Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.
BMC Med Imaging
; 21(1): 17, 2021 02 03.
Article
en En
| MEDLINE
| ID: mdl-33535988
ABSTRACT
BACKGROUND:
Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM.METHODS:
Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity.RESULTS:
No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI] 0.45,0.91), 78.36%(95%CI 0.56,1.00) and 61.33%(95%CI 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists' assessment were66.23%(95% CI 0.55,0.76), 61.50%(95% CI 0.43,0.78) and 68.62%(95% CI 0.55,0.80); 55.84%(95% CI 0.45,0.66),69.25%(95% CI 0.50,0.84) and 49.13%(95% CI 0.36,0.62); 55.84%(95% CI 0.45,0.66), 69.23%(95% CI 0.50,0.84) and 47.06%(95% CI 0.34,0.61), respectively.CONCLUSION:
T1CE-based radiomics showed better classification performance compared with radiologists' assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Encefálicas
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Imagen por Resonancia Magnética
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Glioblastoma
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Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adolescent
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Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
BMC Med Imaging
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2021
Tipo del documento:
Article
País de afiliación:
China