CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study.
J Med Imaging Radiat Oncol
; 64(3): 444-449, 2020 Jun.
Article
en En
| MEDLINE
| ID: mdl-32386109
ABSTRACT
INTRODUCTION:
Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT-based radiomic imaging biomarker to predict pathological response.METHODS:
We used two independent cohorts of rectal cancer patients to develop and validate a CT-based radiomic imaging biomarker predictive of treatment response. A total of 91 rectal cancer cases treated from 2009 to 2018 were assessed for the tumour regression grade (TRG) (0 = pathological complete response, pCR; 1 = moderate response; 2 = partial response; 3 = poor response). Exploratory analysis was performed by combining pre-treatment non-contrast CT images and patterns of TRG. The models built from the training cohort were further assessed using the independent validation cohort.RESULTS:
The patterns of pathological response in training and validation groups were TRG 0 (n = 14, 23.3%; n = 6, 19.4%), 1 (n = 31, 51.7%; n = 15, 48.4%), 2 (n = 12, 20.0%; n = 7, 22.6%) and 3 (n = 3, 5.0%; n = 3, 9.7%), respectively. Separate predictive models were built and analysed from CT features for pathological response. For pathological response prediction, the model including 8 radiomic features by random forest method resulted in 83.9% accuracy in predicting TRG 0 vs TRG 1-3 in validation.CONCLUSION:
The pre-treatment CT-based radiomic signatures were developed and validated in two independent cohorts. This imaging biomarker provided a promising way to predict pCR and select patients for non-operative management.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias del Recto
/
Tomografía Computarizada por Rayos X
/
Aprendizaje Automático
Tipo de estudio:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
País/Región como asunto:
America do norte
Idioma:
En
Revista:
J Med Imaging Radiat Oncol
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
NEOPLASIAS
/
RADIOLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos