{"_buckets": {"deposit": "01433e6c-8647-40f2-902a-78c2be19145c"}, "_deposit": {"id": "29349", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "29349"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00029349"}, "item_9_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2020-02", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "1", "bibliographicPageEnd": "31", "bibliographicPageStart": "25", "bibliographicVolumeNumber": "82", "bibliographic_titles": [{"bibliographic_title": "Nagoya Journal of Medical Science"}]}]}, "item_9_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Micro-computed tomography (\u03bcCT) provides extremely high-resolution images of samples and can be employed as a non-destructive inspection tool. Using \u03bcCT, we can obtain images comparable with microscopic images. In this work, we have attempted to take high-resolution images of the human lung using \u03bcCT. Compared to clinical high-resolution computed tomography (HRCT) images of living body (in-vivo imaging), we can obtain extremely high-resolution images by \u03bcCT of ex-vivo tissues (resected lungs) as three-dimensional data. The purpose of this study was to distinguish between areas of normal lung and lung cancer by \u03bcCT images in order to study the feasibility of cancer diagnosis using this novel radiological image modality. Ten resected human lungs containing primary cancer were fixed by Heitzman\u2019s methods to obtain high-resolution \u03bcCT images. After fixation of the lung, images of the specimens were taken by \u03bcCT between January 2016 and November 2017. The imaging conditions were tube voltage: 90 kV and tube current: 110 \u03bcA. To compare details of images gained by conventional HRCT and \u03bcCT, we measured the thickness of the alveolar walls of the normal lung area and the cancer area of which alveoli might be replaced by tumor cells, and compared their appearance by means of histopathological images. All the nodules were diagnosed as adenocarcinoma. The median whole tumor size was 18 mm (9 mm\u201324 mm). Each specimen was clearly divided into areas of normal alveolar wall and of thickened alveolar wall on \u03bcCT \u2018visually\u2019. Median thickness of alveolar walls of the normal lung was 0.037 mm (0.034 mm\u20130.048 mm), and that of the cancer area was 0.084 mm (0.074 mm\u20130.094 mm); there was a statistically significant difference between both thicknesses by Student\u2019s t-test (P \u003c 0.01). The area of thickened alveolar walls on \u03bcCT corresponded well with the area of microscopically lepidic growth patterns of adenocarcinoma. We found that \u03bcCT images could be correctly divided by alveolar walls into normal lung area and lung cancer area. Further detailed investigations with regard to \u03bcCT are needed to make comparable histological diagnoses using \u03bcCT images with conventional microscopic methods of pathological diagnoses.", "subitem_description_type": "Abstract"}]}, "item_9_description_5": {"attribute_name": "\u5185\u5bb9\u8a18\u8ff0", "attribute_value_mlt": [{"subitem_description": "This work was supported by JSPS KAKENHI Grant Number 15H01116 Grant-in-Aid for Scientific Research on Innovative Areas.", "subitem_description_type": "Other"}]}, "item_9_identifier_registration": {"attribute_name": "ID\u767b\u9332", "attribute_value_mlt": [{"subitem_identifier_reg_text": "10.18999/nagjms.82.1.25", "subitem_identifier_reg_type": "JaLC"}]}, "item_9_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "Nagoya University Graduate School of Medicine, School of Medicine"}]}, "item_9_relation_43": {"attribute_name": "\u7570\u7248\u3067\u3042\u308b", "attribute_value_mlt": 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Kensaku"}], "nameIdentifiers": [{"nameIdentifier": "96280", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Iwano, Shingo"}], "nameIdentifiers": [{"nameIdentifier": "96281", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Kawaguchi, Koji"}], "nameIdentifiers": [{"nameIdentifier": "96282", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Fukui, Takayuki"}], "nameIdentifiers": [{"nameIdentifier": "96283", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hakiri, Shuhei"}], "nameIdentifiers": [{"nameIdentifier": "96284", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ozeki, Naoki"}], "nameIdentifiers": [{"nameIdentifier": "96285", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Oda, Masahiro"}], "nameIdentifiers": [{"nameIdentifier": "96286", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yokoi, Kohei"}], "nameIdentifiers": 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{"subitem_subject": "Micro-computed tomography", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Three dimensional reconstruction", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Pathological diagnosis", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "departmental bulletin paper", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Micro-computed tomography images of lung adenocarcinoma: detection of lepidic growth patterns", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Micro-computed tomography images of lung adenocarcinoma: detection of lepidic growth patterns"}]}, "item_type_id": "9", "owner": "1", "path": ["499/508/509/2478"], "permalink_uri": "https://doi.org/10.18999/nagjms.82.1.25", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2020-02-26"}, "publish_date": "2020-02-26", "publish_status": "0", "recid": "29349", "relation": {}, "relation_version_is_last": true, "title": ["Micro-computed tomography images of lung adenocarcinoma: detection of lepidic growth patterns"], "weko_shared_id": 3}