Diffusion MRI outlined viable tumour volume beats GTV in intra-treatment stratification of outcome

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning

PURPOSE

The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

METHODS

Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature.

RESULTS

Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines.

CONCLUSIONS

Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Geometric distortions in clinical MRI sequences for radiotherapy: insights gained from a multicenter investigation

BACKGROUND

As magnetic resonance imaging (MRI) becomes increasingly integrated into radiotherapy (RT) for enhanced treatment planning and adaptation, the inherent geometric distortion in acquired MR images pose a potential challenge to treatment accuracy. This study aimed to evaluate the geometric distortion levels in the clinical MRI protocols used across Danish RT centers and discuss influence of specific sequence parameters. Based on the variety in geometric performance across centers, we assess if harmonization of MRI sequences is a relevant measure.

MATERIALS AND METHODS

Nine centers participated with 12 MRI scanners and MRI-Linacs (MRL). Using a travelling phantom approach, a reference MRI sequence was used to assess variation in baseline distortion level between scanners. The phantom was also scanned with local clinical MRI sequences for brain, head/neck (H/N), abdomen, and pelvis. The influence of echo time, receiver bandwidth, image weighting, and 2D/3D acquisition was investigated.

RESULTS

We found a large variation in geometric accuracy across 93 clinical sequences examined, exceeding the baseline variation found between MRI scanners (σ = 0.22 mm), except for abdominal sequences where the variation was lower. Brain and abdominal sequences showed lowest distortion levels ([0.22, 2.26] mm), and a large variation in performance was found for H/N and pelvic sequences ([0.19, 4.07] mm). Post hoc analyses revealed that distortion levels decreased with increasing bandwidth and a less clear increase in distortion levels with increasing echo time. 3D MRI sequences had lower distortion levels than 2D (median of 1.10 and 2.10 mm, respectively), and in DWI sequences, the echo-planar imaging read-out resulted in highest distortion levels.

CONCLUSION

There is a large variation in the geometric distortion levels of clinical MRI sequences across Danish RT centers, and between anatomical sites. The large variation observed makes harmonization of MRI sequences across institutions and adoption of practices from well-performing anatomical sites, a relevant measure within RT.

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group

BACKGROUND AND PURPOSE

The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it.

MATERIALS AND METHODS

Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group.

RESULTS

The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types.

CONCLUSION

Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI

Objective.In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization.Approach.Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing:T₂-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome.Main Results.The framework was able to classify the two pancreatic tumor types with an area under curve (AUC) of 0.999,P< 0.001 and predict the tumor volume change with a correlation coefficient of 0.513,P= 0.034. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74,P= 0.065.Significance.A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.

A novel approach for dose painting radiotherapy of brain metastases guided by mr perfusion images

Purpose

To investigate the feasibility and dosimetric index features of dose painting guided by perfusion heterogeneity for brain metastasis (BMs) patients.

Methods

A total of 50 patients with single BMs were selected for this study. CT and MR simulation images were obtained, including contrast-enhanced T1-weighted images (T1WI+C) and cerebral blood flow (CBF) maps from 3D-arterial spin labeling (ASL). The gross tumor volume (GTV) was determined by fusion of CT and T1WI+C images. Hypoperfused subvolumes (GTVH) with less than 25% of the maximum CBF value were defined as the dose escalation region. The planning target volume (PTV) and PTVH were calculated from GTV and GTVH respectively. The PTVN was obtained by subtracting PTVH from PTV, and conventional dose was given. Three kinds of radiotherapy plans were designed based on the CBF values. Plan 1 was defined as the conventional plan with an arbitrary prescription dose of 60 Gy for PTV. For dose painting, Plan 2 and Plan 3 escalated the prescription dose for PTVH to 72 Gy based on Plan 1, but Plan 3 removed the maximum dose constraint. Dosimetric indices were compared among the three plans.

Results

The mean GTV volume was 34.5 (8.4-118.0) cm3, and mean GTVH volume was 17.0 (4.5-58.3) cm3, accounting for 49.3% of GTV. Both conventional plan and dose painting plans achieved 98% target coverage. The conformity index of PTVH were 0.44 (Plan1), 0.64 and 0.72 (Plan 2 and Plan 3, P&lt;0.05). Compared to Plan 1, the D2%, D98% and Dmean values of the PTVH escalated by 20.50%, 19.32%, and 19.60% in Plan 2 and by 24.88%, 17.22% and 19.22% in Plan 3 respectively (P&lt;0.05). In the three plans, the index of achievement value for PTVH was between 1.01 and 1.03 (P&lt;0.05). The dose increment rates of Plan 2 and Plan 3 for each organs at risk (OARs) was controlled at 2.19% - 5.61% compared with Plan 1. The doses received by OARs did not significantly differ among the three plans (P &gt;0.05).

Conclusions

BMs are associated with significant heterogeneity, and effective escalation of the dose delivered to target subvolumes can be achieved with dose painting guided by 3D-ASL without extra doses to OARs.

The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study

OBJECTIVE

To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.

METHODS

We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.

RESULTS

Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response.

CONCLUSION

The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.

KEY POINTS

• Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters Ktrans, ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans, Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

Apparent diffusion coefficient measurement of the parotid gland parenchyma

The measurements of apparent diffusion coefficient (ADC) with diffusion weighted magnetic resonance imaging (DW-MRI) is becoming a popular diagnostic and research tool for examination of parotid glands. However, there is little agreement between the reported ADC values of the parotid gland in published literature. In this review 43 studies on ADC measurement of the parotid glands were included. The analyses indicated several possible culprits of the observed ADC discrepancies. For example, DW-MRI examinations under gustatory stimulation gives higher ADC values compared to the unstimulated parotid gland (P=0.003). The diffusion weighting factors (b-values) can either increase (b-value <200 s/mm²) or decrease ADC values (b-values >1,000 s/mm²). The timing of follow-up DW-MRI after radiotherapy (RT) indicates correlation to the found ADC values (R² =0.39). Interestingly, the choice of regions of interest (ROI) appears not to affect the measurements of ADC (P=0.75). It can be concluded that there is a critical need for standardization of ADC measurement of the parotid glands to allow valid inter-study comparisons and eventually to reach consensus on the use of ADC as biomarker.

Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.

Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma

Background:

Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome.

Methods:

38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADC_T1C, ADC_T2-FLAIR) and perfusion fraction (f_T1C, f_T2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated.

Results:

Higher ADC_T2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01–1.15, p = 0.025], lower f_T2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04–4.27, p = 0.018), and lack of increase in ADC_T2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02–1.22, p = 0.02) were associated with early progression. Combining ADC_T2-FLAIR at baseline, f_T2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher f_T2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56–0.95, p = 0.018) was associated with longer PFS.

Conclusion:

ADC_T2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or f_T2-FLAIR at fraction 10 significantly predicted early progression. f_T2-FLAIR at fraction 10 was associated with PFS.