Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.

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serval:BIB_A190AD7F0236
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Article: article from journal or magazin.
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Publications
Institution
Title
Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.
Journal
EJNMMI research
Author(s)
Oliveira C., Amstutz F., Vuong D., Bogowicz M., Hüllner M., Foerster R., Basler L., Schröder C., Eboulet E.I., Pless M., Thierstein S., Peters S., Hillinger S., Tanadini-Lang S., Guckenberger M.
ISSN
2191-219X (Print)
ISSN-L
2191-219X
Publication state
Published
Issued date
21/08/2021
Peer-reviewed
Oui
Volume
11
Number
1
Pages
79
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC).
A total of 1404 primary tumour radiomic features were extracted from pre-treatment [ <sup>18</sup> F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC).
Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03).
A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol.
Keywords
Lung cancer, Multi-centre, PET, Radiomics, Robust
Pubmed
Web of science
Open Access
Yes
Create date
03/09/2021 17:04
Last modification date
12/01/2022 7:12
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