The purpose of this study was to investigate whether correction with respect to reference tissue of T2-weighted MRimage signal intensity (SI) improves its effectiveness for classification of regions of interest (ROIs) as prostate cancer (PCa) or normal prostatic tissue. Two image datasets collected retrospectively were used in this study: 71 cases acquired with GE scanners (dataset A), and 59 cases acquired with Philips scanners (dataset B). Through a consensus histology- MR correlation review, 175 PCa and 108 normal-tissue ROIs were identified and drawn manually. Reference-tissue ROIs were selected in each case from the levator ani muscle, urinary bladder, and pubic bone. T2-weighted image SI was corrected as the ratio of the average T2-weighted image SI within an ROI to that of a reference-tissue ROI. Area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of T2-weighted image SIs for differentiation of PCa from normal-tissue ROIs. AUC (± standard error) for uncorrected T2-weighted image SIs was 0.78±0.04 (datasets A) and 0.65±0.05 (datasets B). AUC for corrected T2-weighted image SIs with respect to muscle, bladder, and bone reference was 0.77±0.04 (p=1.0), 0.77±0.04 (p=1.0), and 0.75±0.04 (p=0.8), respectively, for dataset A; and 0.81±0.04 (p=0.002), 0.78±0.04 (p<0.001), and 0.79±0.04 (p<0.001), respectively, for dataset B. Correction in reference to the levator ani muscle yielded the most consistent results between GE and Phillips images. Correction of T2-weighted image SI in reference to three types of extra-prostatic tissue can improve its effectiveness for differentiation of PCa from normal-tissue ROIs, and correction in reference to the levator ani muscle produces consistent T2-weighted image SIs between GE and Phillips MR images.
The purpose of this study was to study T2-weighted magnetic resonance (MR) image texture features and diffusionweighted (DW) MR image features in distinguishing prostate cancer (PCa) from normal tissue. We collected two image datasets: 23 PCa patients (25 PCa and 23 normal tissue regions of interest [ROIs]) imaged with Philips MR scanners, and 30 PCa patients (41 PCa and 26 normal tissue ROIs) imaged with GE MR scanners. A radiologist drew ROIs manually via consensus histology-MR correlation conference with a pathologist. A number of T2-weighted texture features and apparent diffusion coefficient (ADC) features were investigated, and linear discriminant analysis (LDA) was used to combine select strong image features. Area under the receiver operating characteristic (ROC) curve (AUC) was used to characterize feature effectiveness in distinguishing PCa from normal tissue ROIs. Of the features studied, ADC 10th percentile, ADC average, and T2-weighted sum average yielded AUC values (±standard error) of 0.95±0.03, 0.94±0.03, and 0.85±0.05 on the Phillips images, and 0.91±0.04, 0.89±0.04, and 0.70±0.06 on the GE images, respectively. The three-feature combination yielded AUC values of 0.94±0.03 and 0.89±0.04 on the Phillips and GE images, respectively. ADC 10th percentile, ADC average, and T2-weighted sum average, are effective in distinguishing PCa from normal tissue, and appear robust in images acquired from Phillips and GE MR scanners.
Quantitative analysis of multi-parametric magnetic resonance (MR) images of the prostate, including T2-weighted
(T2w) and diffusion-weighted (DW) images, requires accurate image registration. We compared two registration
methods between T2w and DW images. We collected pre-operative MR images of 124 prostate cancer patients (68
patients scanned with a GE scanner and 56 with Philips scanners). A landmark-based rigid registration was done based
on six prostate landmarks in both T2w and DW images identified by a radiologist. Independently, a researcher manually
registered the same images. A radiologist visually evaluated the registration results by using a 5-point ordinal scale of 1
(worst) to 5 (best). The Wilcoxon signed-rank test was used to determine whether the radiologist's ratings of the results
of the two registration methods were significantly different. Results demonstrated that both methods were accurate: the
average ratings were 4.2, 3.3, and 3.8 for GE, Philips, and all images, respectively, for the landmark-based method; and
4.6, 3.7, and 4.2, respectively, for the manual method. The manual registration results were more accurate than the
landmark-based registration results (p < 0.0001 for GE, Philips, and all images). Therefore, the manual method
produces more accurate registration between T2w and DW images than the landmark-based method.
Analysis of interactions between B and T cells in tubulointerstitial inflammation is important for understanding human
lupus nephritis. We developed a computer technique to perform this analysis, and compared it with manual analysis.
Multi-channel immunoflourescent-microscopy images were acquired from 207 regions of interest in 40 renal tissue
sections of 19 patients diagnosed with lupus nephritis. Fresh-frozen renal tissue sections were stained with combinations
of immunoflourescent antibodies to membrane proteins and counter-stained with a cell nuclear marker. Manual
delineation of the antibodies was considered as the reference standard. We first segmented cell nuclei and cell
membrane markers, and then determined corresponding cell types based on the distances between cell nuclei and
specific cell-membrane marker combinations. Subsequently, the distribution of the shortest distance from T cell nuclei
to B cell nuclei was obtained and used as a surrogate indicator of cell-cell interactions. The computer and manual
analyses results were concordant. The average absolute difference was 1.1±1.2% between the computer and manual
analysis results in the number of cell-cell distances of 3 μm or less as a percentage of the total number of cell-cell
distances. Our computerized analysis of cell-cell distances could be used as a surrogate for quantifying cell-cell
interactions as either an automated and quantitative analysis or for independent confirmation of manual analysis.
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