| Co-Investigator(Kenkyū-buntansha) |
KONISHI Eiichi KYOTO PREF. UNIV. MED., PATH., ASSISTANT, 医学部・病理, 助手 (50186714)
MURATA Shin-ichi KYOTO PREF. UNIV. MED., PATH., ASSISTANT, 医学部・病理, 助手 (20229991)
URATA Yoji KYOTO PREF. UNIV. MED., PATH., ASSIS. PROF., 医学部・病理, 講師 (30143944)
|
|---|
| Research Abstract |
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
|
