10th Percentile Apparent Diffusion Coefficient Research Articles

Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study.

The objective of our study was to investigate associations between quantitative image features of multiparametric MRI of the prostate and PTEN expression of peripheral zone prostate cancer. A total of 45 peripheral zone cancer foci from 30 patients who had undergone multiparametric prostate MRI before prostatectomy were identified by a genitourinary pathologist and a radiologist who reviewed histologic findings and MR images. Histologic sections of cancer foci underwent immunohistochemical analysis and were scored according to the percentage of tumor-positive cells expressing PTEN as negative (0-20%), mixed (20-80%), or positive (80-100%). Average and 10th percentile apparent diffusion coefficient (ADC) values, skewness of T2-weighted signal intensity histogram, and quantitative perfusion parameters (i.e., forward volume transfer constant [K(trans)], extravascular extracellular volume fraction [ve], and reverse reflux rate constant between the extracellular space and plasma [k(ep)]) from the Tofts model were calculated for each cancer focus. Associations between the quantitative image features and PTEN expression were analyzed with the Spearman rank correlation coefficient (r). Analysis of the 45 cancer foci revealed that 21 (47%) were PTEN-positive, 12 (27%) were PTEN-negative, and 12 (27%) were mixed. There was a weak but significant negative correlation between Gleason score and PTEN expression (r = -0.30, p = 0.04) and between k(ep) and PTEN expression (r = -0.35, p = 0.02). There was no significant correlation between other multiparametric MRI features and PTEN expression. This preliminary study of radiogenomics of peripheral zone prostate cancer revealed weak-but significant-associations between the quantitative dynamic contrast-enhanced MRI feature k(ep) and Gleason score with PTEN expression. These findings warrant further investigation and validation with the aim of using multiparametric MRI to improve risk assessment of patients with prostate cancer.

Prostate Cancer: Utility of Whole-Lesion Apparent Diffusion Coefficient Metrics for Prediction of Biochemical Recurrence After Radical Prostatectomy.

The purpose of this study was to investigate the additional value of whole-lesion histogram apparent diffusion coefficient (ADC) metrics, when combined with standard pathologic features, in prediction of biochemical recurrence (BCR) after radical prostatectomy for prostate cancer. The study included 193 patients (mean age, 61 ± 7 years) who underwent 3-T MRI with DWI (b values, 50 and 1000 s/mm(2)) before prostatectomy. Histogram metrics were derived from 3D volumes of interest encompassing the entire lesion on ADC maps. Pathologic features from radical prostatectomy and subsequent BCR were recorded for each patient. The Fisher exact test and Mann-Whitney test were used to compare ADC-based metrics and pathologic features between patients with and patients without BCR. Stepwise logistic regression analysis was used to construct multivariable models for prediction of BCR, which were assessed by ROC analysis. BCR occurred in 16.6% (32/193) of patients. Variables significantly associated with BCR included primary Gleason grade, Gleason score, extraprostatic extension, seminal vesicle invasion, positive surgical margin, preoperative prostate-specific antigen level, MRI tumor volume, mean whole-lesion ADC, entropy ADC, and mean ADC of the bottom 10th, 10-25th, and 25-50th percentiles (p ≤ 0.019). Significant independent predictors of BCR at multivariable analysis were primary Gleason grade, extraprostatic extension, mean of the bottom 10th percentile ADC, and entropy ADC (p = 0.002-0.037). The AUC of this multivariable model was 0.94 for prediction of BCR; the AUC of pathologic features alone was 0.89 (p = 0.001). A model integrating whole-lesion ADC metrics had significantly higher performance for prediction of BCR than did standard pathologic features alone and may help guide postoperative prognostic assessments and decisions regarding adjuvant therapy.

Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI). We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis. Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement). These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.

Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response.

The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC.

Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade.

