Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy.
delta radiomics
machine learning
magnetic resonance imaging
radiomics
sinonasal cancer
Journal
Journal of imaging
ISSN: 2313-433X
Titre abrégé: J Imaging
Pays: Switzerland
ID NLM: 101698819
Informations de publication
Date de publication:
15 Feb 2022
15 Feb 2022
Historique:
received:
16
12
2021
revised:
23
01
2022
accepted:
11
02
2022
entrez:
24
2
2022
pubmed:
25
2
2022
medline:
25
2
2022
Statut:
epublish
Résumé
Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score. The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65-0.88), 0.76 (CI: 0.62-0.87) and 0.93 (CI: 0.75-1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78. These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers.
Sections du résumé
BACKGROUND
BACKGROUND
Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST.
METHODS
METHODS
Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score.
RESULTS
RESULTS
The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65-0.88), 0.76 (CI: 0.62-0.87) and 0.93 (CI: 0.75-1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78.
CONCLUSIONS
CONCLUSIONS
These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers.
Identifiants
pubmed: 35200748
pii: jimaging8020046
doi: 10.3390/jimaging8020046
pmc: PMC8877083
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Italian Association for Cancer Research
ID : 1732
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