Tripodi S, Chang F, Syrjänen S, Shen Q, Cintorino M, Alia L, Santopietro R, Tosi P, Syrjänen K
Department of Pathology, University of Siena, via delle Scotte 6, I-53100 Siena, Italy.
Anticancer Res. 2000 Sep-Oct;20(5C):3855-62.
Despite much research effort, the major prognostic factor of oesophageal squamous cell cancer (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.
Among the cohort of 700 ESCC patients from the high-incidence area of China, previously subjected to extensive testing for Human papillomavirus (HPV) involvement by in situ hybridization (ISH) and PCR, a group of 273 patients was randomly selected for analysis of the primary tumour, adjacent mucosa and regional lymph nodes, by histopathology and quantitative image analysis. All these and the HPV data were subjected to extensive univariate and multivariate analysis to disclose independent predictors of progressive disease.
For the analyses, the tumours 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 (considered as the surrogate marker of progressive disease) 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 (stepwise backward LR) analysis, 6 variables remained as independent predictors of disease progression (at p < 0.05 level), the three most significant ones being nuclear perimeter, nuclear roundness and equivalent diameter (p < 0.01).
A series of quantitatively measured nuclear parameters seem to bear 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.
尽管进行了大量研究，但食管鳞状细胞癌（ESCC）的主要预后因素仍是TNM分类所定义的疾病病理分期，而肿瘤分级在这方面价值有限，主要是因为其重复性低。迫切需要基于对发病机制的更好理解找到一种更好的疾病预后评估方法。
在中国食管癌高发区的700例ESCC患者队列中，这些患者先前已通过原位杂交（ISH）和聚合酶链反应（PCR）对人乳头瘤病毒（HPV）感染情况进行了广泛检测，从中随机选取273例患者，通过组织病理学和定量图像分析对其原发性肿瘤、相邻黏膜和区域淋巴结进行分析。所有这些数据以及HPV数据都进行了广泛的单因素和多因素分析，以揭示疾病进展的独立预测因素。
为进行分析，肿瘤被分为两类：高-中分化型和低分化型。通过ISH在116例（18.9%）癌组织中检测到HPV DNA，通过PCR检测到的比例为15.2%。在单因素分析中，以下核参数可显著（p<0.01）预测淋巴结状态（被视为疾病进展的替代标志物）：核面积、G0/G1比率、HPV DNA状态、积分光密度（IOD）、平均光密度（MOD）和S期。在多因素（逐步向后逻辑回归）分析中，有6个变量作为疾病进展的独立预测因素保留下来（p<0.05水平），其中最显著的三个变量是核周长、核圆度和等效直径（p<0.01）。
在单因素分析中，一系列定量测量的核参数似乎与ESCC的分化和进展密切相关，并且在多因素建模中，其中一些变量也被证明是疾病进展的重要独立预测因素。这些数据明确支持在寻找ESCC额外预后因素时使用定量图像分析。
