title: Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier.
abstract: A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor "ground truth." Textural features were extracted from the images using co-occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood (ML) classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine (SVM) and Fisher linear discriminant (FLD), utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone (PZ) of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(+/-0.064), and our best SVM classifier achieved an average ROC area of 0.761(+/-0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(+/-0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance (P=0.0003 and 0.0017, respectively) from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary statistical maps can potentially aid in image-guided prostate biopsy and assist in guiding and controlling delivery of localized therapy under image guidance.
authors.affiliation: Surgical Planning Laboratory, Department of Radiology, Division of MRI, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Ian
authors.initials: I
authors.lastname: Chan
authors.forename: William
authors.initials: W
authors.lastname: Wells
authors.forename: Robert V
authors.initials: RV
authors.lastname: Mulkern
authors.forename: Steven
authors.initials: S
authors.lastname: Haker
authors.forename: Jianqing
authors.initials: J
authors.lastname: Zhang
authors.forename: Kelly H
authors.initials: KH
authors.lastname: Zou
authors.forename: Stephan E
authors.initials: SE
authors.lastname: Maier
authors.forename: Clare M C
authors.initials: CM
authors.lastname: Tempany
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D005103:Expert Systems; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011471:Prostatic Neoplasms / Q000145:classification* / Q000175:diagnosis* / Q000532:radiotherapy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D016896:Treatment Outcome