Treatment Response Prediction using MRI-based Pre-, Post- and Delta-Radiomic Features and Machine Learning Algorithms in Colorectal Cancer

Shayesteh, Sajad P; Nazari, Mostafa; Salahshour, Ali; Sandoughdaran, Saleh; Hajianfar, Ghasem; Khateri, Maziar; Joybari, Ali Yaghobi; Jozian, Fariba; Feyzabad, Seyed Hasan Fatehi; Arabi, Hossein; Shiri Lord, Isaac; Zaidi, Habib

doi:10.1002/mp.14896

Scientific article

English

Treatment Response Prediction using MRI-based Pre-, Post- and Delta-Radiomic Features and Machine Learning Algorithms in Colorectal Cancer

ContributorsShayesteh, Sajad P; Nazari, Mostafa; Salahshour, Ali; Sandoughdaran, Saleh; Hajianfar, Ghasem; Khateri, Maziar; Joybari, Ali Yaghobi; Jozian, Fariba; Feyzabad, Seyed Hasan Fatehi; Arabi, Hossein; Shiri Lord, Isaac

; Zaidi, Habib

Published inMedical Physics, vol. 48, no. 7, p. 3691-3701

Publication date2021

Abstract

We evaluate the feasibility of treatment response prediction using MRI-based pre-, post- and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT).

Keywords

MRI
Delta-radiomics
Machine learning
Rectal cancer
Treatment response

Affiliation entities

Research groups

Imagerie Médicale (TEP et TEMP) (542)

Funding

Swiss National Science Foundation - Towards patient-specific hybrid whole-body PET parametric imaging [320030_176052]

Citation (ISO format)

SHAYESTEH, Sajad P et al. Treatment Response Prediction using MRI-based Pre-, Post- and Delta-Radiomic Features and Machine Learning Algorithms in Colorectal Cancer. In: Medical Physics, 2021, vol. 48, n° 7, p. 3691–3701. doi: 10.1002/mp.14896

Article (Published version)

Identifiers

PID : unige:155216
DOI : 10.1002/mp.14896
PMID : 33894058

Additional URL for this publicationhttps://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14896

Journal ISSN0094-2405

187views

0downloads

Creation18/05/2021 15:30:00

First validation18/05/2021 15:30:00

Update time16/03/2023 01:26:43

Status update16/03/2023 01:26:42

Last indexation31/10/2024 23:17:33

Archive ouverte UNIGE

Treatment Response Prediction using MRI-based Pre-, Post- and Delta-Radiomic Features and Machine Learning Algorithms in Colorectal Cancer

We evaluate the feasibility of treatment response prediction using MRI-based pre-, post- and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT).

Technical informations