Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging

Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models

Computer-aided diagnosis (CAD) systems are increasingly being used in clinical settings to report multi-parametric magnetic resonance imaging (mp-MRI) of the prostate. Usually, CAD systems automatically highlight cancer-suspicious regions to the radiologist, reducing reader variability and interpretation errors. Nevertheless, implementing this software requires the selection of which mp-MRI parameters can best discriminate between malignant and non-malignant regions. To exploit functional information, some parameters are derived from dynamic contrast-enhanced (DCE) acquisitions. In particular, much CAD software employs pharmacokinetic features, such as K ^trans and k _ep, derived from the Tofts model, to estimate a likelihood map of malignancy. However, non-pharmacokinetic models can be also used to describe DCE-MRI curves, without any requirement for prior knowledge or measurement of the arterial input function, which could potentially lead to large errors in parameter estimation. In this work, we implemented an empirical function derived from the phenomenological universalities (PUN) class to fit DCE-MRI. The parameters of the PUN model are used in combination with T2-weighted and diffusion-weighted acquisitions to feed a support vector machine classifier to produce a voxel-wise malignancy likelihood map of the prostate. The results were all compared to those for a CAD system based on Tofts pharmacokinetic features to describe DCE-MRI curves, using different quality aspects of image segmentation, while also evaluating the number and size of false positive (FP) candidate regions. This study included 61 patients with 70 biopsy-proven prostate cancers (PCa). The metrics used to evaluate segmentation quality between the two CAD systems were not statistically different, although the PUN-based CAD reported a lower number of FP, with reduced size compared to the Tofts-based CAD. In conclusion, the CAD software based on PUN parameters is a feasible means with which to detect PCa, without affecting segmentation quality, and hence it could be successfully applied in clinical settings, improving the automated diagnosis process and reducing computational complexity.

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

OBJECTIVES

To compare the performance of experienced readers in detecting prostate cancer (PCa) using likelihood maps generated by a CAD system with that of unassisted interpretation of multiparametric magnetic resonance imaging (mp-MRI).

METHODS

Three experienced radiologists reviewed mp-MRI prostate cases twice. First, readers observed CAD marks on a likelihood map and classified as positive those suspicious for cancer. After 6 weeks, radiologists interpreted mp-MRI examinations unassisted, using their favourite protocol. Sensitivity, specificity, reading time and interobserver variability were compared for the two reading paradigms.

RESULTS

The dataset comprised 89 subjects of whom 35 with at least one significant PCa. Sensitivity was 80.9% (95% CI 72.1-88.0%) and 87.6% (95% CI 79.8-93.2; p = 0.105) for unassisted and CAD paradigm respectively. Sensitivity was higher with CAD for lesions with GS > 6 (91.3% vs 81.2%; p = 0.046) or diameter ≥10 mm (95.0% vs 80.0%; p = 0.006). Specificity was not affected by CAD. The average reading time with CAD was significantly lower (220 s vs 60 s; p < 0.001).

CONCLUSIONS

Experienced readers using likelihood maps generated by a CAD scheme can detect more patients with ≥10 mm PCa lesions than unassisted MRI interpretation; overall reporting time is shorter. To gain more insight into CAD-human interaction, different reading paradigms should be investigated.

KEY POINTS

• With CAD, sensitivity increases in patients with prostate tumours ≥10 mm and/or GS > 6. • CAD significantly reduces reporting time of multiparametric MRI. • When using CAD, a marginal increase of inter-reader agreement was observed.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Intravoxel Incoherent Motion Diffusion Weighted MR Imaging for Monitoring the Instantly Therapeutic Efficacy of Radiofrequency Ablation in Rabbit VX2 Tumors without Evident Links between Conventional Perfusion Weighted Images

OBJECTIVE

To investigate the intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) as a potential valuable marker to monitor the therapy responses of VX2 to radiofrequency ablation (RF Ablation).

