Martin D. Pickles

Correlation of ADC and T2 Measurements With Cell Density in Prostate Cancer at 3.0 Tesla

Objectives:To assess the relationship between MRI derived parameters (apparent diffusion coefficient (ADC) and T2 relaxation time) and tumor cellularity as determined from whole mounted radical prostatectomy specimens, for both prostatic carcinoma and normal peripheral zone tissue. Materials and Methods:Over a 16-month period, 20 patients (mean age: 61 years, range: 42–70 years) were prospectively recruited. Diffusion and T2 imaging were performed on a 3.0 Tesla scanner to enable subsequent ADC and T2 calculation. After radical retropubic prostatectomy specimens were whole-mounted and regions of interest (ROIs) drawn in areas of prostatic carcinoma and normal peripheral zone. Cell density was then determined using an adaptive histogram thresholding technique. Differences in tissue type were explored using the unpaired t test while the relationship between parameters was assessed using scatter-plots and the Pearson correlation coefficient. Results:Significant differences (P < 0.0001 in all cases) were noted between peripheral zone tissue and prostatic carcinoma in terms of ADC (1.88 ± 0.22 vs. 1.43 ± 0.19 × 10−3 mm2/s), T2 (142 ± 24 vs. 109 ± 20 milliseconds), and cell density (9.4% ± 3.0% vs. 19.8% ± 5.3%). A significant negative correlation with cell density was noted for both ADC (R = −0.695, P < 0.0001) and T2 (R = −0.505, P = 0.001). Trends for increased cell density, decreased ADC, and decreased T2 with increasing Gleason score were also noted. Conclusions:ADC and to a lesser extent T2 are good indicators of cell density. Because of the potential link with Gleason score, MRI derived parameters may have a prognostic role with regard to potential metastatic activity and tumor aggressiveness.

Correlation of diffusion-weighted magnetic resonance data with cellularity in prostate cancer

To assess the relationship between the apparent diffusion coefficient (ADC) on magnetic resonance imaging (MRI) and cell density (CD) obtained from radical prostatectomy (RP) specimens.

Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy

Neoadjuvant chemotherapy has become the standard treatment for patients with locally advanced breast cancer; however a technique that can accurately differentiate responders from non-responders at an early time point during treatment has still to be identified. The purpose of this work was to evaluate the ability of pharmacokinetically modelled dynamic contrast-enhanced MRI data to predict and monitor response of patients diagnosed with locally advanced breast cancer to neoadjuvant chemotherapy, at an early time point during treatment. Sixty-eight patients with histology proven breast cancer underwent MRI examination prior to treatment, early during treatment and following the final cycle of chemotherapy. A two compartment pharmacokinetic model provided the kinetic parameters transfer constant (Ktrans), rate constant (Kep) and extracellular extravascular space (Ve) for a region of interest encompassing the whole lesion (ROIwhole) and a 3 × 3 pixel ‘hot-spot’ showing the greatest mean maximum percentage enhancement from within that region (ROIhs). Following treatment 48 patients were classified as responders and 20 as non-responders based on total tumour volume reduction. Tumour volume changes between the pre-treatment and early treatment time points demonstrated differences between responders and non-responders with percentage change revealing the most significant result (p < 0.001). Analysis based on ROIhsprovided more statistically significant differences between responders and non-responders then ROIwhole analysis. ROIhs analysis demonstrated differences between responders and non-responders both prior to and early during treatment. A highly significant reduction in both Ktrans and Kep (p < 0.001) was noted for responders between the pre-treatment and early treatment time points, while Ve significantly increased during the same time period for non-responders (p < 0.001). Quantification of dynamic contrast enhancement parameters provides a potential means for differentiating responders from non-responders early during their treatment, thereby allowing a prompt change in treatment if necessary.

Diffusion-weighted imaging of normal and malignant prostate tissue at 3.0T.

To measure the apparent diffusion coefficient (ADC) of normal and malignant prostate tissue at 3.0T using a phased‐array coil and parallel imaging, and determine the utility of ADC values in differentiating tumor from normal peripheral zone (PZ).

Diffusion imaging of the prostate at 3.0 tesla.

Objectives:We sought to assess the efficacy of diffusion imaging in the differential diagnosis of prostatic carcinoma using a 3.0 T scanner and parallel imaging technology. Materials and Methods:Diffusion-weighted images were acquired using a single shot echo-planar imaging sequence with b = 0 and 500 seconds/mm2. Apparent diffusion coefficient (ADCy) values were calculated in tumor and healthy-appearing peripheral zone for 62 patients. Diffusion tensor images were also acquired in 25 patients and mean diffusivity and fractional anisotropy determined. Results:Significant differences were noted between prostatic carcinoma (1.33 ± 0.32 × 10−3 mm2/s) and peripheral zone (1.86 ± 0.47 × 10−3 mm2/s) for ADCy. Significant differences between the 2 tissue types were also noted for mean diffusivity and fractional anisotropy. Utilizing a cut-off of 1.45 × 10−3 mm2/s for mean diffusivity, a sensitivity of 84% and a specificity of 80% were obtained. Conclusions:Diffusion imaging of the prostate was implemented at high magnetic field strength. Reduced ADC and increased fractional anisotropy values were noted in prostatic carcinoma.

Texture analysis in assessment and prediction of chemotherapy response in breast cancer.

To assess the efficacy of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients.

Voxel-by-Voxel Functional Diffusion Mapping for Early Evaluation of Breast Cancer Treatment

Quantitative isotropic diffusion MRI and voxel-based analysis of the apparent diffusion coefficient (ADC) changes have been demonstrated to be able to accurately predict early response of brain tumors to therapy. The ADC value changes measured during pre- and posttherapy interval are closely correlated to treatment response. This work was demonstrated using a voxel-based analysis of ADC change during therapy in the brains of both rats and humans, following rigidly registering pre- and post-therapeutic ADC MRI exams. The primary goal of this paper is to extend this voxel-by-voxel analysis to assess therapeutic response in breast cancer. Nonlinear registration (with higher degrees of freedom) between the pre- and post-treatment exams is needed to ensure that the corresponding voxels actually contain similar cellular partial contributions due to soft tissue deformations in the breast and compartmental tumor changes during treatment as well. With limited data sets, we have observed the correlation between changes of ADC values and treatment response also exists in breast cancers. With diffusion scans acquired at three different timepoints (pre-treatment, early post-treatment and late post-treatment), we have also shown that ADC changes across responders within 5 weeks are a function of time interval after the initiation of treatment. Comparison of the experimental results with pathology shows that ADC changes can be used to evaluate early response of breast cancer treatment.

Analysis of prostate DCE-MRI: comparison of fast exchange limit and fast exchange regimen pharmacokinetic models in the discrimination of malignant from normal tissue.

Objectives:The ability to detect and identify malignant lesions within the prostate with conventional T2-weighted imaging is still limited. Although lesion conspicuity is improved with dynamic contrast-enhanced imaging there still remains some ambiguity as all tissues within the prostate may enhance. The aim of the current study was to take advantage of the improved signal-to-noise ratio at 3 T and assess the ability of 2 alternative pharmacokinetic models to clearly identify malignant areas within the prostate. We also aspire to assess the impact of tissue heterogeneity on variation in estimated pharmacokinetic parameters. Materials and Methods:Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the prostate was implemented using multiple flip angles for T1 determination, and a rapid dynamic 3D T1-weighted acquisition with parallel imaging and a temporal resolution of 6.7 s. Pharmacokinetic analysis was performed for regions of tumor, normal-appearing peripheral zone (PZ), and central gland (CG) using fast exchange limit (FXL) or fast exchange regimen (FXR) models. Cell density was obtained from hematoxylin and eosin stained whole mount radical prostatectomy specimens. Results:Native tissue T1 was significantly lower in tumor and PZ tissue than in CG. The FXL model revealed increased mean Ktrans, kep, and ve in tumor and CG compared with PZ. With the FXR model, fitting was improved and all parameters were significantly increased, however, there were no longer significant differences between regions for ve. The additional parameter of the FXR model, &tgr;i, nominally representing mean lifetime of intracellular water, was significantly decreased in tumor compared with both PZ and CG. Rate constants for CG were significantly lower than those of tumor for both models. In addition, for all tissues, Ktrans and ve were positively correlated with cell density. Conclusions:Accounting for a finite water exchange rate between cells and their environment improves the discrimination of malignant from benign tissues within the prostate and may aid staging accuracy and ability to monitor response to treatment.

Minkowski functionals: An MRI texture analysis tool for determination of the aggressiveness of breast cancer.

This work aims to see whether Minkowski Functionals can be used to distinguish between cancer types before chemotherapy treatment has begun, and whether a response to treatment can be predicted by an initial scan alone.