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.


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
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

153-160

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Benjamin Vandendorpe (B)

Department of Radiation Oncology, Institut Jean Godinot, Reims, France.

Carole Durot (C)

Radiology, Centre Hospitalier Universitaire de Reims, France.

Loïc Lebellec (L)

Biostatistics Unit, Centre Oscar Lambret, Lille, France.

Marie-Cécile Le Deley (MC)

Biostatistics Unit, Centre Oscar Lambret, Lille, France; CESP, INSERM, Paris-Sud Paris-Orsay University, Villejuif, France.

Dienabou Sylla (D)

Biostatistics Unit, Centre Oscar Lambret, Lille, France.

André-Michel Bimbai (AM)

Biostatistics Unit, Centre Oscar Lambret, Lille, France.

Kocéila Amroun (K)

Department of Surgery, Institut Jean Godinot, Reims, France.

Fabrice Ramiandrisoa (F)

Department of Radiation Oncology, Institut Jean Godinot, Reims, France.

Abel Cordoba (A)

Department of Radiation Oncology, Centre Oscar Lambret, Lille, France.

Xavier Mirabel (X)

Department of Radiation Oncology, Centre Oscar Lambret, Lille, France.

Christine Hoeffel (C)

Radiology, Centre Hospitalier Universitaire de Reims, France.

David Pasquier (D)

Department of Radiation Oncology, Centre Oscar Lambret, Lille, France.

Stéphanie Servagi-Vernat (S)

Department of Radiation Oncology, Institut Jean Godinot, Reims, France; CRESTIC, University of Reims, France. Electronic address: stephanie.servagivernat@reims.unicancer.fr.

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