CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer.
Adult
Aged
Algorithms
Chemoradiotherapy
Female
Head and Neck Neoplasms
/ diagnostic imaging
Humans
Kaplan-Meier Estimate
Male
Middle Aged
Models, Biological
Models, Statistical
Positron Emission Tomography Computed Tomography
/ methods
Prognosis
Radiotherapy Planning, Computer-Assisted
/ methods
Risk
Squamous Cell Carcinoma of Head and Neck
/ diagnostic imaging
Tomography, X-Ray Computed
/ methods
Computed tomography
Imaging during treatment
Patient stratification
Radiomic risk modelling
Journal
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
16
11
2017
revised:
27
06
2018
accepted:
24
07
2018
pubmed:
9
8
2018
medline:
11
1
2020
entrez:
9
8
2018
Statut:
ppublish
Résumé
The development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy. Treatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests. The performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan-Meier analyses, in which risk stratification based on the second week CT could be improved for LRC (p = 0.002) compared to pre-treatment CT (p = 0.063). Incorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.
Sections du résumé
BACKGROUND AND PURPOSE
The development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy.
MATERIAL AND METHODS
Treatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests.
RESULTS
The performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan-Meier analyses, in which risk stratification based on the second week CT could be improved for LRC (p = 0.002) compared to pre-treatment CT (p = 0.063).
CONCLUSION
Incorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.
Identifiants
pubmed: 30087056
pii: S0167-8140(18)33419-4
doi: 10.1016/j.radonc.2018.07.020
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10-17Informations de copyright
Copyright © 2018 Elsevier B.V. All rights reserved.