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Journal of Molecular Cell Biology 2019年第4期11卷 267-276页

作者： mizuho nakayama Masanobu Oshima Division of Genetics Cancer Research Institute Kanazawa University Kanazawa 920-1192 Japan WPI-Nano Life Science Institute Kanazawa University Kanazawa 920-1192 Japan

The accumulation of genetic alterations in driver genes is responsible for the development and malignant progression of colorectal cancer. Comprehensive genome analyses have revealed the driver genes, including APC, KRAS, TGFBR2, and TP53, whose mutations are frequently found in human colorectal cancers. Among them, the p53 mutation is found in ~60% of colorectal cancers, and a majority of mutations are missense-type at ‘hot spots’, suggesting an oncogenic role of mutant p53 by ‘gain-of-function’ mechanisms. Mouse model studies have shown that one of these missense-type mutations, p53 R270H (corresponding to human R273H), causes submucosal invasion of intestinal tumors, while the loss of wild-type p53 has a limited effect on the invasion process. Furthermore, the same mutant p53 promotes metastasis when combined with Kras activation and TGF-β suppression. Importantly, either missense-type p53 mutation or loss of wild-type p53 induces NF-κB activation by a variety of mechanisms, such as increasing promoter accessibility by chromatin remodeling, which may contribute to progression to epithelial–mesenchymal transition. These results indicate that missense-type p53 mutations together with loss of wild-type p53 accelerate the late stage of colorectal cancer progression through the activation of both oncogenic and inflammatory pathways. Accordingly, the suppression of the mutant p53 function via the inhibition of nuclear accumulation is expected to be an effective strategy against malignant progression of colorectal cancer.

关键词： colon cancer missense-type mutant p53 gain-of-function mouse model NF-κB multistep tumorigenesis organoids

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Segmentation Method of Breast Masses on Ultrasonographic Images Using Level Set Method Based on Statistical Model

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Journal of Biomedical Science and Engineering 2017年第4期10卷 149-162页

作者： Akiyoshi Hizukuri Ryohei nakayama Hiroshi Ashiba Information and Communication Research Division Mizuho Information & Research Institute Inc. Tokyo Japan Department of Electronic and Computer Engineering Ritsumeikan University Kusatsu Japan Graduate School of Health Science Suzuka University of Medical Science Suzuka Japan

It is important to segment mass region accurately in a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of the mass on ultrasonographic breast image. The purpose of this study was to develop a novel level set method for segmentation of breast mass on ultrasonographic image. Our database consisted of 151 ultrasonographic images with 70 malignant and 81 benign breast masses. In a novel level set method, an energy function was defined with region-based, edge-based, and regularizing terms. The region-based term analyzed global information, whereas the edge-based term analyzed local information. The regularizing term also controlled the length of the boundary curve. The region of breast mass was segmented so that the energy based on those terms was minimized. With our proposed method, true positive (TP) ratio, false positive (FP) ratio, jaccard similarity (JS), and Dice similarity coefficient (DSC) were 92.2%, 9.1%, 84.2%, and 91.3%, respectively. These results tended to be substantially higher than those with two conventional segmentation methods. Our proposed method based on the novel level set method was shown to segment mass region accurately on ultrasonographic breast image.

关键词： Segmentation Level Set Breast Mass Ultrasonographic Image

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Computerized Scheme for Histological Classification of Masses with Architectural Distortions in Ultrasonographic Images

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Journal of Biomedical Science and Engineering 2016年第8期9卷 399-409页

作者： Akiyoshi Hizukuri Ryohei nakayama Emi Honda Yumi Kashikura Tomoko Ogawa mizuho Information & Research Institute Inc. Information and Communication Research Division Tokyo Japan Department of Electronic and Computer Engineering Ritsumeikan University Kusatsu Japan Department of Radiology Japanese Red Cross Medical Center Tokyo Japan Department of Breast Surgery Mie University School of Medicine Tsu Japan

Architectural distortion is an important ultrasonographic indicator of breast cancer. However, it is difficult for clinicians to determine whether a given lesion is malignant because such distortions can be subtle in ultrasonographic images. In this paper, we report on a study to develop a computerized scheme for the histological classification of masses with architectural distortions as a differential diagnosis aid. Our database consisted of 72 ultrasonographic images obtained from 47 patients whose masses had architectural distortions. This included 51 malignant (35 invasive and 16 non-invasive carcinomas) and 21 benign masses. In the proposed method, the location of the masses and the area occupied by them were first determined by an experienced clinician. Fourteen objective features concerning masses with architectural distortions were then extracted automatically by taking into account subjective features commonly used by experienced clinicians to describe such masses. The k-nearest neighbors (k-NN) rule was finally used to distinguish three histological classifications. The proposed method yielded classification accuracy values of 91.4% (32/35) for invasive carcinoma, 75.0% (12/16) for noninvasive carcinoma, and 85.7% (18/21) for benign mass, respectively. The sensitivity and specificity values were 92.2% (47/51) and 85.7% (18/21), respectively. The positive predictive values (PPV) were 88.9% (32/36) for invasive carcinoma and 85.7% (12/14) for noninvasive carcinoma whereas the negative predictive values (NPV) were 81.8% (18/22) for benign mass. Thus, the proposed method can help the differential diagnosis of masses with architectural distortions in ultrasonographic images.

关键词： Computer-Aided Diagnosis Architectural Distortion Mass Histological Classification Ultrasonographic Image

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