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-Reference-Cited by-同舟云学术

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

Published:2020-01-06 Issue:3 Volume:38 Page:265-273
ISSN:1867-1071
Container-title:Japanese Journal of Radiology
language:en
Short-container-title:Jpn J Radiol

Author:

Takada Akiyo,Yokota Hajime^ORCID,Watanabe Nemoto Miho,Horikoshi Takuro,Matsushima Jun,Uno Takashi

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging

Link

http://link.springer.com/content/pdf/10.1007/s11604-019-00917-0.pdf

Reference24 articles.

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2. Nemoto MW, Iwai Y, Togasaki G, Kurokawa M, Harada R, Kobayashi H, et al. Preliminary results of a new workflow for MRI/CT-based image-guided brachytherapy in cervical carcinoma. Jpn J Radiol. 2017;35:760–5.

3. Rose PG, Java J, Whitney CW, Stehman FB, Lanciano R, Thomas GM, et al. Nomograms predicting progression-free survival, overall survival, and pelvic recurrence in locally advanced cervical cancer developed from an analysis of identifiable prognostic factors in patients from NRG Oncology/Gynecologic Oncology Group Randomized Trials of chemoradiotherapy. J Clin Oncol. 2015;33:2136–42.

4. Chen CC, Wang L, Lin JC, Jan JS. The prognostic factors for locally advanced cervical cancer patients treated by intensity-modulated radiation therapy with concurrent chemotherapy. J Formos Med Assoc. 2015;114:231–7.

5. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.

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