Yahui Peng

Quantitative Analysis of Multiparametric Prostate MR Images: Differentiation between Prostate Cancer and Normal Tissue and Correlation with Gleason Score—A Computer-aided Diagnosis Development Study

PURPOSE To evaluate the potential utility of a number of parameters obtained at T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced multiparametric magnetic resonance (MR) imaging for computer-aided diagnosis (CAD) of prostate cancer and assessment of cancer aggressiveness. MATERIALS AND METHODS In this institutional review board-approved HIPAA-compliant study, multiparametric MR images were acquired with an endorectal coil in 48 patients with prostate cancer (median age, 62.5 years; age range, 44-73 years) who subsequently underwent prostatectomy. A radiologist and a pathologist identified 104 regions of interest (ROIs) (61 cancer ROIs, 43 normal ROIs) based on correlation of histologic and MR findings. The 10th percentile and average apparent diffusion coefficient (ADC) values, T2-weighted signal intensity histogram skewness, and Tofts K(trans) were analyzed, both individually and combined, via linear discriminant analysis, with receiver operating characteristic curve analysis with area under the curve (AUC) as figure of merit, to distinguish cancer foci from normal foci. Spearman rank-order correlation (ρ) was calculated between cancer foci Gleason score (GS) and image features. RESULTS AUC (maximum likelihood estimate ± standard error) values in the differentiation of prostate cancer from normal foci of 10th percentile ADC, average ADC, T2-weighted skewness, and K(trans) were 0.92 ± 0.03, 0.89 ± 0.03, 0.86 ± 0.04, and 0.69 ± 0.04, respectively. The combination of 10th percentile ADC, average ADC, and T2-weighted skewness yielded an AUC value for the same task of 0.95 ± 0.02. GS correlated moderately with 10th percentile ADC (ρ = -0.34, P = .008), average ADC (ρ = -0.30, P = .02), and K(trans) (ρ = 0.38, P = .004). CONCLUSION The combination of 10th percentile ADC, average ADC, and T2-weighted skewness with CAD is promising in the differentiation of prostate cancer from normal tissue. ADC image features and K(trans) moderately correlate with GS.

Seminal Vesicle Invasion in Prostate Cancer: Evaluation by Using Multiparametric Endorectal MR Imaging

PURPOSE To retrospectively evaluate the diagnostic performance of multiparametric endorectal magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted (DW), and dynamic contrast material-enhanced (DCE) MR techniques, for the diagnosis of seminal vesicle invasion (SVI) and to determine the incremental value of DW MR and DCE MR images. MATERIALS AND METHODS This retrospective HIPAA-compliant study was approved by the institutional review board, with a waiver of informed consent. The study included 131 patients (mean age, 68 years; range, 43-75 years) who underwent endorectal MR imaging before radical prostatectomy between January 2007 and April 2010. Two radiologists (A: experienced, B: less experienced) estimated the likelihood of SVI by using a five-point ordinal scale in three image-viewing settings: T2-weighted images alone; T2-weighted and DW MR images; and T2-weighted, DW MR, and DCE MR images. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated. Confidence intervals estimated with bootstrapping and the McNemar test or Fisher exact test were used to compare sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS Of the 131 patients, 23 (17.6%) had SVI identified after surgery. Review of T2-weighted MR images alone resulted in high specificity (93.1% and 93.6%, for radiologists A and B, respectively) and high negative predictive value (94.8% and 94.0%) but moderate sensitivity (59% and 52%) and positive predictive value (52% and 50%). Review of T2-weighted and DW MR images significantly improved specificity (96.6% [P = .02] and 98.3% [P = .003]) and positive predictive value (70% [P < .05] and 79% [P < .05]) without significantly improving AUC. Additional review of DCE MR images did not yield further incremental improvement. CONCLUSION Additional review of DW MR images improves specificity and positive predictive value in SVI detection compared with reviewing T2-weighted images alone. Addition of DCE MR images to this combination, however, does not provide incremental value for diagnosis of SVI.

Validation of Quantitative Analysis of Multiparametric Prostate MR Images for Prostate Cancer Detection and Aggressiveness Assessment: A Cross-Imager Study

