Development of a Quantitative Method of Cancer Cell Diagnosis Based on the Fluorescence Image Analysis of the Paraffin Sections

该项目试图客观地提取癌细胞的病理特征，并基于核DNA荧光的显微图像分析（图像细胞术），使用石蜡切片开发“癌细胞诊断的定量方法”。我们使用我们开发的，计算机控制的显微图像处理系统配备了高灵敏度（单色）摄像机。我们首先用BASIC和C语言编制了计算机程序，研究了荧光图像密度线性定量等基本技术，然后对切片中随机切取的细胞体DNA进行碘化丙啶染色后，对每个核DNA荧光图像进行图像分析。用图像处理器和个人计算机进行形态测量，得到许多参数（多达70个;大小，形状，染色质），然后通过多变量，判别和统计分析进行处理，以确定 ...更多信息 客观地挖掘癌细胞和正常细胞（上皮细胞、成纤维细胞和淋巴细胞）。然后，我们将其应用于160多例人类胃癌和结肠癌的分析。这些癌症被选择为具有独特的多形性以研究特征性癌细胞。通过这些分析，我们初步选择了癌细胞的特征参数，并且进一步地，可以在单个细胞的基础上以0.75-0.93的概率定量地正确确定显微切片中的癌细胞。我们总结了这种方法的硬件和软件，以提出发展的“现场测试模型”，以帮助常规病理诊断。此外，基于本图像细胞术的研究现在正在进行中，用于1）改进软件（通过制作系统的、有效的程序结构并通过加速计算时间），2）分析具有轻微核分裂的癌症病例（如偶尔在早期癌症、边界病变等中看到的），3)4）“人工智能”技术在癌症诊断中的应用。少

The proposed project has attempted to extract objectively pathological features of the cancer cell, and to develop "a quantitative method of cancer cell diagnosis" using paraffin sections based on the microscopic image analysis of the nuclear DNA fluorescence (image cytometry). We used our developed, computer-controlled microscopic image processing system equipped with a high-sensitive (monochromatic) video-camera. At the beginning, we mad e computer programs in BASIC and C languages, and studied basic techniques concerning linear quantitation of the fluorescence image density, etc. After DNA staining with propidium iodide, we carried out image analysis of each nuclear DN A fluorescence figure which was obtained from the randomly cut cell body in the section. The morphometry, performed with both an image processor and a personal computer, yielded many parameters (up to 70; size, shape, chromatin), which were then processed by multivariate, discriminant and statistical analyses to deter … More mine objectively cancer cells and normal cells (epithelial, fibroblastic, and lymphocytic cells). We then applied it to the analysis of more than 160 cases of human gastric and colonic cancers. those cancers being chosen as having the distinctive pleomorphism to study the characteristic cancer cell. Through these analyses, we tentatively selected parameters characteristic for the cancer c ells, and further, could determine correctly cancer cells in the microscopic section in quantitative term with the probability of 0.75-0.93 on a single cell basis. We are summarizing this methodology for both hardware and software in order to propose the development of "the field-test models" in aid of the routine pathological diagnosis. Moreover, studies based on the present image cytometry, are now under way for 1)improving software (by making a systemic, efficient structure of the program and by speed-up of the computation time), 2)analyzing cancer cases with slight nuclear atypism (as occasionally seen in the early cancers, borderline lesions, etc.), 3)the quantitation of the degree of nuclear atypism, and 4)the technique of "artifical intelligence (AI)" in the cancer diagnosis. Less