Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study.
Colorectal neoplasms
Computed X-ray tomography
Disease-free survival
Machine learning
Radiomics
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jan 2022
Jan 2022
Historique:
received:
30
03
2021
accepted:
27
05
2021
revised:
11
05
2021
pubmed:
26
6
2021
medline:
15
12
2021
entrez:
25
6
2021
Statut:
ppublish
Résumé
To assess the value of contrast-enhanced (CE) diagnostic CT scans characterized through radiomics as predictors of recurrence for patients with stage II and III colorectal cancer in a two-center context. This study included 193 patients diagnosed with stage II and III colorectal adenocarcinoma from 1 July 2008 to 15 March 2017 in two different French University Hospitals. To compensate for the variability in two-center data, a statistical harmonization method Bootstrapped ComBat (B-ComBat) was used. Models predicting disease-free survival (DFS) were built using 3 different machine learning (ML): (1) multivariate regression (MR) with 10-fold cross-validation after feature selection based on least absolute shrinkage and selection operator (LASSO), (2) random forest (RF), and (3) support vector machine (SVM), both with embedded feature selection. The performance for both balanced and 95% sensitivity models was systematically higher after our proposed B-ComBat harmonization compared to the use of the original untransformed data. The most clinically relevant performance was achieved by the multivariate regression model combining a clinical variable (postoperative chemotherapy) with two radiomics shape descriptors (compactness and least axis length) with a BAcc of 0.78 and an MCC of 0.6 associated with a required sensitivity of 95%. The resulting stratification in terms of DFS was significant (p = 0.00021), especially compared to the use of unharmonized original data (p = 0.17). Radiomics models derived from contrast-enhanced CT could be trained and validated in a two-center cohort with a good predictive performance of recurrence in stage II et III colorectal cancer patients. • Adjuvant therapy decision in colorectal cancer can be a challenge in medical oncology. • Radiomics models, derived from diagnostic CT, trained and validated in a two-center cohort, could predict recurrence in stage II and III colorectal cancer patients. • Identifying patients with a low risk of recurrence, these models could facilitate treatment optimization and avoid unnecessary treatment.
Identifiants
pubmed: 34170367
doi: 10.1007/s00330-021-08104-4
pii: 10.1007/s00330-021-08104-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
405-414Subventions
Organisme : FP7 People: Marie-Curie Actions
ID : ETN 2017 PREDICT project
Organisme : Ligue Contre le Cancer
ID : Comité des Côtes-d'Armor
Informations de copyright
© 2021. European Society of Radiology.
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