Treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features and machine learning algorithms in colorectal cancer-Reference-Cited by-同舟云学术

Treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features and machine learning algorithms in colorectal cancer

Published:2021-05-17 Issue:7 Volume:48 Page:3691-3701
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Shayesteh Sajad¹,Nazari Mostafa²,Salahshour Ali³,Sandoughdaran Saleh⁴,Hajianfar Ghasem⁵,Khateri Maziar⁶,Yaghobi Joybari Ali⁴,Jozian Fariba⁴,Fatehi Feyzabad Seyed Hasan⁵,Arabi Hossein⁷,Shiri Isaac⁷,Zaidi Habib⁷⁸⁹¹⁰

Affiliation:

1. Department of Physiology Pharmacology and Medical Physics Alborz University of Medical Sciences Karaj Iran

2. Department of Biomedical Engineering and Medical Physics Shahid Beheshti University of Medical Sciences Tehran Iran

3. Department of Radiology Alborz University of Medical Sciences Karaj Iran

4. Department of Radiation Oncology Shahid Beheshti University of Medical Sciences Tehran Iran

5. Rajaie Cardiovascular, Medical & Research Centre Iran University of Medical Science Tehran Iran

6. Department of Medical Radiation Engineering, Science and Research Branch Islamic Azad University Tehran Iran

7. Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Geneva Switzerland

8. Geneva University Neurocenter Geneva University Geneva Switzerland

9. Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen Groningen Netherlands

10. Department of Nuclear Medicine University of Southern Denmark Odense Denmark

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14896

Reference59 articles.

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4. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis

5. Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI

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