Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy.
CT
PET
neoadjuvant chemoradiation therapy
pathologic response
radiomics
rectal cancer
Journal
Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
ISSN: 1507-1367
Titre abrégé: Rep Pract Oncol Radiother
Pays: Poland
ID NLM: 100885761
Informations de publication
Date de publication:
2021
2021
Historique:
received:
28
05
2020
accepted:
12
12
2020
entrez:
5
5
2021
pubmed:
6
5
2021
medline:
6
5
2021
Statut:
epublish
Résumé
The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET. The patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation.
Sections du résumé
BACKGROUND
BACKGROUND
The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and
MATERIALS AND METHODS
METHODS
An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET.
RESULTS
RESULTS
The patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0
CONCLUSION
CONCLUSIONS
The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation.
Identifiants
pubmed: 33948299
doi: 10.5603/RPOR.a2021.0004
pii: rpor-26-1-29
pmc: PMC8086711
doi:
Types de publication
Journal Article
Langues
eng
Pagination
29-34Informations de copyright
© 2021 Greater Poland Cancer Centre.
Déclaration de conflit d'intérêts
Conflicts of interest None were declared.
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