METHODS

The institutional animal care and use committee approved this study. In 10 VX2 tumor-bearing rabbits, IVIM-DWI examinations were performed with a 3.0T imaging unit by using 16 b values from 0 to 800 sec/mm2. The true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) of tumors were compared between before and instantly after RF Ablation treatment. The differences of D, D* and f and conventional perfusion parameters (from perfusion CT and dynamic enhanced magnetic resonance imaging, DCE-MRI) in the coagulation necrosis area, residual unablated area, untreated area, and normal control had been calculated by compared t-test. The correlation between f or D* with perfusion weighted CT including blood flow, BF (milliliter per 100 mL/min), blood volume, BV (milliliter per 100 mL/min), and capillary permeability-surface area, PMB (as a fraction) or from DCE-MRI: transfer constant (Ktrans), extra-vascular extra-cellular volume fraction (Ve) and reflux constant (Kep) values had been analyzed by region-of-interest (ROI) methods to calculate Pearson's correlation coefficients.

RESULTS

In the ablated necrosis areas, f and D* significantly decreased and D significantly increased, compared with residual unblazed areas or untreated control groups and normal control groups (P < 0.001). The IVIM-DWI derived f parameters showed significant increases in the residual unablated tumor area. There was no significant correlations between f or D* and conventional perfusion parameters.

CONCLUSIONS

The IVIM-DW derived f, D and D* parameters have the potential to indicate therapy response immediately after RF Ablation treatment, while no significant correlations with classical tumor perfusion metrics were derived from DCE-MRI and perfusion-CT measurements.

Dynamic Contrast-Enhanced MRI in the Study of Brain Tumors. Comparison Between the Extended Tofts-Kety Model and a Phenomenological Universalities (PUN) Algorithm

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a well-established technique for studying blood-brain barrier (BBB) permeability that allows measurements to be made for a wide range of brain pathologies, including multiple sclerosis and brain tumors (BT). This latter application is particularly interesting, because high-grade gliomas are characterized by increased microvascular permeability and a loss of BBB function due to the structural abnormalities of the endothelial layer. In this study, we compared the extended Tofts-Kety (ETK) model and an extended derivate class from phenomenological universalities called EU1 in 30 adult patients with different BT grades. A total of 75 regions of interest were manually drawn on the MRI and subsequently analyzed using the ETK and EU1 algorithms. Significant linear correlations were found among the parameters obtained by these two algorithms. The means of R (2) obtained using ETK and EU1 models for high-grade tumors were 0.81 and 0.91, while those for low-grade tumors were 0.82 and 0.85, respectively; therefore, these two models are equivalent. In conclusion, we can confirm that the application of the EU1 model to the DCE-MRI experimental data might be a useful alternative to pharmacokinetic models in the study of BT, because the analytic results can be generated more quickly and easily than with the ETK model.

Joint fitting reveals hidden interactions in tumor growth

Tumor growth is often the result of the simultaneous development of two or more cancer cell populations. Crucial to the system evolution are the interactions between these populations. To obtain information about these interactions we apply the recently developed vector universality (VUN) formalism to various instances of competition between tumor populations. The formalism allows us (a) to quantify the growth mechanisms of a HeLa cell colony, describing the phenotype switching responsible for its fast expansion, (b) to reliably reconstruct the evolution of the necrotic and viable fractions in both in vitro and in vivo tumors using data for the time dependences of the total masses alone, and (c) to show how the shedding of cells leading to subspheroid formation is beneficial to both the spheroid and subspheroid populations, suggesting that shedding is a strong positive influence on cancer dissemination.

Visualization of thermal ablation lesions using cumulative dynamic contrast enhancement MRI

A novel robust and user friendly method for post-processing dynamic contrast enhanced (DCE) MRI data is presented, which provides reliable real-time delineation of the borders of thermal ablation lesions on low SNR images shortly after contrast agent injection without any model-based curve fitting. Some simple descriptors of the DCE process are calculated in a time efficient recursive manner and combined into a single image reflecting both current and previous enhancement states of each pixel, which allows robust discrimination between tissue areas with different perfusion properties. The resulting cumulative DCE (CDCE) images are shown to exhibit a strong correlation with histopathology and late gadolinium enhancement representations of the thermal damage in soft tissue. It is shown that the outer border of the non-perfused ablation lesion core on CDCE MRI corresponds to the histopathological lesion border. The described method has a potential not only to facilitate thermal ablation outcome assessment, but also to improve detection of infiltrative tumours and reduce the administered contrast agent dose in any DCE scans.