Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer.
Computed tomography
Neoadjuvant therapy
Prognostic value
Rectal cancer
Texture analysis
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:
06 2019
06 2019
Historique:
received:
06
11
2018
revised:
28
02
2019
accepted:
11
03
2019
pubmed:
25
4
2019
medline:
24
3
2020
entrez:
25
4
2019
Statut:
ppublish
Résumé
Baseline contrast-enhanced computed tomography (CT)-derived texture analysis in locally advanced rectal cancer could help offer the best personalized treatment. The purpose of this study was to determine the value of baseline-CT texture analysis in the prediction of downstaging in patients with locally advanced rectal cancer. We retrospectively included all consecutive patients treated with neoadjuvant chemoradiation therapy (CRT) followed by surgery for locally advanced rectal cancer. Tumor texture analysis was performed on the baseline pre-CRT contrast-enhanced CT examination. Based on the selected model of downstaging with a penalized logistic regression in a training set, a radiomics score (Radscore) was calculated as a linear combination of selected features. A multivariable prognostic model that included Radscore and clinical factors was created. Of the 121 patients included in the study, 109 patients (90%) had T3-T4 cancer and 99 (82%) had N+ cancer. A downstaging response was observed in 96 patients (79%). In the training set (79 patients), the best model (ELASTIC-NET method) reduced the 36 texture features to a combination of 6 features. The multivariate analysis retained the Radscore (odds ratio [OR] = 13.25; 95% confidence interval [95% CI], 4.06-71.64; p < 0.001) and age (OR = 1.10/1 year; 1.03-1.20; p = 0.008) as independent factors. In the test set, the area under the curve was estimated to be 0.70 (95% CI, 0.48-0.92). This study presents a prognostic score for downstaging, from initial computed tomography-derived texture analysis in locally advanced rectal cancer, which may lead to a more personalized treatment for each patient.
Sections du résumé
BACKGROUND AND PURPOSE
Baseline contrast-enhanced computed tomography (CT)-derived texture analysis in locally advanced rectal cancer could help offer the best personalized treatment. The purpose of this study was to determine the value of baseline-CT texture analysis in the prediction of downstaging in patients with locally advanced rectal cancer.
PATIENTS AND METHODS
We retrospectively included all consecutive patients treated with neoadjuvant chemoradiation therapy (CRT) followed by surgery for locally advanced rectal cancer. Tumor texture analysis was performed on the baseline pre-CRT contrast-enhanced CT examination. Based on the selected model of downstaging with a penalized logistic regression in a training set, a radiomics score (Radscore) was calculated as a linear combination of selected features. A multivariable prognostic model that included Radscore and clinical factors was created.
RESULTS
Of the 121 patients included in the study, 109 patients (90%) had T3-T4 cancer and 99 (82%) had N+ cancer. A downstaging response was observed in 96 patients (79%). In the training set (79 patients), the best model (ELASTIC-NET method) reduced the 36 texture features to a combination of 6 features. The multivariate analysis retained the Radscore (odds ratio [OR] = 13.25; 95% confidence interval [95% CI], 4.06-71.64; p < 0.001) and age (OR = 1.10/1 year; 1.03-1.20; p = 0.008) as independent factors. In the test set, the area under the curve was estimated to be 0.70 (95% CI, 0.48-0.92).
CONCLUSION
This study presents a prognostic score for downstaging, from initial computed tomography-derived texture analysis in locally advanced rectal cancer, which may lead to a more personalized treatment for each patient.
Identifiants
pubmed: 31015162
pii: S0167-8140(19)30115-X
doi: 10.1016/j.radonc.2019.03.011
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
153-160Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.