Magnetic Resonance Dispersion Imaging for Localization of Angiogenesis and Cancer Growth:

Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?

In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

Pharmacokinetic modeling of PSMA-targeted nanobubbles for quantification of extravasation and binding in mice models of prostate cancer

PURPOSE

Contrast-enhanced ultrasound (CEUS) by injection of microbubbles (MBs) has shown promise as a cost-effective imaging modality for prostate cancer (PCa) detection. More recently, nanobubbles (NBs) have been proposed as novel ultrasound contrast agents. Unlike MBs, which are intravascular ultrasound contrast agents, the smaller diameter of NBs allows them to cross the vessel wall and target specific receptors on cancer cells such as the prostate-specific membrane antigen (PSMA). It has been demonstrated that PSMA-targeted NBs can bind to the receptors of PCa cells and show a prolonged retention effect in dual-tumor mice models. However, the analysis of the prolonged retention effect has so far been limited to qualitative or semi-quantitative approaches.

METHODS

This work introduces two pharmacokinetics models for quantitative analysis of time-intensity curves (TICs) obtained from the CEUS loops. The first model is based on describing the vascular input by the modified local density random walk (mLDRW) model and independently interprets TICs from each tumor lesion. Differently, the second model is based on the reference-tissue model, previously proposed in the context of nuclear imaging, and describes the binding kinetics of an indicator in a target tissue by using a reference tissue where binding does not occur.

RESULTS

Our results show that four estimated parameters, β,β / λ $\beta /\lambda $ ,β + / β - ${\beta }_ + /{\beta }_ - $ , for the mLDRW-input model, and γ for the reference-based model, were significantly different (p-value <0.05) between free NBs and PSMA-NBs. These parameters estimated by the two models demonstrate different behaviors between PSMA-targeted and free NBs.

CONCLUSIONS

These promising results encourage further quantitative analysis of targeted NBs for improved cancer diagnostics and characterization.

Radiomic combination of spatial and temporal features extracted from DCE-MRI for prostate cancer detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:3153-3156. [PMID: 34891910 DOI: 10.1109/embc46164.2021.9630015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Multi-parametric MRI is part of the standard prostate cancer (PCa) diagnostic protocol. Recent imaging guidelines (PI-RADS v2) downgraded the value of Dynamic Contrast-Enhanced (DCE)-MRI in the diagnosis of PCa. A purely qualitative analysis of the DCE-MRI time series, as it is generally done by radiologists, might indeed overlook information on the microvascular architecture and function. In this study, we investigate the discriminative power of quantitative imaging features derived from texture and pharmacokinetic analysis of DCE-MRI. In 605 regions of interest (benign and malignant tissue) delineated in 80 patients, we found through independent cross-validation that a subset of quantitative spatial and temporal features extracted from DCE-MRI and incorporated in machine learning classifiers obtains a good diagnostic performance (AUC = 0.80-0.86) in distinguishing malignant from benign regions.Clinical Relevance- These findings highlight the underlying potential of quantitative DCE-derived radiomic features in identifying PCa by MRI.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Super-Resolution Contrast-Enhanced Ultrasound Methodology for the Identification of In Vivo Vascular Dynamics in 2D

OBJECTIVES

The aim of this study was to provide an ultrasound-based super-resolution methodology that can be implemented using clinical 2-dimensional ultrasound equipment and standard contrast-enhanced ultrasound modes. In addition, the aim is to achieve this for true-to-life patient imaging conditions, including realistic examination times of a few minutes and adequate image penetration depths that can be used to scan entire organs without sacrificing current super-resolution ultrasound imaging performance.

METHODS

Standard contrast-enhanced ultrasound was used along with bolus or infusion injections of SonoVue (Bracco, Geneva, Switzerland) microbubble (MB) suspensions. An image analysis methodology, translated from light microscopy algorithms, was developed for use with ultrasound contrast imaging video data. New features that are tailored for ultrasound contrast image data were developed for MB detection and segmentation, so that the algorithm can deal with single and overlapping MBs. The method was tested initially on synthetic data, then with a simple microvessel phantom, and then with in vivo ultrasound contrast video loops from sheep ovaries. Tracks detailing the vascular structure and corresponding velocity map of the sheep ovary were reconstructed. Images acquired from light microscopy, optical projection tomography, and optical coherence tomography were compared with the vasculature network that was revealed in the ultrasound contrast data. The final method was applied to clinical prostate data as a proof of principle.

