期刊
MEDICAL PHYSICS
卷 30, 期 9, 页码 2390-2398出版社
WILEY
DOI: 10.1118/1.1593633
关键词
prostate cancer detection; magnetic resonance imaging; T2 map; line scan diffusion imaging; image guided diagnosis
资金
- AHRQ HHS [R03HS13234-01] Funding Source: Medline
- NCI NIH HHS [1 R33 CA99015] Funding Source: Medline
- NIA NIH HHS [R01 AG19513-01, AG 5 P01CA67165-03] Funding Source: Medline
- NINDS NIH HHS [1R01 NS39335-01A1] Funding Source: Medline
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. (C) 2003 American Association of Physicists in Medicine.
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