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Multiparametric MRI maps for detection and grading of dominant prostate tumors
Author(s) -
Moradi Mehdi,
Salcudean Septimiu E.,
Chang Silvia D.,
Jones Edward C.,
Buchan Nicholas,
Casey Rowan G.,
Goldenberg S. Larry,
Kozlowski Piotr
Publication year - 2012
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.23540
Subject(s) - receiver operating characteristic , prostate cancer , grading (engineering) , histopathology , biopsy , prostatectomy , medicine , diffusion mri , cancer detection , radiology , prostate , magnetic resonance imaging , pattern recognition (psychology) , cancer , nuclear medicine , artificial intelligence , computer science , pathology , civil engineering , engineering
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 and Methods: 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. J. Magn. Reson. Imaging 2012;35:1403–1413. © 2012 Wiley Periodicals, Inc.