Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer-Reference-Cited by-同舟云学术

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Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

Published:2019-03 Issue: Volume:132 Page:171-177
ISSN:0167-8140
Container-title:Radiotherapy and Oncology
language:en
Short-container-title:Radiotherapy and Oncology

Author:

Wang Shuo,Liu Zhenyu^ORCID,Rong Yu,Zhou Bin,Bai Yan,Wei Wei,Wei Wei,Wang Meiyun,Guo Yingkun,Tian Jie^ORCID

Funder

National Key Research and Development Plan of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Beijing Municipal Science & Technology Commission

Chinese Academy of Sciences

Henan Province Scientific and Technological Cooperation Project

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,Oncology,Hematology

Reference33 articles.

1. Paclitaxel with and without pazopanib for persistent or recurrent ovarian cancer: a randomized clinical trial;Richardson;JAMA Oncol,2018

2. Newly diagnosed and relapsed epithelial ovarian carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up;Ledermann;Ann Oncol,2013

3. Risk of ovarian cancer relapse score: a prognostic algorithm to predict relapse following treatment for advanced ovarian cancer;Rizzuto;Int J Gynecol Cancer,2015

4. Dose-response association of CD8+ tumor-infiltrating lymphocytes and survival time in high-grade serous ovarian cancer;Goode;JAMA Oncol,2017

5. Treatment options in recurrent ovarian cancer: latest evidence and clinical potential;Luvero;Ther Adv Med Oncol,2014

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