MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma.
anal cancer
machine learning
magnetic resonance imaging
precision medicine
prediction medicine
radiomics
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
07 Jan 2021
07 Jan 2021
Historique:
received:
15
11
2020
revised:
20
12
2020
accepted:
01
01
2021
entrez:
12
1
2021
pubmed:
13
1
2021
medline:
13
1
2021
Statut:
epublish
Résumé
Chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas (ASCC). Despite excellent results for T1-2 stages, relapses still occur in around 35% of locally advanced tumors. Recent strategies focus on treatment intensification, but could benefit from a better patient selection. Our goal was to assess the prognostic value of pre-therapeutic MRI radiomics on 2-year disease control (DC). We retrospectively selected patients with non-metastatic ASCC treated at the CHU Bordeaux and in the French FFCD0904 multicentric trial. Radiomic features were extracted from T2-weighted pre-therapeutic MRI delineated sequences. After random division between training and testing sets on a 2:1 ratio, univariate and multivariate analysis were performed on the training cohort to select optimal features. The correlation with 2-year DC was assessed using logistic regression models, with AUC and accuracy as performance gauges, and the prediction of disease-free survival using Cox regression and Kaplan-Meier analysis. A total of 82 patients were randomized in the training ( A mixed model with two clinical and two radiomic features was predictive of 2-year disease control after CRT and could contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine.
Identifiants
pubmed: 33430396
pii: cancers13020193
doi: 10.3390/cancers13020193
pmc: PMC7827348
pii:
doi:
Types de publication
Journal Article
Langues
eng
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