Automated voxel-based analysis of volumetric dynamic contrast-enhanced CT data improves measurement of serial changes in tumor vascular biomarkers

Magnetic resonance imaging of anal cancer: tumor characteristics and early prediction of treatment outcome

PURPOSE

To analyze tumor characteristics derived from pelvic magnetic resonance imaging (MRI) of patients with squamous cell carcinoma of the anus (SCCA) before and during chemoradiotherapy (CRT), and to compare the changes in these characteristics between scans of responders vs. nonresponders to CRT.

METHODS

We included 52 patients with a pelvic 3T MRI scan prior to CRT (baseline scan); 39 of these patients received an additional scan during week 2 of CRT (second scan). Volume, diameter, extramural tumor depth (EMTD), and external anal sphincter infiltration (EASI) of the tumor were assessed. Mean, kurtosis, skewness, standard deviation (SD), and entropy values were extracted from apparent diffusion coefficient (ADC) histograms. The main outcome was locoregional treatment failure. Correlations were evaluated with Wilcoxon's signed rank-sum test and Pearson's correlation coefficient, quantile regression, univariate logistic regression, and area under the ROC curve (AUC) analyses.

RESULTS

In isolated analyses of the baseline and second MRI scans, none of the characteristics were associated with outcome. Comparison between the scans showed significant changes in several characteristics: volume, diameter, EMTD, and ADC skewness decreased in the second scan, although the mean ADC increased. Small decreases in volume and diameter were associated with treatment failure, and these variables had the highest AUC values (0.73 and 0.76, respectively) among the analyzed characteristics.

CONCLUSION

Changes in tumor volume and diameter in an early scan during CRT could represent easily assessable imaging-based biomarkers to eliminate the need for analysis of more complex MRI characteristics.

Transformational Role of Medical Imaging in (Radiation) Oncology

Simple Summary

Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints.

Abstract

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.

Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques

Imaging in radiation oncology is essential for the evaluation of treatment response in tumors and organs at risk. This influences further treatment decisions and could possibly be used to adapt therapy. This review article focuses on the currently used imaging modalities for response assessment in radiation oncology and gives an overview of new and promising techniques within this field.

Accuracy and Performance of Functional Parameter Estimation Using a Novel Numerical Optimization Approach for GPU-Based Kinetic Compartmental Modeling

Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {K_trans, K_ep, V_b, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10⁻⁸%, 10⁻⁷%, and ×10⁻⁶%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was <10%.

Unified platform for multimodal voxel-based analysis to evaluate tumour perfusion and diffusion characteristics before and after radiation treatment evaluated in metastatic brain cancer

OBJECTIVE:

Early changes in tumour behaviour following stereotactic radiosurgery) are potential biomarkers of response. To-date quantitative model-based measures of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI parameters have shown widely variable findings, which may be attributable to variability in image acquisition, post-processing and analysis. Big data analytic approaches are needed for the automation of computationally intensive modelling calculations for every voxel, independent of observer interpretation.

METHODS:

This unified platform is a voxel-based, multimodality architecture that brings complimentary solute transport processes such as perfusion and diffusion into a common framework. The methodology was tested on synthetic data and digital reference objects and consequently evaluated in patients who underwent volumetric DCE-CT, DCE-MRI and DWI-MRI scans before and after treatment. Three-dimensional pharmacokinetic parameter maps from both modalities were compared as well as the correlation between apparent diffusion coefficient (ADC) values and the extravascular, extracellular volume (V_e). Comparison of histogram parameters was done via Bland-Altman analysis, as well as Student's t-test and Pearson's correlation using two-sided analysis.

RESULTS:

System testing on synthetic Tofts model data and digital reference objects recovered the ground truth parameters with mean relative percent error of 1.07 × 10^-7 and 5.60 × 10^-4 respectively. Direct voxel-to-voxel Pearson's analysis showed statistically significant correlations between CT and MR which peaked at Day 7 for K_trans (R = 0.74, p <= 0.0001). Statistically significant correlations were also present between ADC and Ve derived from both DCE-MRI and DCE-CT with highest median correlations found at Day 3 between median ADC and Ve,_MRI values (R = 0.6, p < 0.01) The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxelwise T₁₀ maps (R = 0.575, p < 0.001) instead of assigning a fixed T₁₀ value.

CONCLUSION:

The unified implementation of multiparametric transport modelling allowed for more robust and timely observer-independent data analytics. Utility of a common analysis platform has shown higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

ADVANCES IN KNOWLEDGE:

Utility of a common analysis platform has shown statistically higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

Detectability of radiation-induced changes in magnetic resonance biomarkers following stereotactic radiosurgery: A pilot study

Our objective was to investigate direct voxel-wise relationship between dose and early MR biomarker changes both within and in the high-dose region surrounding brain metastases following stereotactic radiosurgery (SRS). Specifically, we examined the apparent diffusion coefficient (ADC) from diffusion-weighted imaging and the contrast transfer coefficient (K^trans) and volume of extracellular extravascular space (v_e) derived from dynamic contrast-enhanced (DCE) MRI data. We investigated 29 brain metastases in 18 patients using 3 T MRI to collect imaging data at day 0, day 3 and day 20 following SRS. The ADC maps were generated by the scanner and K^trans and v_e maps were generated using in-house software for dynamic tracer-kinetic analysis. To enable spatially-correlated voxel-wise analysis, we developed a registration pipeline to register all ADC, K^trans and v_e maps to the planning MRI scan. To interrogate longitudinal changes, we computed absolute ΔADC, ΔK^trans and Δv_e for day 3 and 20 post-SRS relative to day 0. We performed a Kruskall-Wallice test on each biomarker between time points and investigated dose correlations within the gross tumour volume (GTV) and surrounding high dose region > 12 Gy via Spearman’s rho. Only v_e exhibited significant differences between day 0 and 20 (p < 0.005) and day 3 and 20 (p < 0.05) within the GTV following SRS. Strongest dose correlations were observed for ADC within the GTV (rho = 0.17 to 0.20) and weak correlations were observed for ADC and K^trans in the surrounding > 12 Gy region. Both ΔK^trans and Δv_e showed a trend with dose at day 20 within the GTV and > 12 Gy region (rho = -0.04 to -0.16). Weak dose-related decreases in K^trans and v_e within the GTV and high dose region at day 20 most likely reflect underlying vascular responses to radiation. Our study also provides a voxel-wise analysis schema for future MR biomarker studies with the goal of elucidating surrogates for radionecrosis.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. K^trans, v_e) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known K^trans and v_e values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain K^trans and v_e kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of K^trans and 84% of v_e algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in v_e of the primary gross tumor volume with time. Algorithmic differences in K^trans and v_e values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study

