Article info
Stomach
Original research
Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial
- Correspondence to Dr Patrick Tan, Program in Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore; gmstanp{at}duke-nus.edu.sg; Takashi Oshima, Deparment of Gastrointestinal Surgery, Kanagawa Cancer Center, Yokohama, Japan; oshimat{at}kcch.jp; Akira Tsuburaya, Department of Surgery, Ozawa Hospital, Odawara, Japan; tuburayaa{at}gmail.com
Citation
Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial
Publication history
- Received January 7, 2021
- Revised April 26, 2021
- Accepted April 29, 2021
- First published May 12, 2021.
Online issue publication
May 18, 2022
Article Versions
- Previous version (20 April 2022).
- Previous version (20 April 2022).
- You are viewing the most recent version of this article.
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Request permissions
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
Copyright information
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.