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J-GLOBAL ID：202002228192196553   Reference number：20A0698894

A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions

子宮頸癌におけるMRI放射線医学の多スキャナー研究:腫瘍周囲領域を含む機械学習法に基づく根治的放射線療法後の腫瘍内コントロールの予想

Author (6)： Takada Akiyo (Department of Radiology, Chiba University Hospital, Chiba, Chiba, Japan) ,  Yokota Hajime (Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan) ,  Watanabe Nemoto Miho (Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan) ,  Horikoshi Takuro (Department of Radiology, Chiba University Hospital, Chiba, Chiba, Japan) ,  Matsushima Jun (Department of Diagnostic Pathology, Chiba University Hospital, Chiba, Chiba, Japan) ,  Uno Takashi (Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan)
Material： Japanese Journal of Radiology  (Jpn J Radiol)
Volume： 38  Issue： 3  Page： 265-273  Publication year： Mar. 25, 2020 
JST Material Number： L3853A  ISSN： 1867-1071  Document type： Article
Article type： 原著論文  Country of issue： Germany, Federal Republic of (DEU)  Language： ENGLISH (EN)

Thesaurus term： *cervix tumor ,...
Semi thesaurus term： T2-weighted image ,...

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Author keywords (5)： Uterine cervical cancer ,  Radiomics ,  Machine learning ,  In-field recurrence ,  Prognosis

JST classification (2)： Tumors(=neoplasms)of female genitalia and fetus  (GS04000K) ,  Clinical oncology in general  (GE03010Y)

Terms in the title (10)： 子宮頸癌 ,  MRI ,  放射線医学 ,  スキャナー ,  研究 ,  腫瘍 ,  機械学習 ,  根治的放射線療法 ,  コントロール ,  予想