To explore the role of histogram analysis of apparent diffusion coefficient (ADC) MRI maps based on entire tumor volume data in determining pancreatic neuroendocrine tumor (PNT) grade. Retrospective evaluation of 22 patients with PNTs included low-grade (G1; n = 15), intermediate-grade (G2; n = 4), and high-grade (G3; n = 3) tumors. Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and summated to obtain histograms for entire tumor volume. Calculated histographic parameters included mean ADC (mADC), 5th percentile ADC, 10th percentile ADC, 25th percentile ADC, 50th percentile ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90) and 95th percentile ADC (ADC95), skewness and kurtosis. Histogram parameters were correlated with tumor grade by repeated measures analysis of variance with Tukey-Kramer post hoc comparisons. The mADC, ADC75, ADC90, and ADC95 were significantly higher in G1 tumors (1283 ± 267; 1404 ± 300; 1495 ± 318; 1562 ± 347 × 10(-6) mm(2)/s) compared to G2 (892 ± 390; 952 ± 381; 1036 ± 384; 1072 ± 374 × 10(-6) mm(2)/s) and to G3 tumors (733 ± 225; 864 ± 284; 1008 ± 288; 1152 ± 192 × 10(-6) mm(2)/s) (p value <0.05). Skewness and kurtosis were significantly different between G1 (0.041 ± 0.466; 2.802 ± 0.679) and G3 (1.01 ± 1.140; 5.963 ± 4.008) tumors (p value <0.05). Tumor volume (mL) was significantly higher on G3 (55 ± 15.7) compared to G1 (1.9 ± 2.7) and G2 (4.5 ± 3.6) tumors (p value <0.05). In this small sample size, we did not detect statistically significant parameters between G2 (n = 4) and G3 (n = 3) tumors. Histographic analysis of ADC maps on the basis of the entire tumor volume can be useful in differentiating histologic grades of PNTs.

P0191 Histogram analysis of apparent diffusion coefficients after neoadjuvant chemotherapy in breast cancer

Background The purpose of our study was to evaluate change of the apparent diffusion coefficient (ADC) histogram during the neoadjuvant chemotherapy (NAC) in breast cancer, and to compare changes between pathologically verified responders and non-responders. Methods 62 patients received NAC followed by surgery. We defined responders and non-responders based on 30% reduction in tumour cells using the Miller-Payne Grading System. All patients underwent 3T magnetic resonance (MR) with diffusion-weighted imaging (DWI) before the NAC and after the completion of two cycles of NAC. ADC histogram analysis encompassing the entire tumour was performed and various ADC parameters (mean, minimum, 10th, 25th, 50th, 75th, 90th percentile, maximum ADCs, skewness, and kurtosis) were obtained. Findings Mean, minimum, 10th, 25th, 50th, and 75th percentile ADCs significantly increased after NAC ( p =0.0129, p =0.0004, p =0.0047, p =0.0002, p =0.0006, p =0.0030) and maximum ADC significantly decreased (p=0.0003). Skewness changed into less positive ( p p =0.0168) after NAC. Although there was no statistical significance, mean, minimum, 10th, 25th, 50th, 75th, and 90th percentile ADCs tended to decrease more in responders compared with non-responders. Interpretation There is significant change in distribution of ADC after NAC. ADC histogram analysis quantitatively demonstrates the alterations more precisely during the treatment course.

Histogram Analysis of Apparent Diffusion Coefficients for Occult Tonsil Cancer in Patients with Cervical Nodal Metastasis from an Unknown Primary Site at Presentation.