PURPOSE To validate three previously identified quantitative image features across multiparametric magnetic resonance (MR) images acquired with imagers made by two different manufacturers to differentiate prostate cancer (PC) from normal prostatic tissue and to assess cancer aggressiveness. MATERIALS AND METHODS This study was HIPAA-compliant and approved by the institutional review board. Preoperative 1.5-T multiparametric endorectal MR images of 119 PC patients (dataset A, 71 patients; dataset B, 48 patients) were analyzed, and 265 PC and normal peripheral zone regions of interests (ROIs) were identified through histologic and MR consensus review. The 10th percentile average apparent diffusion coefficient (ADC) value, average ADC value, and skewness of T2-weighted signal-intensity histogram were evaluated with area under the receiver operating characteristic curve (AUC). The image features were combined with a linear discriminant analysis classifier and evaluated both on the image dataset of each type of imager alone (leave-one-patient-out evaluation) and across the datasets (training on one dataset, testing on the other). Spearman correlation coefficient was calculated between the image features and ROI-specific Gleason scores. RESULTS AUC values of the image features combined were 0.95 ± 0.02 (standard error) and 0.88 ± 0.03 on dataset B and dataset A alone, respectively, and 0.96 ± 0.02 and 0.89 ± 0.03 when training on dataset A and testing on dataset B and vice versa, respectively. Spearman correlation coefficients between Gleason scores and the ADC features were between -0.27 and -0.34. CONCLUSION Consistently across images from datasets A and B, the 10th percentile ADC value, average ADC value, and T2-weighted skewness can distinguish PC from normal-tissue ROIs, and ADC features correlate moderately with ROI-specific Gleason scores.

Dynamic Contrast-enhanced MR Imaging Curve-type Analysis: Is It Helpful in the Differentiation of Prostate Cancer from Healthy Peripheral Zone?

PURPOSE To evaluate the performance and interobserver agreement of qualitative dynamic contrast material enhanced magnetic resonance (MR) imaging curve analysis as described in the Prostate Imaging Reporting and Data System (PI-RADS) for the differentiation of prostate cancer (PCa) from healthy prostatic tissue in the peripheral zone (PZ). MATERIALS AND METHODS This Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective analysis included 120 consecutive pretreatment dynamic contrast-enhanced (DCE) MR imaging PCa examinations. Regions of interest (ROIs) were placed in 251 spots, including 95 (37.8%) in healthy PZ tissue and 156 (62.2%) in PCa, by using detailed histologic-multiparametric MR correlation review. Three radiologists reviewed the DCE time curves and assessed qualitative curve types as described in PI-RADS: type 1 (progressive), type 2 (plateau), or type 3 (washout). Receiver operating characteristic curve analysis was used to assess accuracy in differentiating PCa from healthy tissue on the basis of curve type, and κ was calculated to assess interobserver agreement. RESULTS Receiver operating characteristic curves were similar for all observers, but mean areas under the receiver operating characteristic curve were poor (0.58 ± 0.04 [standard deviation] to 0.63 ± 0.04). No differences in accuracy were seen for varying DCE time resolution and imaging length. Observer agreement in assessment of type 3 versus types 1 or 2 curves was substantial (0.66 < κ < 0.79), better for PCa ROIs than for healthy-tissue ROIs. The agreement between type 1 and type 2 curves was moderate to substantial (0.49 < κ < 0.78). CONCLUSION Qualitative DCE MR imaging time-curve-type analysis performs poorly for differentiation of PCa from healthy prostatic tissue. Interobserver agreement is excellent in assessment of type 3 curves but only moderate for type 1 and 2 curves.

Apparent Diffusion Coefficient for Prostate Cancer Imaging: Impact of b Values

OBJECTIVE The purpose of this article is to investigate the effect of b values on apparent diffusion coefficient (ADC) values estimated from 1.5-T diffusion-weighted MRI (DWI) of the prostate acquired with an endorectal coil in distinguishing prostate cancer from normal-tissue regions of interest (ROIs) and the correlation of ADC values with the tumor Gleason score. MATERIALS AND METHODS Pretreatment DWI studies were analyzed retrospectively in 51 consecutive patients with prostate cancer with either two (b=0 and 1000 s/mm2; n=26 patients) or five (b=0, 50, 200, 1500, and 2000 s/mm2; n=25 patients) b values. In 45 normal peripheral-zone ROIs and 65 prostate cancer ROIs (14 in the central gland), ADC values were estimated by use of several combinations of two or five b values and a monoexponential model. We used the area under the receiver operating characteristic curve to characterize the effectiveness of ADC values in distinguishing prostate cancer from normal-tissue ROIs, and we calculated Spearman rank-order correlation between ADC values and the Gleason score. RESULTS ADC values were often significantly different (p<0.001) when estimated from different combinations of two or five b values. However, except when both b values were less than or equal to 200 mm2/s or greater than or equal to 1500 mm2/s, the AUC value for distinguishing prostate cancer from normal-tissue ROIs was similar (0.88-0.93). The correlation coefficients between ADC values and the Gleason score were between -0.30 and -0.68. CONCLUSION The choice of b values can significantly affect ADC estimates. ADC values can produce a similar discriminant performance in distinguishing prostate cancer from normal-tissue ROIs and in correlation with the Gleason score, but an appropriate ADC cutoff value needs to be selected specifically for each b-value combination.

Dynamic contrast-enhanced MR imaging features of the normal central zone of the prostate.

