1 ratio, HPV DNA status, integrated optical density (IOD), mean optical density (MOD) and S-Phase. In multivariate analysis, 6 variables remained as independent predictors of disease progression (p < 0.05 level), the three most significant ones being nuclear perimeter, nuclear roundness and equivalent diameter (p < 0.01). Conclusions: A series of quantitatively measured nuclear parameters seem to be a close correlation with ESCC differentiation and progression in univariate analysis and some of these variables proved to be significant independent predictors of disease progression in multivariate modelling as well. These data clearly advocate the use of quantitative image analysis in searching for additional prognostic factors of ESCC.

How to cite: Alia, L. (2020). Morphological, Quantitative Image Analysis and molecular biology of oesophageal squamous cell carcinoma (ESCC), with special reference to tumor progression and human papillomavirus (HPV) involvement. Medicus, 4(2), 37–51.

https://doi.org/10.58944/jeyc1034