To explore the added value of histogram analysis of apparent diffusion coefficient (ADC) values over magnetic resonance (MR) imaging and fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) for the detection of occult palatine tonsil squamous cell carcinoma (SCC) in patients with cervical nodal metastasis from a cancer of an unknown primary site. The institutional review board approved this retrospective study, and the requirement for informed consent was waived. Differences in the bimodal histogram parameters of the ADC values were assessed among occult palatine tonsil SCC (n = 19), overt palatine tonsil SCC (n = 20), and normal palatine tonsils (n = 20). One-way analysis of variance was used to analyze differences among the three groups. Receiver operating characteristic curve analysis was used to determine the best differentiating parameters. The increased sensitivity of histogram analysis over MR imaging and (18)F-FDG PET/CT for the detection of occult palatine tonsil SCC was evaluated as added value. Histogram analysis showed statistically significant differences in the mean, standard deviation, and 50th and 90th percentile ADC values among the three groups (P < .0045). Occult palatine tonsil SCC had a significantly higher standard deviation for the overall curves, mean and standard deviation of the higher curves, and 90th percentile ADC value, compared with normal palatine tonsils (P < .0167). Receiver operating characteristic curve analysis showed that the standard deviation of the overall curve best delineated occult palatine tonsil SCC from normal palatine tonsils, with a sensitivity of 78.9% (15 of 19 patients) and a specificity of 60% (12 of 20 patients). The added value of ADC histogram analysis was 52.6% over MR imaging alone and 15.8% over combined conventional MR imaging and (18)F-FDG PET/CT. Adding ADC histogram analysis to conventional MR imaging can improve the detection sensitivity for occult palatine tonsil SCC in patients with a cervical nodal metastasis originating from a cancer of an unknown primary site.

Endometrial Cancer: Combined MR Volumetry and Diffusion-weighted Imaging for Assessment of Myometrial and Lymphovascular Invasion and Tumor Grade

To investigate magnetic resonance (MR) volumetry of endometrial tumors and its association with deep myometrial invasion, tumor grade, and lymphovascular invasion and to assess the value of apparent diffusion coefficient (ADC) histographic analysis of the whole tumor volume for prediction of tumor grade and lymphovascular invasion. The institutional review board approved this retrospective study; patient consent was not required. Between May 2010 and May 2012, 70 women (mean age, 64 years; range, 24-91 years) with endometrial cancer underwent preoperative MR imaging, including axial oblique and sagittal T2-weighted, dynamic contrast material-enhanced, and diffusion-weighted imaging. Volumetry of the tumor and uterus was performed during the six sequences, with manual tracing of each section, and the tumor volume ratio (TVR) was calculated. ADC histograms were generated from pixel ADCs from the whole tumor volume. The threshold of TVR associated with myometrial invasion was assessed by using receiver operating characteristic curves. An independent sample Mann Whitney U test was used to compare differences in ADCs, skewness, and kurtosis between tumor grade and the presence of lymphovascular invasion. No significant difference in tumor volume and TVR was found among the six MR imaging sequences (P = .95 and .86, respectively). A TVR greater than or equal to 25% allowed prediction of deep myometrial invasion with sensitivity of 100% and specificity of 93% (area under the curve, 0.96; 95% confidence interval: 0.86, 0.99) at axial oblique diffusion-weighted imaging. A TVR of greater than or equal to 25% was associated with grade 3 tumors (P = .0007) and with lymphovascular invasion (P < .0001). There was no significant difference in the ADCs between grades 1 and 2 tumors (P > .05). The minimum, 10th, 25th, 50th, 75th, and 90th percentile ADCs were significantly lower in grade 3 tumors than in grades 1 and 2 tumors (P < .02). The combination of whole tumor volume and ADC can be used for prediction of tumor grade, lymphovascular invasion, and depth of myometrial invasion.

Prostate Cancer Aggressiveness: Assessment with Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient

To evaluate the relationship between prostate cancer aggressiveness and histogram-derived apparent diffusion coefficient (ADC) parameters obtained from whole-lesion assessment of diffusion-weighted magnetic resonance (MR) imaging of the prostate and to determine which ADC metric may help best differentiate low-grade from intermediate- or high-grade prostate cancer lesions. The institutional review board approved this retrospective HIPAA-compliant study of 131 men (median age, 60 years) who underwent diffusion-weighted MR imaging before prostatectomy for prostate cancer. Clinically significant tumors (tumor volume > 0.5 mL) were identified at whole-mount step-section histopathologic examination, and Gleason scores of the tumors were recorded. A volume of interest was drawn around each significant tumor on ADC maps. The mean, median, and 10th and 25th percentile ADCs were determined from the whole-lesion histogram and correlated with the Gleason score by using the Spearman correlation coefficient (ρ). The ability of each parameter to help differentiate tumors with a Gleason score of 6 from those with a Gleason score of at least 7 was assessed by using the area under the receiver operating characteristic curve (Az). In total, 116 clinically significant lesions (89 in the peripheral zone, 27 in the transition zone) were identified in 85 of the 131 patients (65%). Forty-six patients did not have a clinically significant lesion. For mean ADC, median ADC, 10th percentile ADC, and 25th percentile ADC, the Spearman ρ values for correlation with Gleason score were -0.31, -0.30, -0.36, and -0.35, respectively, whereas the Az values for differentiating lesions with a Gleason score of 6 from those with a Gleason score of at least 7 were 0.704, 0.692, 0.758, and 0.723, respectively. The Az of 10th percentile ADC was significantly higher than that of the mean ADC for all lesions and peripheral zone lesions (P = .0001). When whole-lesion histograms were used to derive ADC parameters, 10th percentile ADC correlated with Gleason score better than did other ADC parameters, suggesting that 10th percentile ADC may prove to be optimal for differentiating low-grade from intermediate- or high-grade prostate cancer with diffusion-weighted MR imaging.

Pre-Treatment Diffusion-Weighted MR Imaging for Predicting Tumor Recurrence in Uterine Cervical Cancer Treated with Concurrent Chemoradiation: Value of Histogram Analysis of Apparent Diffusion Coefficients

ObjectiveTo evaluate the value of apparent diffusion coefficient (ADC) histogram analysis for predicting tumor recurrence in patients with uterine cervical cancer treated with chemoradiation therapy (CRT).Materials and MethodsOur institutional review board approved this retrospective study and waived informed consent from each patient. Forty-two patients (mean age, 56 ± 14 years) with biopsy-proven uterine cervical squamous cell carcinoma who underwent both pre-treatment pelvic magnetic resonance imaging with a 3.0 T magnetic resonance scanner and concurrent CRT were included. All patients were followed-up for more than 6 months (mean, 36.4 ± 11.9 months; range 9.0-52.8 months) after completion of CRT. Baseline ADC parameters (mean ADC, 25th percentile, 50th percentile, and 75th percentile ADC values) of tumors were calculated and compared between the recurrence and no recurrence groups.ResultsIn the recurrence group, the mean ADC and 75th percentile ADC values of tumors were significantly higher than those of the no recurrence group (p = 0.043 and p = 0.008, respectively). In multivariate analysis, the 75th percentile ADC value of tumors was a significant predictor for tumor recurrence (p = 0.009; hazard ratio, 1.319). When the cut-off value of the 75th percentile ADC (0.936 × 10-3 mm2/sec) was used, the overall recurrence free survival rate above the cut-off value was significantly lower than that below the cut-off value (51.9% vs. 91.7%, p = 0.003, log-rank test).ConclusionPre-CRT ADC histogram analysis may serve as a biomarker for predicting tumor recurrence in patients with uterine cervical cancer treated with CRT.

Prostate Cancer Aggressiveness: In Vivo Assessment of MR Spectroscopy and Diffusion-weighted Imaging at 3 T