RATIONALE AND OBJECTIVES Evaluate qualitative dynamic contrast-enhanced magnetic resonance imaging (MRI) characteristics of normal central zone based on recently described central zone MRI features. MATERIALS AND METHODS Institutional review board-approved, Health Insurance Portability and Accountability Act compliant study, 59 patients with prostate cancer, histopathology proven to not involve central zone or prostate base, underwent endorectal MRI before prostatectomy. Two readers independently reviewed T2-weighted images and apparent diffusion coefficient (ADC) maps identifying normal central zone based on low signal intensity and location. Next, two readers drew bilateral central zone regions of interest on dynamic contrast-enhanced magnetic resonance images in consensus and independently recorded enhancement curve types as type 1 (progressive), type 2 (plateau), and type 3 (wash-out). Identification rates of normal central zone and enhancement curve type were recorded and compared for each reviewer. The institutional review board waiver was approved and granted 05/2010. RESULTS Central zone identified in 92%-93% of patients on T2-weighted images and 78%-88% on ADC maps without significant difference between identification rates (P = .63 and P = .15 and inter-reader agreement (κ) is 0.64 and 0.29, for T2-weighted images and ADC maps, respectively). All central zones were rated either curve type 1 or curve type 2 by both radiologists. No statistically significant difference between the two radiologists (P = .19) and inter-reader agreement was κ = 0.37. CONCLUSIONS Normal central zone demonstrates either type 1 (progressive) or type 2 (plateau) enhancement curves on dynamic contrast-enhanced MRI that can be potentially useful to differentiate central zone from prostate cancer that classically demonstrates a type 3 (wash-out) enhancement curve.

Prostate Volumes Derived From MRI and Volume-Adjusted Serum Prostate-Specific Antigen: Correlation With Gleason Score of Prostate Cancer

OBJECTIVE The purpose of this article is to study relationships between MRI-based prostate volume and volume-adjusted serum prostate-specific antigen (PSA) concentration estimates and prostate cancer Gleason score. MATERIALS AND METHODS The study included 61 patients with prostate cancer (average age, 63.3 years; range 52-75 years) who underwent MRI before prostatectomy. A semiautomated and MRI-based technique was used to estimate total and central gland prostate volumes, central gland volume fraction (central gland volume divided by total prostate volume), PSA density (PSAD; PSA divided by total prostate volume), and PSAD for the central gland (PSA divided by central gland volume). These MRI-based volume and volume-adjusted PSA estimates were compared with prostatectomy specimen weight and Gleason score by using Pearson (r) or Spearman (ρ) correlation coefficients. RESULTS The estimated total prostate volume showed a high correlation with reference standard volume (r = 0.94). Of the 61 patients, eight (13.1%) had a Gleason score of 6, 40 (65.6%) had a Gleason score of 7, seven (11.5%) had a Gleason score of 8, and six (9.8%) had a Gleason score of 9 for prostate cancer. The Gleason score was significantly correlated with central gland volume fraction (ρ = -0.42; p = 0.0007), PSAD (ρ = 0.46; p = 0.0002), and PSAD for the central gland (ρ = 0.55; p = 0.00001). CONCLUSION Central gland volume fraction, PSAD, and PSAD for the central gland estimated from MRI examinations show a modest but significant correlation with Gleason score and have the potential to contribute to personalized risk assessment for significant prostate cancer.

Hybrid multidimensional T2 and diffusion‐weighted MRI for prostate cancer detection

To study the dependence of apparent diffusion coefficient (ADC) and T2 on echo time (TE) and b‐value, respectively, in normal prostate and prostate cancer, using two‐dimensional MRI sampling, referred to as “hybrid multidimensional imaging.”

Magnetic resonance imaging of benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is a common condition in middle-aged and older men and negatively affects the quality of life. An ultrasound classification for BPH based on a previous pathologic classification was reported, and the types of BPH were classified according to different enlargement locations in the prostate. Afterwards, this classification was demonstrated using magnetic resonance imaging (MRI). The classification of BPH is important, as patients with different types of BPH can have different symptoms and treatment options. BPH types on MRI are as follows: type 0, an equal to or less than 25 cm3 prostate showing little or no zonal enlargements; type 1, bilateral transition zone (TZ) enlargement; type 2, retrourethral enlargement; type 3, bilateral TZ and retrourethral enlargement; type 4, pedunculated enlargement; type 5, pedunculated with bilateral TZ and/or retrourethral enlargement; type 6, subtrigonal or ectopic enlargement; type 7, other combinations of enlargements. We retrospectively evaluated MRI images of BPH patients who were histologically diagnosed and presented the different types of BPH on MRI. MRI, with its advantage of multiplanar imaging and superior soft tissue contrast resolution, can be used in BPH patients for differentiation of BPH from prostate cancer, estimation of zonal and entire prostatic volumes, determination of the stromal/glandular ratio, detection of the enlargement locations, and classification of BPH types which may be potentially helpful in choosing the optimal treatment.

A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

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.