Diffusion-weighted imaging on an MRI-linear accelerator to identify adversely prognostic tumour regions in glioblastoma during chemoradiation

MR-linac: role of artificial intelligence and automation

The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs. Despite these improvements, MR-guided radiotherapy (MRgRT) remains labor intensive and time consuming, highlighting the need for AI to streamline workflows and support rapid decision-making. Synthetic CTs from MR images and automated contouring and treatment planning will reduce manual processes, thus optimizing treatment times and expanding access to MR-linac technology. AI-driven quality assurance will ensure patient safety by predicting machine errors and validating treatment delivery. Advances in intrafractional motion management will increase the accuracy of treatment, and the integration of imaging biomarkers for outcome prediction and early toxicity assessment will enable more precise and effective treatment strategies.

Clinical validation of a prognostic preclinical magnetic resonance imaging biomarker for radiotherapy outcome in head-and-neck cancer

PURPOSE

To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy.

MATERIAL AND METHODS

A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV, ADCmean and clinical factors.

RESULTS

The best model was defined by 800

CONCLUSIONS

A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches.

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Key Words

• DW-MRI
• Dose painting
• Prognostic biomarker
• Quantitative MRI
• Radiotherapy

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MESH Headings

• Humans
• Head and Neck Neoplasms/radiotherapy
• Head and Neck Neoplasms/diagnostic imaging
• Head and Neck Neoplasms/pathology
• Male
• Female
• Prognosis
• Middle Aged
• Aged
• Diffusion Magnetic Resonance Imaging/methods
• Chemoradiotherapy
• Adult
• Treatment Outcome
• Magnetic Resonance Imaging/methods
• Tumor Burden

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Affiliation(s)

René M Winter Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
Simon Boeke Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany
Sara Leibfarth Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
Jonas Habrich Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
Kerstin Clasen Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
Konstantin Nikolaou Diagnostic and Interventional Radiology, Department of Radiology, University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
Daniel Zips Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany
Daniela Thorwarth Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Cluster of Excellence "Machine Learning", University of Tübingen, Tübingen, Germany.

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Precision radiotherapy with molecular-profiling of CNS tumours

Diagnoses of CNS malignancies in the primary and metastatic setting have significantly advanced in the last decade with the advent of molecular pathology. Using a combination of immunohistochemistry, next-generation sequencing, and methylation profiling integrated with traditional histopathology, patient prognosis and disease characteristics can be understood to a much greater extent. This has recently manifested in predicting response to targeted drug therapies that are redefining management practices of CNS tumours. Radiotherapy, along with surgery, still remains an integral part of treating the majority of CNS tumours. However, the rapid advances in CNS molecular diagnostics have not yet been effectively translated into improving CNS radiotherapy. We explore several promising strategies under development to integrate molecular oncology into radiotherapy, and explore future directions that can serve to use molecular diagnostics to personalize radiotherapy. Evolving the management of CNS tumours with molecular profiling will be integral to supporting the future of precision radiotherapy.

Magnetic Resonance Imaging Relaxometry for Glioblastoma Response Assessment During Radiation Therapy on a 0.35 T Magnetic Resonance Imaging Linear Accelerator

PURPOSE

The integration of magnetic resonance imaging (MRI) and linear accelerator (MRI-Linac) enables daily imaging during radiation therapy (RT). This study implements MRI-Linac relaxometry to evaluate quantitative imaging changes in patients with glioblastoma during RT and identify associations with disease progression and survival outcomes.

METHODS AND MATERIALS

Thirty-eight patients with glioblastoma were treated on a 0.35 T MRI-Linac with Strategically Acquired Gradient Echo and T2 multiecho acquisitions every other day. Per voxel changes in tumor T2, T2*, and T1 values were assessed by parametric response mapping comparing each treatment fraction with pre-RT baselines. Statistical analyses included the Wilcoxon test for group comparisons and Cox proportional hazards models for survival associations.

RESULTS

Progressors had higher proportions of voxels with increased T2 values at week 2 (49% vs 40%, P = .008) and week 6 (58% vs 43%, P = .012) and higher T2* values at week 1 (47% vs 43%, P = .016), week 2 (48% vs 43%, P = .016), week 3 (50% vs 44%, P = .012), and the final week (53% vs 43%, P = .021). Cox modeling linked increased T2 values at week 4 with overall survival (hazard ratio [HR], 4.72; 95% CI, 1.24-12.9) and progression-free survival (HR, 9.26; 95% CI, 1.88-24.5). Increased T2* values at weeks 2 and 3 correlated with progression-free survival (HR, 5.02; 95% CI, 1.44-17.6; HR, 6.04; 95% CI, 1.59-22.9) and overall survival at week 3 (HR, 3.09; 95% CI, 0.94-10.1).