To determine the individual and combined performance of magnetic resonance (MR) spectroscopic imaging and diffusion-weighted (DW) imaging at 3 T in the in vivo assessment of prostate cancer aggressiveness by using histopathologically defined regions of interest on radical prostatectomy specimens to define the prostate cancer regions to be investigated. The local institutional ethics review board approved this retrospective study and waived the informed consent requirement. Fifty-four patients with biopsy-proved prostate cancer underwent clinical MR spectroscopic imaging followed by prostatectomy. Guided by the histopathologic map, all spectroscopy voxels that contained tumor tissue were selected, and metabolite ratios (choline [Cho] plus creatine [Cr]-to-citrate [Cit] and Cho/Cr ratios) were derived. For each spectroscopic voxel, 25th percentile apparent diffusion coefficient (ADC) of the region corresponding to that voxel was determined, representing the most aberrant tumor part on the ADC map, which was often smaller than spectroscopic imaging voxels. Maximum metabolic ratios and minimum 25th percentile ADC of each tumor were related to tumor aggressiveness and were used to differentiate aggressiveness classes. A logistic regression model (LRM) was used to combine data from both modalities. Significant correlation was found between aggressiveness classes and maximum Cho+Cr/Cit ratio (ρ=0.36), maximum Cho/Cr ratio (ρ=0.35), and minimum 25th percentile ADC (ρ=-0.63) in the peripheral zone (PZ). In the transition zone (TZ), the correlation was significant for only Cho+Cr/Cit and Cho/Cr ratios (ρ=0.58 and ρ=0.60, respectively). For differentiation between aggressiveness classes, LRM use did not result in significantly improved differentiation over any individual variables. These findings enabled confirmation that MR spectroscopic imaging and DW imaging offer potential for in vivo noninvasive assessment of prostate cancer aggressiveness, and both modalities have comparable performance. The combination did not result in better performance. Nonetheless, the better performances of metabolite ratios in the TZ and of ADCs in the PZ suggest that they have complementary value.

P2-08-06: Improved Spatial Resolution Diffusion-Weighted Imaging for Characterizing Tumors and Treatment Response in Patients with Invasive Breast Cancer.

Abstract Background: Diffusion weighted magnetic resonance imaging (DWI) is a non-invasive technique that is sensitive to tissue microstructure. Previous studies have shown that DWI adds positive predictive value in diagnostic studies of breast cancer and it has been shown to predict tumor response to neoadjuvant chemotherapy. While DWI shows promise for evaluating breast cancer, the technique suffers from limitations. Specifically, image distortion is common with the echo planar sequence available for DWI on clinical scanners, and spatial resolution is lower than that of other MRI sequences. Our group has optimized a high-resolution reduced field-of-view DWI acquisition, originally developed for the spine by Saritas et al., for breast imaging. The goal of this work was to compare high resolution (hr)-DWI) to standard resolution (std)-DWI for characterizing breast tumors. Methods: Patients undergoing neoadjuvant chemotherapy were scanned with MRI before, during and after neoadjuvant chemotherapy as part of IRB-approved studies at our institution. Nine women were scanned with both hr-DWI and std-DWI before and after one cycle of chemotherapy. Apparent diffusion coefficient (ADC) maps were calculated from hr-DWI and std-DWI data using previously described methods. One tumor region of interest (ROI) was defined on the hr-DWI slice estimated to contain the largest tumor area. This tumor ROI was then applied to the corresponding slice and location on the std-DWI and hr-DWI ADC maps. Mean tumor ADC as well as 15th, 25th, 50th, 75th, and 90th percentile ADCs were calculated for both DWI acquisitions for all subjects. Results: The mean tumor ADC values measured prior to treatment were similar for the hr-DWI and std-DWI acquisitions, however there was a significant difference between hr- and std-DWI 15th and 25th percentile ADC values (p= 0.0495, p=0.0717) For the early treatment time point, significant differences between the two DWI acquisitions were found for: mean tumor ADC, 15th, 25th, and 50th percentiles (p=0.0302, 0.0075, 0.0212, and 0.0488, respectively), with the most significant difference found for the lowest (15th) percentile measured. Tumor hr-DWI ADCs were consistently lower than std-DWI ADCs. Discussion: These data show that although the mean ADC values calculated from the pre-treatment hr-DWI and std-DWI are similar, the lower percentile (15th, and 25th) ADC values are significantly lower for the hr-DWI acquisition. Our results also showed larger difference in lower percentile ADC values between the two sequences after one cycle of chemotherapy. The differences in the lower percentile ADC values calculated from the hr-DWI are consistent with reduced partial voluming between viable tumor tissue, which is characterized low ADC values, and normal fibroglandular tissue. This may be particularly important for post-treatment ADC measurements where tumor size may decrease, potentially making partial volume effects more pronounced. Continuing studies are evaluating the relationship between low percentile ADC values from hr-DWI and tumor stage and response to treatment. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-08-06.