RESULTS

Features of the ovary identified in optical modalities mentioned previously were also identified in the ultrasound super-resolution density maps. Follicular areas, follicle wall, vessel diameter, and tissue dimensions were very similar. An approximately 8.5-fold resolution gain was demonstrated in vessel width, as vessels of width down to 60 μm were detected and verified (λ = 514 μm). Best agreement was found between ultrasound measurements and optical coherence tomography with 10% difference in the measured vessel widths, whereas ex vivo microscopy measurements were significantly lower by 43% on average. The results were mostly achieved using video loops of under 2-minute duration that included respiratory motion. A feasibility study on a human prostate showed good agreement between density and velocity ultrasound maps with the histological evaluation of the location of a tumor.

CONCLUSIONS

The feasibility of a 2-dimensional contrast-enhanced ultrasound-based super-resolution method was demonstrated using in vitro, synthetic and in vivo animal data. The method reduces the examination times to a few minutes using state-of-the-art ultrasound equipment and can provide super-resolution maps for an entire prostate with similar resolution to that achieved in other studies.

Modified MR dispersion imaging in prostate dynamic contrast-enhanced MRI

BACKGROUND

An estimation of an intravascular dispersion parameter was previously proposed to improve the overall accuracy and precision of the model parameters, but the high computation complexity can limit its practical usability in prostate dynamic contrast-enhanced MRI (DCE-MRI).

PURPOSE

To compare and evaluate the model fitting uncertainty and error in the model parameter estimation using different DCE-MRI analysis models and to evaluate the ability of the intravascular dispersion parameter to delineate between noncancerous and cancerous prostate tissue in the transition and peripheral zones.

STUDY TYPE

Retrospective.

POPULATION

Fifty-three patients who underwent radical prostatectomy.

FIELD STRENGTH/SEQUENCE

3 T/3D RF-spoiled gradient echo sequence.

ASSESSMENT

The coefficient of variation was used to assess the model fitting uncertainty by adding random noise to the time-concentration curves, and the Akaike information criterion was used to assess the model fitting error. The parametric maps derived from four DCE-MRI analysis models were evaluated by evaluating the delineation between noncancerous tissue and prostate cancer or clinically significant prostate cancer.

STATISTICAL TESTS

The receiver operating curve analysis was performed to compare the ability to delineate between noncancerous and prostate cancer tissue in the transition and peripheral zones.

RESULTS

Both MR dispersion imaging (MRDI) and Weinmann analysis models had the maximum coefficient of variation in different tissue types, while the model fitting uncertainty of modified (m)MRDI was similar to the standard Toft model. In mMRDI, the model fitting error was minimum, and the delineation between noncancerous and clinically significant prostate cancer tissue was improved in both transition (area under the curve [AUC] = 0.92) and peripheral zones (AUC = 0.92), in comparison with MRDI (AUC = 0.89 and AUC = 0.85, respectively).

DATA CONCLUSION

The mMRDI showed promising results in detecting prostate cancer while maintaining a similar model fitting uncertainty.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1307-1317.

Convective-Dispersion Modeling in 3D Contrast-Ultrasound Imaging for the Localization of Prostate Cancer

Despite being the solid tumor with the highest incidence in western men, prostate cancer (PCa) still lacks reliable imaging solutions that can overcome the need for systematic biopsies. Dynamic contrast-enhanced ultrasound imaging (DCE-US) allows us to quantitatively characterize the vascular bed in the prostate, due to its ability to visualize an intravenously administered bolus of contrast agents. Previous research has demonstrated that DCE-US parameters related to the vascular architecture are useful markers for the localization of PCa lesions. In this paper, we propose a novel method to assess the convective dispersion (D) and velocity (v) of the contrast bolus spreading through the prostate from three-dimensional (3D) DCE-US recordings. By assuming that D and v are locally constant, we solve the convective-dispersion equation by minimizing the corresponding regularized least-squares problem. 3D multiparametric maps of D and v were compared with 3D histopathology retrieved from the radical prostatectomy specimens of six patients. With a pixel-wise area under the receiver operating characteristic curve of 0.72 and 0.80, respectively, the method shows diagnostic value for the localization of PCa.

Dynamics of angiogenesis and cellularity in rabbit VX2 tumors using contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging

A number of studies have demonstrated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be used to evaluate microvessel density (MVD), and may quantitatively reflect tumor angiogenesis. To investigate the dynamics, including angiogenesis and tumor cellularity, of rabbit VX2 tumors during the 4 weeks following tumor implantation, the present study used DCE-MRI combined with diffusion-weighted imaging (DWI) to scan the tumors at 3 days, and then at 1, 2, 3 and 4-week intervals, following tumor implantation. The dynamics, volume transfer coefficient (K^trans) and apparent diffusion coefficient (ADC) of the tumor parenchyma were analyzed. Furthermore, the associations between K^trans and MVD at 4 weeks after tumor implantation were analyzed. Tumor K^trans was positively correlated with MVD at 4 weeks (r=0.674, P<0.001). Following tumor implantation, the tumor K^trans level rose for 2 weeks and then began to decline, reaching its lowest point at 4 weeks (P<0.001). ADC values at 1 week were higher than at 3 days, but declined thereafter (P<0.001). Tumor necrosis appeared by 1 week after tumor implantation. The necrosis degree of tumor was gradually increased from the occurrence of necrosis within the 4-week time span of the present study (1 vs. 2 weeks, P=0.008; 2 vs. 3 weeks, P<0.001; 3 vs. 4 weeks, P<0.001). The present study identified that tumor angiogenesis is a dynamic process that serves a function in tumor growth, and that DCE-MRI may reflect tumor parenchymal MVD and be useful in evaluating angiogenesis.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance

PURPOSE

To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions.

MATERIALS AND METHODS

66 male patients (age 65.4±10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3T MRI scanner. All detected lesions were categorized based on ACR PI-RADS version 2 and divided into 2 groups (A: PI-RADS ≤3, n=32; B: PI-RADS >3, n=34). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (chi(2)) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow) as provided by a commercially available postprocessing software. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs.

RESULTS

Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p<.001). Ktrans and Kep were significantly higher in group B compared to group A (all p<.001). For chi(2), lowest results (representing highest goodness of fit) were found for intermediate pAIF (chi(2) 0.073). ROC analyses revealed comparable diagnostic accuracies for the different pAIFs, which were high for Ktrans and Kep and low for ve.

CONCLUSION

Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. Therefore, it is of great importance to consider this as potential artifact and thus keep AIF type selection constant in DCE-MRI studies.

3D surface-based registration of ultrasound and histology in prostate cancer imaging

Several transrectal ultrasound (TRUS)-based techniques aiming at accurate localization of prostate cancer are emerging to improve diagnostics or to assist with focal therapy. However, precise validation prior to introduction into clinical practice is required. Histopathology after radical prostatectomy provides an excellent ground truth, but needs accurate registration with imaging. In this work, a 3D, surface-based, elastic registration method was developed to fuse TRUS images with histopathologic results. To maximize the applicability in clinical practice, no auxiliary sensors or dedicated hardware were used for the registration. The mean registration errors, measured in vitro and in vivo, were 1.5±0.2 and 2.1±0.5mm, respectively.

Contrast dispersion imaging for cancer localization

Cancer growth is associated with angiogenic processes in many types of cancer. Several imaging strategies have therefore been developed that target angiogenesis as a marker for cancer localization. To this end, intravascular and extravascular tissue perfusion is typically assessed by dynamic contrast enhanced (DCE) ultrasound (US) and MRI. All the proposed strategies, however, overlook important changes in the microvascular architecture that result from angiogenic processes. To overcome these limitations, we have recently introduced a new imaging strategy that analyzes the intravascular dispersion kinetics of contrast agents spreading through the microvasculature. Contrast dispersion is mainly determined by microvascular multi-path trajectories, reflecting the underlying microvascular architecture. This paper reviews the results obtained for prostate cancer localization by US and MRI dispersion imaging, also presenting the latest new developments and future perspectives.

Closed-form solution of the convolution integral in the magnetic resonance dispersion model for quantitative assessment of angiogenesis

Prostate cancer (PCa) diagnosis and treatment is still limited due to the lack of reliable imaging methods for cancer localization. Based on the fundamental role played by angiogenesis in cancer growth and development, several dynamic contrast enhanced (DCE) imaging methods have been developed to probe tumor angiogenic vasculature. In DCE magnetic resonance imaging (MRI), pharmacokinetic modeling allows estimating quantitative parameters related to the physiology underlying tumor angiogenesis. In particular, novel magnetic resonance dispersion imaging (MRDI) enables quantitative assessment of the microvascular architecture and leakage, by describing the intravascular dispersion kinetics of an extravascular contrast agent with a dispersion model. According to this model, the tissue contrast concentration at each voxel is given by the convolution between the intravascular concentration, described as a Brownian motion process according to the convective-dispersion equation, with the interstitium impulse response, represented by a mono-exponential decay, and describing the contrast leakage in the extravascular space. In this work, an improved formulation of the MRDI method is obtained by providing an analytical solution for the convolution integral present in the dispersion model. The performance of the proposed method was evaluated by means of dedicated simulations in terms of estimation accuracy, precision, and computation time. Moreover, a preliminary clinical validation was carried out in five patients with proven PCa. The proposed method allows for a reduction by about 40% of computation time without any significant change in estimation accuracy and precision, and in the clinical performance.