Multiparametric MRI maps for detection and grading of dominant prostate tumors.
J Magn Reson Imaging
; 35(6): 1403-13, 2012 Jun.
Article
em En
| MEDLINE
| ID: mdl-22267089
ABSTRACT
PURPOSE:
To develop an image-based technique capable of detection and grading of prostate cancer, which combines features extracted from multiparametric MRI into a single parameter map of cancer probability. MATERIALS ANDMETHODS:
A combination of features extracted from diffusion tensor MRI and dynamic contrast enhanced MRI was used to characterize biopsy samples from 29 patients. Support vector machines were used to separate the cancerous samples from normal biopsy samples and to compute a measure of cancer probability, presented in the form of a cancer colormap. The classification results were compared with the biopsy results and the classifier was tuned to provide the largest area under the receiver operating characteristic (ROC) curve. Based solely on the tuning of the classifier on the biopsy data, cancer colormaps were also created for whole-mount histopathology slices from four radical prostatectomy patients.RESULTS:
An area under ROC curve of 0.96 was obtained on the biopsy dataset and was validated by a "leave-one-patient-out" procedure. The proposed measure of cancer probability shows a positive correlation with Gleason score. The cancer colormaps created for the histopathology patients do display the dominant tumors. The colormap accuracy increases with measured tumor area and Gleason score.CONCLUSION:
Dynamic contrast enhanced imaging and diffusion tensor imaging, when used within the framework of supervised classification, can play a role in characterizing prostate cancer.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Próstata
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Algoritmos
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Interpretação de Imagem Assistida por Computador
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Técnica de Subtração
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Gadolínio DTPA
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Imagem de Difusão por Ressonância Magnética
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
Limite:
Aged
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article