| Project/Area Number |
19592321
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Surgical dentistry
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| Research Institution | The Nippon Dental University |
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Principal Investigator |
YAGISHITA Hisao The Nippon Dental University, 生命歯学部, 准教授 (50256989)
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| Co-Investigator(Kenkyū-buntansha) |
IZUMO Toshiyuki 埼玉県立がんセンター, 臨床腫瘍研究所, 研究員 (80322709)
SHIMAZU Yoshihito 日本歯科大学, 生命歯学部, 講師 (10297947)
SATO Kaori 日本歯科大学, 生命歯学部, 講師 (90287772)
AOBA Takaaki 日本歯科大学, 生命歯学部, 教授 (30028807)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2009: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2008: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 口腔扁平上皮癌 / 浸潤先端 / 病理組織学 / 免疫組織化学 / 3次元構築 / 歯学 / 病理学 / 舌癌 / 立体観察 / 細胞異型 / 浸潤・転移 / 血管新生 / 診断基準 |
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| Research Abstract |
We initiated histology-based 3D reconstruction of oral squamous cell carcinoma (SCC) arising on the lateral border of the tongue in order to collect quantitative information of diagnostic value regarding cancer invasion and prognosis. The basic procedures for 3D reconstruction are: preparation of serial histological sections in combination with immunostaining of cell/tissue constituents of interest, alignment and superposition of digitized images, and computer-assisted color segmentation of labeled targets. Cytoplasm/nucleus segmentation of individual SCC cells lead to quantitative analysis of histological parameters. The results support the wide potential usage and advantage of histology-based 3D reconstruction in cancer biology understanding and pathological diagnosis. Particularly, 3D histomorphometric parameters regarding tumor alveolar structure and spatial distribution of cancer foci in a microenvironment will be of inexpensive value in prediction and diagnosis of tumor aggressiveness and malignancy.
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