Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Advertisers
    • Editorial Board
    • Special Issues
  • Journal Metrics
  • Other Publications
    • Anticancer Research
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
  • About Us
    • General Policy
    • Contact
  • Other Publications
    • In Vivo
    • Anticancer Research
    • Cancer Genomics & Proteomics

User menu

  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
In Vivo
  • Other Publications
    • In Vivo
    • Anticancer Research
    • Cancer Genomics & Proteomics
  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart
In Vivo

Advanced Search

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Advertisers
    • Editorial Board
    • Special Issues
  • Journal Metrics
  • Other Publications
    • Anticancer Research
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
  • About Us
    • General Policy
    • Contact
  • Visit iiar on Facebook
  • Follow us on Linkedin
Research ArticleClinical Studies

Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network

TOMOKAZU HASEGAWA, MASANORI SOMEYA, MASAKAZU HORI, TAKAAKI TSUCHIYA, YUUKI FUKUSHIMA, YOSHIHISA MATSUMOTO and KOH-ICHI SAKATA
In Vivo September 2020, 34 (5) 2865-2872; DOI: https://doi.org/10.21873/invivo.12114
TOMOKAZU HASEGAWA
1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: hasse{at}sapmed.ac.jp
MASANORI SOMEYA
1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
MASAKAZU HORI
1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
TAKAAKI TSUCHIYA
1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
YUUKI FUKUSHIMA
1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
YOSHIHISA MATSUMOTO
2Research Laboratory for Nuclear Reactors, Tokyo Institute of Technology, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
KOH-ICHI SAKATA
1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Information

vol. 34 no. 5 2865-2872
DOI 
https://doi.org/10.21873/invivo.12114
PMID 
32871826

Published By 
International Institute of Anticancer Research
Print ISSN 
0258-851X
Online ISSN 
1791-7549
History 
  • Received May 30, 2020
  • Revision received July 1, 2020
  • Accepted July 3, 2020
  • Published online August 31, 2020.

Copyright & License 
Copyright © 2020 The Author(s). Published by the International Institute of Anticancer Research.

Author Information

  1. TOMOKAZU HASEGAWA1⇑,
  2. MASANORI SOMEYA1,
  3. MASAKAZU HORI1,
  4. TAKAAKI TSUCHIYA1,
  5. YUUKI FUKUSHIMA1,
  6. YOSHIHISA MATSUMOTO2 and
  7. KOH-ICHI SAKATA1
  1. 1Department of Radiation Oncology, Sapporo Medical University School of Medicine, Sapporo, Japan
  2. 2Research Laboratory for Nuclear Reactors, Tokyo Institute of Technology, Tokyo, Japan
  1. Correspondence to: Tomokazu Hasegawa, Department of Radiation Oncology, Sapporo Medical University School of medicine, S1W16, Chuo-ku, Sapporo, Japan. Tel: +81 116112111 (ext. 35350), Fax: +81 116139920, e-mail: hasse{at}sapmed.ac.jp
View Full Text

Statistics from Altmetric.com

Cited By...

  • Citations
  • Google Scholar

This article has not yet been cited by articles in journals that are participating in Crossref Cited-by Linking.

PreviousNext
Back to top

In this issue

In Vivo
Vol. 34, Issue 5
September-October 2020
  • Table of Contents
  • Table of Contents (PDF)
  • Index by author
  • Back Matter (PDF)
  • Ed Board (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on In Vivo.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network
(Your Name) has sent you a message from In Vivo
(Your Name) thought you would like to see the In Vivo web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
1 + 3 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network
TOMOKAZU HASEGAWA, MASANORI SOMEYA, MASAKAZU HORI, TAKAAKI TSUCHIYA, YUUKI FUKUSHIMA, YOSHIHISA MATSUMOTO, KOH-ICHI SAKATA
In Vivo Sep 2020, 34 (5) 2865-2872; DOI: 10.21873/invivo.12114

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Reprints and Permissions
Share
Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network
TOMOKAZU HASEGAWA, MASANORI SOMEYA, MASAKAZU HORI, TAKAAKI TSUCHIYA, YUUKI FUKUSHIMA, YOSHIHISA MATSUMOTO, KOH-ICHI SAKATA
In Vivo Sep 2020, 34 (5) 2865-2872; DOI: 10.21873/invivo.12114
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Patients and Methods
    • Results
    • Discussion
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF

Related Articles

Cited By...

  • Artificial Intelligence in the Diagnosis, Treatment, and Prognosis of Hypopharyngeal Carcinoma: A Scoping Review
  • Four Different Artificial Intelligence Models Versus Logistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting
  • Identification and Quantification of Radiotherapy-related Protein Expression in Cancer Tissues Using the Qupath Software and Prediction of Treatment Response
  • Google Scholar

More in this TOC Section

  • Bioelectrical Impedance Analysis in the Diagnosis of Sarcopenia in Oncological Patients: The Sarco-Detect Study
  • Stage-dependent Expression of Autophagy-related Genes in Patients With Knee Osteoarthritis
  • Effects of Short-term High-dose Vitamin D Supplementation on Mineral Homeostasis in Healthy Young Adults
Show more Clinical Studies

Keywords

  • prediction
  • artificial neural network
  • Ku70
  • radiotherapy
  • prostate cancer
  • hypopharyngeal cancer
In Vivo

© 2026 In Vivo

Powered by HighWire