CONCLUSIONS

Quantitative changes in T2 and T2* values during RT, particularly in weeks 3 to 4, were associated with progression and survival outcomes. Early detection of poor responders may enable therapy adaptation, improving glioblastoma treatment outcomes.

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas

PURPOSE

Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.

METHODS

We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of vision transformer (ViT) layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2-fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N = 400), validation (N = 50), and test (N = 51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models with the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE).

RESULTS

The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT-PSNR: 31.0 ± 2.1, MSE: 0.009 ± 0.0005, SSIM: 0.950 ± 0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR-ViT model performs favorably against the ground truth data.

CONCLUSION

We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.

Magnetic resonance imaging techniques for monitoring glioma response to chemoradiotherapy

PURPOSE

Treatment response assessment for gliomas currently uses changes in tumour size as measured with T1- and T2-weighted MRI. However, changes in tumour size may occur many weeks after therapy completion and are confounded by radiation treatment effects. Advanced MRI techniques sensitive to tumour physiology may provide complementary information to evaluate tumour response at early timepoints during therapy. The objective of this review is to provide a summary of the history and current knowledge regarding advanced MRI techniques for early treatment response evaluation in glioma.

METHODS

The literature survey included perfusion MRI, diffusion-weighted imaging, quantitative magnetization transfer imaging, and chemical exchange transfer MRI. Select articles spanning the history of each technique as applied to treatment response evaluation in glioma were chosen. This report is a narrative review, not formally systematic.

RESULTS

Chemical exchange saturation transfer imaging potentially offers the earliest method to detect tumour response due to changes in metabolism. Diffusion-weighted imaging is sensitive to changes in tumour cellularity later during radiotherapy and is prognostic for progression-free and overall survival. Substantial evidence suggests that perfusion MRI can differentiate between tumour recurrence and treatment effect, but consensus regarding acquisition, processing, and interpretation is still lacking. Magnetization transfer imaging shows promise for detecting subtle white matter damage which could indicate tumour invasion, but more research in this area is needed.

CONCLUSION

Advanced MRI techniques show potential for early treatment response assessment, but each technique alone lacks specificity. Multiparametric imaging may be necessary to aid biological interpretation and enable treatment guidance.

Enhancing biomedical imaging: the role of nanoparticle-based contrast agents

Biomedical imaging plays a critical role in early detection, precise diagnosis, treatment planning, and monitoring responses, but traditional methods encounter challenges such as limited sensitivity, specificity, and inability to monitor therapeutic responses due to factors like short circulation half-life and potential toxicity. Nanoparticles are revolutionizing biomedical imaging as contrast agents across modalities like computed tomography (CT), optical, magnetic resonance imaging (MRI), and ultrasound, exploiting unique attributes such as those of metal-based, polymeric, and lipid nanoparticles. They shield imaging agents from immune clearance, extending circulation time, and enhancing bioavailability at tumor sites. This results in improved imaging sensitivity. The study highlights advancements in multifunctional nanoparticles for targeted imaging, tackling concerns regarding toxicity and biocompatibility. Critically evaluating conventional contrast agents, emphasizes the shortcomings that nanoparticles aim to overcome. This review provides insight into the current status of nanoparticle-based contrast agents, illuminating their potential to reshape therapeutic monitoring and precision diagnostics.

Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac

Background and Purpose

The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner.

Materials and Methods

Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues.

Results

Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T.

Conclusion

Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.

Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac

BACKGROUND

Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac.

METHODS

Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers.

RESULTS

Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations.

CONCLUSIONS

High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.

Evolving concepts in margin strategies and adaptive radiotherapy for glioblastoma: A new future is on the horizon

Chemoradiotherapy is the standard treatment after maximal safe resection for glioblastoma (GBM). Despite advances in molecular profiling, surgical techniques, and neuro-imaging, there have been no major breakthroughs in radiotherapy (RT) volumes in decades. Although the majority of recurrences occur within the original gross tumor volume (GTV), treatment of a clinical target volume (CTV) ranging from 1.5 to 3.0 cm beyond the GTV remains the standard of care. Over the past 15 years, the incorporation of standard and functional MRI sequences into the treatment workflow has become a routine practice with increasing adoption of MR simulators, and new integrated MR-Linac technologies allowing for daily pre-, intra- and post-treatment MR imaging. There is now unprecedented ability to understand the tumor dynamics and biology of GBM during RT, and safe CTV margin reduction is being investigated with the goal of improving the therapeutic ratio. The purpose of this review is to discuss margin strategies and the potential for adaptive RT for GBM, with a focus on the challenges and opportunities associated with both online and offline adaptive workflows. Lastly, opportunities to biologically guide adaptive RT using non-invasive imaging biomarkers and the potential to define appropriate volumes for dose modification will be discussed.