A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

Non-contrast renal magnetic resonance imaging to assess perfusion and corticomedullary differentiation in health and chronic kidney disease

AIMS Arterial spin labelling (ASL) MRI measures perfusion without administration of contrast agent. While ASL has been validated in animals and healthy volunteers (HVs), application to chronic kidney disease (CKD) has been limited. We investigated the utility of ASL MRI in patients with CKD. METHODS We studied renal perfusion in 24 HVs and 17 patients with CKD (age 22-77 years, 40% male) using ASL MRI at 3.0T. Kidney function was determined using estimated glomerular filtration rate (eGFR). T1 relaxation time was measured using modified look-locker inversion and xFB02;ow-sensitive alternating inversion recovery true-fast imaging and steady precession was performed to measure cortical and whole kidney perfusion. RESULTS T1 was higher in CKD within cortex and whole kidney, and there was association between T1 time and eGFR. No association was seen between kidney size and volume and either T1, or ASL perfusion. Perfusion was lower in CKD in cortex (136 ± 37 vs. 279 ± 69 ml/min/100 g; p < 0.001) and whole kidney (146 ± 24 vs. 221 ± 38 ml/min/100 g; p < 0.001). There was significant, negative, association between T1 longitudinal relaxation time and ASL perfusion in both the cortex (r = -0.75, p < 0.001) and whole kidney (r = -0.50, p < 0.001). There was correlation between eGFR and both cortical (r = 0.73, p < 0.01) and whole kidney (r = 0.69, p < 0.01) perfusion. CONCLUSIONS Significant differences in renal structure and function were demonstrated using ASL MRI. T1 may be representative of structural changes associated with CKD; however, further investigation is required into the pathological correlates of reduced ASL perfusion and increased T1 time in CKD

3D fusion of histology to multi-parametric MRI for prostate cancer imaging evaluation and lesion-targeted treatment planning

Multi-parametric magnetic resonance imaging (mpMRI) of localized prostate cancer has the potential to support detection, staging and localization of tumors, as well as selection, delivery and monitoring of treatments. Delineating prostate cancer tumors on imaging could potentially further support the clinical workflow by enabling precise monitoring of tumor burden in active-surveillance patients, optimized targeting of image-guided biopsies, and targeted delivery of treatments to decrease morbidity and improve outcomes. Evaluating the performance of mpMRI for prostate cancer imaging and delineation ideally includes comparison to an accurately registered reference standard, such as prostatectomy histology, for the locations of tumor boundaries on mpMRI. There are key gaps in knowledge regarding how to accurately register histological reference standards to imaging, and consequently further gaps in knowledge regarding the suitability of mpMRI for tasks, such as tumor delineation, that require such reference standards for evaluation. To obtain an understanding of the magnitude of the mpMRI-histology registration problem, we quantified the position, orientation and deformation of whole-mount histology sections relative to the formalin-fixed tissue slices from which they were cut. We found that (1) modeling isotropic scaling accounted for the majority of the deformation with a further small but statistically significant improvement from modeling affine transformation, and (2) due to the depth (mean±standard deviation (SD) 1.1±0.4 mm) and orientation (mean±SD 1.5±0.9°) of the sectioning, the assumption that histology sections are cut from the front faces of tissue slices, common in previous approaches, introduced a mean error of 0.7 mm. To determine the potential consequences of seemingly small registration errors such as described above, we investigated the impact of registration accuracy on the statistical power of imaging validation studies using a co-registered spatial reference standard (e.g. histology images) by deriving novel statistical power formulae that incorporate registration error. We illustrated, through a case study modeled on a prostate cancer imaging trial at our centre, that submillimeter differences in registration error can have a substantial impact on the required sample sizes (and therefore also the study cost) for studies aiming to detect mpMRI signal differences due to 0.5 – 2.0 cm3 prostate tumors. With the aim of achieving highly accurate mpMRI-histology registrations without disrupting the clinical pathology workflow, we developed a three-stage method for accurately registering 2D whole-mount histology images to pre-prostatectomy mpMRI that allowed flexible placement of cuts during slicing for pathology and avoided the assumption that histology sections are cut from the front faces of tissue slices. The method comprised a 3D reconstruction of histology images, followed by 3D–3D ex vivo–in vivo and in vivo–in vivo image transformations. The 3D reconstruction method minimized fiducial registration error between cross-sections of non-disruptive histology- and ex-vivo-MRI-visible strand-shaped fiducials to reconstruct histology images into the coordinate system of an ex vivo MR image. We quantified the mean±standard deviation target registration error of the reconstruction to be 0.7±0.4 mm, based on the post-reconstruction misalignment of intrinsic landmark pairs. We also compared our fiducial-based reconstruction to an alternative reconstruction based on mutual-information-based registration, an established method for multi-modality registration. We found that the mean target registration error for the fiducial-based method (0.7 mm) was lower than that for the mutual-information-based method (1.2 mm), and that the mutual-information-based method was less robust to initialization error due to multiple sources of error, including the optimizer and the mutual information similarity metric. The second stage of the histology–mpMRI registration used interactively defined 3D–3D deformable thin-plate-spline transformations to align ex vivo to in vivo MR images to compensate for deformation due to endorectal MR coil positioning, surgical resection and formalin fixation. The third stage used interactively defined 3D–3D rigid or thin-plate-spline transformations to co-register in vivo mpMRI images to compensate for patient motion and image distortion. The combined mean registration error of the histology–mpMRI registration was quantified to be 2 mm using manually identified intrinsic landmark pairs. Our data set, comprising mpMRI, target volumes contoured by four observers and co-registered contoured and graded histology images, was used to quantify the positive predictive values and variability of observer scoring of lesions following the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, the variability of target volume contouring, and appropriate expansion margins from target volumes to achieve coverage of histologically defined cancer. The analysis of lesion scoring showed that a PI-RADS overall cancer likelihood of 5, denoting “highly likely cancer”, had a positive predictive value of 85% for Gleason 7 cancer (and 93% for lesions with volumes \u3e0.5 cm3 measured on mpMRI) and that PI-RADS scores were positively correlated with histological grade (ρ=0.6). However, the analysis also showed interobserver differences in PI-RADS score of 0.6 to 1.2 (on a 5-point scale) and an agreement kappa value of only 0.30. The analysis of target volume contouring showed that target volume contours with suitable margins can achieve near-complete histological coverage for detected lesions, despite the presence of high interobserver spatial variability in target volumes. Prostate cancer imaging and delineation have the potential to support multiple stages in the management of localized prostate cancer. Targeted biopsy procedures with optimized targeting based on tumor delineation may help distinguish patients who need treatment from those who need active surveillance. Ongoing monitoring of tumor burden based on delineation in patients undergoing active surveillance may help identify those who need to progress to therapy early while the cancer is still curable. Preferentially targeting therapies at delineated target volumes may lower the morbidity associated with aggressive cancer treatment and improve outcomes in low-intermediate-risk patients. Measurements of the accuracy and variability of lesion scoring and target volume contouring on mpMRI will clarify its value in supporting these roles

Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions

Magnetic Resonance Imaging (MRI) Biomarkers for Therapeutic Response Prediction in Rectal Cancer

Prediction of chemoradiotherapy (CRT) response in rectal cancer would enable stratification of management whereby responders could undergo ‘watch-and-wait’ to avoid surgical morbidity, and non-responders could have early treatment intensification to improve therapeutic outcomes. Functional MRI can assess tumour function and heterogeneity, and may improve therapeutic response prediction. The aims of this PhD were to (i) prospectively evaluate multi-parametric MRI at 3.0 tesla in vivo combining diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI for prediction of CRT response and 2 year disease-free survival (DFS), and (ii) examine diffusion tensor imaging (DTI) MRI biomarkers of rectal cancer extent and heterogeneity at ultra-high field 11.7 tesla ex vivo in order to establish a pipeline for MRI biomarker discovery from ultra-high field to clinical field. Patients with locally advanced rectal cancer undergoing CRT followed by surgery underwent multi-parametric MRI before, during, and after CRT. A whole tumour voxelwise histogram analysis of apparent diffusion co-efficient (ADC) and Ktrans heterogeneity was performed and correlated with histopathology tumour regression grade. After CRT (before surgery) ADC 75th and 90th quantiles were significantly higher in responders than non-responders. Patients with higher Ktrans values after CRT or greater increase in Ktrans values from before to after CRT had a significantly higher risk of distant metastases, and lower 2 year DFS. Biobank tissue from patients with rectal cancer were examined at 11.7 tesla and DTI-MRI results correlated with histopathology. This work established a discovery framework for screening Biobank cancer tissue for novel MRI biomarkers of tumour extent and heterogeneity, and resulted in good preservation of tissue integrity and MRI-histopathology alignment. DTI-MRI derived fractional anisotropy (FA) was able to differentiate between tumour and desmoplasia, fibrous tissue, and muscularis propria, allowing for more accurate delineation of rectal cancer tumour extent and stromal heterogeneity ex vivo. In conclusion, DWI-MRI was predictive of CRT response, DCE-MRI was predictive of 2 year DFS, and DTI-MRI was able to more accurately define tumour extent and heterogeneity in rectal cancer. These findings could be useful for stratification of patients for individualised treatment based on accurate assessment of tumour extent and therapeutic response prediction