PURPOSE

We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation.

METHODS

Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis.

RESULTS

With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ _c) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement.

CONCLUSIONS

NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery

Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T₁₀, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for K_trans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T₁₀ maps (R = 0.575, P < .001) instead of assigning a fixed T₁₀ value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median K_trans (R = 0.42, P = .01), median area under the enhancement curve (iAUC₉₀) (R = 0.55, P < .01), and median iAUC₉₀ skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise K_trans, iAUC₉₀, and v_e values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T₁₀ maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps.

A prospective study of DWI, DCE-MRI and FDG PET imaging for target delineation in brachytherapy for cervical cancer

BACKGROUND AND PURPOSE

We examined the utility of dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted MRI (DWI), and FDG-PET imaging for brachytherapy target delineation in patients with locally advanced cervical cancer.

MATERIALS AND METHODS

Twenty-two patients had DWI, DCE-MRI, and FDG-PET/CT scans after brachytherapy applicator insertion, in addition to standard T2-weighted (T2w) 3T MRI. Gross tumor volume (GTV_B) and high-risk clinical target volume (HRCTV) were contoured first on T2w images, and then modified if indicated upon review of DWI/DCE-MRI/FDG-PET images by two observers. The primary endpoint was utility, determined by the number of patients whose volumes were modified, and interobserver variability.

RESULTS

Eleven patients' T2w-GTV_B were modified based on DWI/DCE-MRI/FDG-PET by observer 1, due to clearer demarcation (7) and residual disease not well visualized on T2w MRI (4). GTV_B was modified in 17 patients by observer 2 (11 and 6, respectively). Incorporation of functional imaging improved the conformity index (CI) for GTV_B from 0.54 (T2w alone) to 0.65 (P=0.003). HRCTV was modified in 3 and 8 patients by observers 1 and 2, respectively, with a trend toward higher CI using functional imaging (0.71 to 0.76, P=0.06).

CONCLUSIONS

DWI/DCE-MRI/FDG-PET imaging as a supplement to T2w MRI decreased interobserver variability in GTV_B delineation.

Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib

Objectives

To evaluate the feasibility of 4-dimensional perfusion computed tomography (CT) as an imaging biomarker for patients with hepatocellular carcinoma and metastatic liver disease.

Methods and materials

Patients underwent volumetric dynamic contrast-enhanced CT on a 320-slice scanner before and during stereotactic body radiation therapy and sorafenib, and at 1 and 3 months after treatment. Quiet free breathing was used in the CT acquisition and multiple techniques (rigid or deformable registration as well as outlier removal) were applied to account for residual liver motion. Kinetic modeling was performed on a voxel-by-voxel basis in the gross tumor volume and normal liver resulting in 3-dimensional parameter maps of blood perfusion, capillary permeability, blood volume, and mean transit time. Perfusion characteristics in the tumor and adjacent liver were correlated with radiation dose distributions to evaluate dose-response. Paired t tests assessed change in spatial and histogram parameters from baseline to different time points during and after treatment. Technique reproducibility as well as the impact of arterial and portal vein input functions was also investigated using intra- and inter-subject variance and Bland-Altman analysis.

Results

Quantitative perfusion parameters were reproducible (±5.7%; range, 2%-10%) depending on tumor/normal liver type and kinetic parameter. Statistically significant reductions in tumor perfusion were measurable over the course of treatment and as early as 1 week after sorafenib administration (P < .05). Marked liver parenchyma perfusion reduction was seen with a strong dose-response effect (R² = 0.95) that increased significantly over the course treatment.

Conclusions

The proposed methodology demonstrated feasibility of evaluating spatiotemporal changes in liver tumor perfusion and normal liver function following antiangiogenic therapy and radiation treatment warranting further evaluation of biomarker prognostication.

Functional imaging for radiotherapy treatment planning: current status and future directions-a review

In recent years, radiotherapy (RT) has been subject to a number of technological innovations. Today, RT is extremely flexible, allowing irradiation of tumours with high doses, whilst also sparing normal tissues from doses. To make use of these additional degrees of freedom, integration of functional image information may play a key role (i) for better staging and tumour detection, (ii) for more accurate RT target volume delineation, (iii) to assess functional information about biological characteristics and individual radiation resistance and (iv) to apply personalized dose prescriptions. In this article, we discuss the current status and future directions of different clinically available functional imaging modalities; CT, MRI, positron emission tomography (PET) as well as the hybrid imaging techniques PET/CT and PET/MRI and their potential for individualized RT.

Multimodality functional imaging in radiation therapy planning: relationships between dynamic contrast-enhanced MRI, diffusion-weighted MRI, and 18F-FDG PET

OBJECTIVES

Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective.

MATERIALS AND METHODS

The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response.

RESULTS

ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas.

CONCLUSION

A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets.