Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Diffusion-Weighted MRI for Recurrent/Persistent Head and Neck Squamous-Cell Carcinoma After Radiotherapy: Systematic Review and Meta-Analysis

OBJECTIVE

To evaluate the accuracy of diffusion-weighted magnetic resonance imaging (DWI-MRI) in diagnosing persistent/recurrent head and neck squamous cell carcinomas (HNSCCs) after primary chemoradiotherapy (CRT).

DATA SOURCES

Scopus, PubMed/MEDLINE, and Cochrane Library databases were searched for relevant publications until April 18, 2023.

REVIEW METHODS

A systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses of Diagnostic Test Accuracy statement. The search was conducted independently by 2 investigators. Methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Studies-2 questionnaire. Extracted data were used to calculate the pooled DWI-MRI sensitivity, specificity, diagnostic odds ratio, and positive and negative likelihood ratio.

RESULTS

A total of 618 patients from 10 studies were included for calculation of diagnostic accuracy parameters. At the level of the primary tumor, the pooled sensitivity and specificity were, respectively, 0.96 (95% confidence interval [CI]: 0.89-1.00) and 0.81 (95% CI: 0.54-0.98) in the case of qualitative analysis, and, respectively, 0.79 (95% CI: 0.66-0.89) and 0.88 (95% CI: 0.77-0.96) for quantitative analysis. At the level of the neck, the pooled sensitivity and specificity were, respectively, 0.87 (95% CI: 0.75-0.95) and 0.84 (95% CI: 0.74-0.93) when images were analyzed qualitatively, and 0.79 (95% CI: 0.60-0.94) and 0.90 (95% CI: 0.82-0.97) when analyzed quantitatively.

CONCLUSION

DWI-MRI showed high diagnostic accuracy and should be considered if persistent/recurrent HNSCCs is suspected after primary CRT. No significant differences were found between qualitative and quantitative imaging assessment.

The Use of Apparent Diffusion Coefficient Values for Differentiating Bevacizumab-Related Cytotoxicity from Tumor Recurrence and Radiation Necrosis in Glioblastoma

OBJECTIVES

Glioblastomas (GBM) are the most common primary invasive neoplasms of the brain. Distinguishing between lesion recurrence and different types of treatment related changes in patients with GBM remains challenging using conventional MRI imaging techniques. Therefore, accurate and precise differentiation between true progression or pseudoresponse is crucial in deciding on the appropriate course of treatment. This retrospective study investigated the potential of apparent diffusion coefficient (ADC) map values derived from diffusion-weighted imaging (DWI) as a noninvasive method to increase diagnostic accuracy in treatment response.

METHODS

A cohort of 21 glioblastoma patients (mean age: 59.2 ± 11.8, 12 Male, 9 Female) that underwent treatment with bevacizumab were selected. The ADC values were calculated from the DWI images obtained from a standardized brain protocol across 1.5-T and 3-T MRI scanners. Ratios were calculated for rADC values. Lesions were classified as bevacizumab-induced cytotoxicity based on characteristic imaging features (well-defined regions of restricted diffusion with persistent diffusion restriction over the course of weeks without tissue volume loss and absence of contrast enhancement). The rADC value was compared to these values in radiation necrosis and recurrent lesions, which were concluded in our prior study. The nonparametric Wilcoxon signed rank test with p < 0.05 was used for significance.

RESULTS

The mean ± SD age of the selected patients was 59.2 ± 11.8. ADC values and corresponding mean rADC values for bevacizumab-induced cytotoxicity were 248.1 ± 67.2 and 0.39 ± 0.10, respectively. These results were compared to the ADC values and corresponding mean rADC values of tumor progression and radiation necrosis. Significant differences between rADC values were observed in all three groups (p < 0.001). Bevacizumab-induced cytotoxicity had statistically significant lower ADC values compared to both tumor recurrence and radiation necrosis.

CONCLUSION

The study demonstrates the potential of ADC values as noninvasive imaging biomarkers for differentiating recurrent glioblastoma from radiation necrosis and bevacizumab-induced cytotoxicity.

Understanding ADC variation by fat content effect using a dual-function MRI phantom

BACKGROUND

A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs.

METHODS

A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the "iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ," IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC.

RESULTS

The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R2 = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs.

CONCLUSIONS

A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC.

RELEVANCE STATEMENT

The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements.

KEY POINTS

• A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values are determined by GBD and FF. • The dual-function phantom showed the mutual ADC addition between FF and GBD.

Predictive value of diffusion MRI-based parametric response mapping for prognosis and treatment response in glioblastoma

BACKGROUND

Early detection of treatment response is important for the management of patients with malignant brain tumors such as glioblastoma to assure good quality of life in relation to therapeutic efficacy.

AIM

To investigate whether parametric response mapping (PRM) with diffusion MRI may provide prognostic information at an early stage of standard therapy for glioblastoma.

MATERIALS AND METHODS

This prospective study included 31 patients newly diagnosed with glioblastoma WHO grade IV, planned for primary standard postoperative treatment with radiotherapy 60Gy/30 fractions with concomitant and adjuvant Temozolomide. MRI follow-up including diffusion and perfusion weighting was performed at 3 T at start of postoperative chemoradiotherapy, three weeks into treatment, and then regularly until twelve months postoperatively. Regional mean diffusivity (MD) changes were analyzed voxel-wise using the PRM method (MD-PRM). At eight and twelve months postoperatively, after completion of standard treatment, patients were classified using conventional MRI and clinical evaluation as either having stable disease (SD, including partial response) or progressive disease (PD). It was assessed whether MD-PRM differed between patients having SD versus PD and whether it predicted the risk of disease progression (progression-free survival, PFS) or death (overall survival, OS). A subgroup analysis was performed that compared MD-PRM between SD and PD in patients only undergoing diagnostic biopsy. MGMT-promotor methylation status (O6-methylguanine-DNA methyltransferase) was registered and analyzed with respect to PFS, OS and MD-PRM.

RESULTS

Of the 31 patients analyzed: 21 were operated by resection and ten by diagnostic biopsy. At eight months, 19 patients had SD and twelve had PD. At twelve months, ten patients had SD and 20 had PD, out of which ten were deceased within twelve months and one was deceased without known tumor progression. Median PFS was nine months, and median OS was 17 months. Eleven patients had methylated MGMT-promotor, 16 were MGMT unmethylated, and four had unknown MGMT-status. MD-PRM did not significantly predict patients having SD versus PD neither at eight nor at twelve months. Patients with an above median MD-PRM reduction had a slightly longer PFS (P = 0.015) in Kaplan-Maier analysis, as well as a non-significantly longer OS (P = 0.099). In the subgroup of patients only undergoing biopsy, total MD-PRM change at three weeks was generally higher for patients with SD than for patients with PD at eight months, although no tests were performed. MGMT status strongly predicted both PFS and OS but not MD-PRM change.

CONCLUSION

MD-PRM at three weeks was not demonstrated to be predictive of treatment response, disease progression, or survival. Preliminary results suggested a higher predictive value in non-resected patients, although this needs to be evaluated in future studies.

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

BACKGROUND AND PURPOSE

Survival in glioblastoma might be extended by escalating the radiotherapy dose to treatment-resistant tumour and adapting to tumour changes. Diffusion-weighted imaging (DWI) on MRI-linear accelerators (MR-Linacs) could be used to identify a dose escalation target, but its prognostic value must be demonstrated. The purpose of this study was to determine whether MR-Linac DWI can assess treatment response in glioblastoma and whether changes in DWI show greater prognostic value than changes in the contrast-enhancing gross tumour volume (GTV).

MATERIALS AND METHODS

Seventy-five patients with glioblastoma were treated with chemoradiotherapy, of which 32 were treated on a 1.5 T MRI-linear accelerator (MR-Linac). Patients were imaged with simulation MRI scanners (MR-sim) at treatment planning and weeks 2, 4, and 10 after treatment start. Twenty-eight patients had additional MR-Linac DWI sequences. Cox modelling was used to evaluate the correlation of overall and progression-free survival (OS and PFS) with clinical variables and volumetric changes in the GTV and low-ADC regions (ADC < 1.25 µm2/ms within GTV).

RESULTS

In total, 479 MR-Linac DWI and 289 MR-sim DWI datasets were analyzed. MR-Linac low-ADC changes between weeks 2 and 5 inclusive were prognostic for OS (hazard ratio lower limits ≥ 1.2, p-values ≤ 0.02). MR-sim low-ADC changes showed greater correlation with OS and PFS than GTV changes (e.g., OS hazard ratio at week 2 was 3.4 (p <0.001) for low-ADC versus 2.0 (p = 0.022) for GTV).

CONCLUSION

MR-Linac DWI can measure low-ADC tumour volumes that correlate with OS and PFS better than contrast-enhancing GTV. Low-ADC regions could serve as dose escalation targets.

Molecular Imaging with Aquaporin-Based Reporter Genes: Quantitative Considerations from Monte Carlo Diffusion Simulations

Aquaporins provide a unique approach for imaging genetic activity in deep tissues by increasing the rate of cellular water diffusion, which generates a magnetic resonance contrast. However, distinguishing aquaporin signals from the tissue background is challenging because water diffusion is influenced by structural factors, such as cell size and packing density. Here, we developed a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on subtracting signals at two diffusion times can improve specificity by unambiguously isolating aquaporin signals from the tissue background. We further used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin and established a mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. The quantitative framework developed in this study will enable a broad range of applications in biomedical synthetic biology, requiring the use of aquaporins to noninvasively monitor the location and function of genetically engineered devices in live animals.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.

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.

Early MRI Predictors of Relapse in Primary Central Nervous System Lymphoma Treated with MATRix Immunochemotherapy

Primary Central Nervous System Lymphoma (PCNSL) is a highly malignant brain tumour. We investigated dynamic changes in tumour volume and apparent diffusion coefficient (ADC) measurements for predicting outcome following treatment with MATRix chemotherapy in PCNSL. Patients treated with MATRix (n = 38) underwent T1 contrast-enhanced (T1CE) and diffusion-weighted imaging (DWI) before treatment, after two cycles and after four cycles of chemotherapy. Response was assessed using the International PCNSL Collaborative Group (IPCG) imaging criteria. ADC histogram parameters and T1CE tumour volumes were compared among response groups, using one-way ANOVA testing. Logistic regression was performed to examine those imaging parameters predictive of response. Response after two cycles of chemotherapy differed from response after four cycles; of the six patients with progressive disease (PD) after four cycles of treatment, two (33%) had demonstrated a partial response (PR) or complete response (CR) after two cycles. ADC_mean at baseline, T1CE at baseline and T1CE percentage volume change differed between response groups (0.005 < p < 0.038) and were predictive of MATRix treatment response (area under the curve: 0.672-0.854). Baseline ADC and T1CE metrics are potential biomarkers for risk stratification of PCNSL patients early during remission induction therapy with MATRix. Standard interim response assessment (after two cycles) according to IPCG imaging criteria does not reliably predict early disease progression in the context of a conventional treatment approach.

Molecular imaging with aquaporin-based reporter genes: quantitative considerations from Monte Carlo diffusion simulations

Aquaporins provide a new class of genetic tools for imaging molecular activity in deep tissues by increasing the rate of cellular water diffusion, which generates magnetic resonance contrast. However, distinguishing aquaporin contrast from the tissue background is challenging because water diffusion is also influenced by structural factors such as cell size and packing density. Here, we developed and experimentally validated a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on time-dependent changes in diffusivity can improve specificity by unambiguously isolating aquaporin-driven contrast from the tissue background. Finally, we used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin, and established a simple mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. This study creates a framework for broad applications of aquaporins, particularly in biomedicine and in vivo synthetic biology, where quantitative methods to measure the location and performance of genetic devices in whole vertebrates are necessary.

Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke

Predicting clinical outcomes after stroke, using magnetic resonance imaging (MRI) measures, remains a challenge. The purpose of this study was to investigate the prediction of long-term clinical outcomes after ischemic stroke using parametric response mapping (PRM) based on perfusion MRI data. Multiparametric perfusion MRI datasets from 30 patients with chronic ischemic stroke were acquired at four-time points ranging from V2 (6  weeks) to V5 (7  months) after stroke onset. All perfusion MR parameters were analyzed using the classic whole-lesion approach and voxel-based PRM at each time point. The imaging biomarkers from each acquired MRI metric that was predictive of both neurological and functional outcomes were prospectively investigated. For predicting clinical outcomes at V5, it was identified that PRMTmax-, PRMrCBV-, and PRMrCBV+ at V3 were superior to the mean values of the corresponding maps at V3. We identified correlations between the clinical prognosis after stroke and MRI parameters, emphasizing the superiority of the PRM over the whole-lesion approach for predicting long-term clinical outcomes. This indicates that complementary information for the predictive assessment of clinical outcomes can be obtained using PRM analysis. Moreover, new insights into the heterogeneity of stroke lesions revealed by PRM can help optimize the accurate stratification of patients with stroke and guide rehabilitation.

Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging

Biomedical imaging, especially molecular imaging, has been a driving force in scientific discovery, technological innovation, and precision medicine in the past two decades. While substantial advances and discoveries in chemical biology have been made to develop molecular imaging probes and tracers, translating these exogenous agents to clinical application in precision medicine is a major challenge. Among the clinically accepted imaging modalities, magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) exemplify the most effective and robust biomedical imaging tools. Both MRI and MRS enable a broad range of chemical, biological and clinical applications from determining molecular structures in biochemical analysis to imaging diagnosis and characterization of many diseases and image-guided interventions. Using chemical, biological, and nuclear magnetic resonance properties of specific endogenous metabolites and native MRI contrast-enhancing biomolecules, label-free molecular and cellular imaging with MRI can be achieved in biomedical research and clinical management of patients with various diseases. This review article outlines the chemical and biological bases of several label-free chemically and molecularly selective MRI and MRS methods that have been applied in imaging biomarker discovery, preclinical investigation, and image-guided clinical management. Examples are provided to demonstrate strategies for using endogenous probes to report the molecular, metabolic, physiological, and functional events and processes in living systems, including patients. Future perspectives on label-free molecular MRI and its challenges as well as potential solutions, including the use of rational design and engineered approaches to develop chemical and biological imaging probes to facilitate or combine with label-free molecular MRI, are discussed.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 10⁶ C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

Time-dependent diffusivity and kurtosis in phantoms and patients with head and neck cancer

PURPOSE

To assess the reliability of measuring diffusivity, diffusional kurtosis, and cellular-interstitial water exchange time with long diffusion times (100-800 ms) using stimulated-echo DWI.

METHODS

Time-dependent diffusion MRI was tested on two well-established diffusion phantoms and in 5 patients with head and neck cancer. Measurements were conducted using an in-house diffusion-weighted STEAM-EPI pulse sequence with multiple diffusion times at a fixed TE on three scanners. We used the weighted linear least-squares fit method to estimate time-dependent diffusivity,D ( t ) $$ D(t) $$ , and diffusional kurtosis,K ( t ) $$ K(t) $$ . Additionally, the Kärger model was used to estimate cellular-interstitial water exchange time (τ ex $$ {\tau}_{ex} $$ ) fromK ( t ) $$ K(t) $$ .

RESULTS

Diffusivity measured by time-dependent STEAM-EPI measurements and commercial SE-EPI showed comparable results with R2 above 0.98 and overall 5.4 ± 3.0% deviation across diffusion times. Diffusional kurtosis phantom data showed expected patterns: constantD $$ D $$ andK $$ K $$  = 0 for negative controls and slow varyingD $$ D $$ andK $$ K $$ for samples made of nanoscopic vesicles. Time-dependent diffusion MRI in patients with head and neck cancer found that the Kärger model could be considered valid in 72% ± 23% of the voxels in the metastatic lymph nodes. The median cellular-interstitial water exchange time estimated for lesions was between 58.5 ms and 70.6 ms.

CONCLUSIONS

Based on two well-established diffusion phantoms, we found that time-dependent diffusion MRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times and across multiple MRI systems. Moreover, estimation of cellular-interstitial water exchange time can be achieved using the Kärger model for the metastatic lymph nodes in patients with head and neck cancer.

Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases

In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the in vivo evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.

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.

Metabolic activity diffusion imaging (MADI): I. Metabolic, cytometric modeling and simulations

Evidence mounts that the steady-state cellular water efflux (unidirectional) first-order rate constant (k_io [s^-1 ]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na⁺ , K⁺ -ATPase (NKA), ^c MR_NKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal ¹ H₂ O MR k_io determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic k_io biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA-free) method to determine k_io in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well-known diffusion-weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water-filled and -immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density (ρ [cells/μL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI). We simulate water molecule displacements and transverse MR signal decays covering the entirety of b-space from pure water (ρ = V = 0; k_io undefined; diffusion coefficient, D₀ ) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (D_n ) is diffusion time-dependent, a manifestation of the "diffusion dispersion". When the "well-mixed" (steady-state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering (ρ, V), and (2) permeating (k_io ) cytoplasmic membranes, and less so by D_n magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.

Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange

Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.

High grade glioma radiation therapy on a high field 1.5 Tesla MR-Linac - workflow and initial experience with daily adapt-to-position (ATP) MR guidance: A first report

Purpose

This study reports the workflow and initial clinical experience of high grade glioma (HGG) radiotherapy on the 1.5 T MR-Linac (MRL), with a focus on the temporal variations of the tumor and feasibility of multi-parametric image (mpMRI) acquisition during routine treatment workflow.

Materials and methods

Ten HGG patients treated with radiation within the first year of the MRL's clinical operation, between October 2019 and August 2020, were identified from a prospective database. Workflow timings were recorded and online adaptive plans were generated using the Adapt-To-Position (ATP) workflow. Temporal variation within the FLAIR hyperintense region (FHR) was assessed by the relative FHR volumes (n = 281 contours) and migration distances (maximum linear displacement of the volume). Research mpMRIs were acquired on the MRL during radiation and changes in selected functional parameters were investigated within the FHR.

Results

All patients completed radiotherapy to a median dose of 60 Gy (range, 54-60 Gy) in 30 fractions (range, 30-33), receiving a total of 287 fractions on the MRL. The mean in-room time per fraction with or without post-beam research imaging was 42.9 minutes (range, 25.0-69.0 minutes) and 37.3 minutes (range, 24.0-51.0 minutes), respectively. Three patients (30%) required re-planning between fractions 9 to 12 due to progression of tumor and/or edema identified on daily MRL imaging. At the 10, 20, and 30-day post-first fraction time points 3, 3, and 4 patients, respectively, had a FHR volume that changed by at least 20% relative to the first fraction. Research mpMRIs were successfully acquired on the MRL. The median apparent diffusion coefficient (ADC) within the FHR and the volumes of FLAIR were significantly correlated when data from all patients and time points were pooled (R=0.68, p<.001).

Conclusion

We report the first clinical series of HGG patients treated with radiotherapy on the MRL. The ATP workflow and treatment times were clinically acceptable, and daily online MRL imaging triggered adaptive re-planning for selected patients. Acquisition of mpMRIs was feasible on the MRL during routine treatment workflow. Prospective clinical outcomes data is anticipated from the ongoing UNITED phase 2 trial to further refine the role of MR-guided adaptive radiotherapy.

Voxel-Based Analysis of the Relation of 3'-Deoxy-3'-[18F]fluorothymidine ([18F]FLT) PET and Diffusion-Weighted (DW) MR Signals in Subcutaneous Tumor Xenografts Does Not Reveal a Direct Spatial Relation of These Two Parameters

PURPOSE

Multimodal molecular imaging allows a direct coregistration of different images, facilitating analysis of the spatial relation of various imaging parameters. Here, we further explored the relation of proliferation, as measured by [18F]FLT PET, and water diffusion, as an indicator of cellular density and cell death, as measured by diffusion-weighted (DW) MRI, in preclinical tumor models. We expected these parameters to be negatively related, as highly proliferative tissue should have a higher density of cells, hampering free water diffusion.

PROCEDURES

Nude mice subcutaneously inoculated with either lung cancer cells (n = 11 A549 tumors, n = 20 H1975 tumors) or colorectal cancer cells (n = 13 Colo205 tumors) were imaged with [18F]FLT PET and DW-MRI using a multimodal bed, which was transferred from one instrument to the other within the same imaging session. Fiducial markers allowed coregistration of the images. An automatic post-processing was developed in MATLAB handling the spatial registration of DW-MRI (measured as apparent diffusion coefficient, ADC) and [18F]FLT image data and subsequent voxel-wise analysis of regions of interest (ROIs) in the tumor.

RESULTS

Analyses were conducted on a total of 76 datasets, comprising a median of 2890 data points (ranging from 81 to 13,597). Scatterplots showing [18F]FLT vs. ADC values displayed various grades of relations (Pearson correlation coefficient (PCC) varied from - 0.58 to 0.49, median: -0.07). When relating PCC to tumor volume (median: 46 mm3, range: 3 mm3 to 584 mm3), lung tumors tended to have a more pronounced negative spatial relation of [18F]FLT and ADC with increasing tumor size. However, due to the low number of large tumors (> ~ 200 mm3), this conclusion has to be treated with caution.

CONCLUSIONS

A spatial relation of water diffusion, as measured by DW-MRI, and cellular proliferation, as measured by [18F]FLT PET, cannot be detected in the experimental datasets investigated in this study.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Portable, handheld, and affordable blood perfusion imager for screening of subsurface cancer in resource-limited settings

Existing procedures of screening subsurface cancers are either prohibitively resource-intensive and expensive or are unable to provide direct quantitative estimates of the relevant physiological parameters for accurate classification accommodating interpatient variabilities and overlapping clinical manifestations. Here, we introduce a handheld and inexpensive blood perfusion imager that provides a noninvasive in situ screening approach for distinguishing precancer, cancer, and normal scenarios by precise quantitative estimation of the localized blood circulation in the tissue over an unrestricted region of interest without any unwarranted noise in the data, augmented by machine learning–based classification. Clinical trials in minimally resourced settings have established the efficacy of the method in differentiating cancerous and precancerous stages of suspected oral abnormalities, as verified by gold-standard biopsy reports.

Precise information on localized variations in blood circulation holds the key for noninvasive diagnostics and therapeutic assessment of various forms of cancer. While thermal imaging by itself may provide significant insights on the combined implications of the relevant physiological parameters, viz. local blood perfusion and metabolic balance due to active tumors as well as the ambient conditions, knowledge of the tissue surface temperature alone may be somewhat inadequate in distinguishing between some ambiguous manifestations of precancer and cancerous lesions, resulting in compromise of the selectivity in detection. This, along with the lack of availability of a user-friendly and inexpensive portable device for thermal-image acquisition, blood perfusion mapping, and data integration acts as a deterrent against the emergence of an inexpensive, contact-free, and accurate in situ screening and diagnostic approach for cancer detection and management. Circumventing these constraints, here we report a portable noninvasive blood perfusion imager augmented with machine learning–based quantitative analytics for screening precancerous and cancerous traits in oral lesions, by probing the localized alterations in microcirculation. With a proven overall sensitivity >96.66% and specificity of 100% as compared to gold-standard biopsy-based tests, the method successfully classified oral cancer and precancer in a resource-limited clinical setting in a double-blinded patient trial and exhibited favorable predictive capabilities considering other complementary modes of medical image analysis as well. The method holds further potential to achieve contrast-free, accurate, and low-cost diagnosis of abnormal microvascular physiology and other clinically vulnerable conditions, when interpreted along with complementary clinically evidenced decision-making perspectives.

Comparison of contrast-enhanced T1-weighted imaging using DANTE-SPACE, PETRA, and MPRAGE: a clinical evaluation of brain tumors at 3 Tesla

Background

We aimed to compare the performance of three contrast-enhanced T1-weighted three-dimensional (3D) magnetic resonance (MR) sequences to detect brain tumors at 3 Tesla. The three sequences were: (I) delay alternating with nutation for tailored excitation sampling perfection with application-optimized contrasts using different flip angle evolution (DANTE-SPACE), (II) pointwise encoding time reduction with radial acquisition (PETRA), and (III) magnetization-prepared rapid acquisition with gradient echo (MPRAGE).

Methods

This study involved 77 consecutive patients, including 34 patients with known primary brain tumors and 43 patients suspected of intracranial metastases. All patients underwent each of the three sequences with comparable spatial resolution and acquisition time post-injection. Signal-to-noise ratios (SNRs) for gray matter (GM) and white matter (WM), contrast-to-noise ratios (CNRs) for lesion/GM, lesion/WM, and GM/WM were quantitatively compared. Two radiologists determined the total number of enhancing lesions by consensus. Intraclass correlation coefficients (ICCs) between the two radiologists for metastases presence, qualitative ratings for image quality, and acoustic noise level of each sequence were assessed.

Results

Among the three sequences, SNRs and CNRs between lesions and surrounding parenchyma were highest using DANTE-SPACE, but CNR_WM/GM was the lowest with DANTE-SPACE. SNRs for PETRA images were significantly higher than those for MPRAGE (P<0.001). CNRs between lesions and surrounding parenchyma were similar for PETRA and MPRAGE (P>0.05). Significantly more brain metastases were detected with DANTE-SPACE (n=94) compared with MPRAGE (n=71) and PETRA (n=72). The ICCs were 0.964 for MPRAGE, 0.975 for PETRA, and 0.973 for DANTE-SPACE. Qualitative scores for lesion imaging using DANTE-SPACE were significantly higher than those obtained with PETRA and MPRAGE (P=0.002 and P=0.004, respectively). The acoustic noise level for PETRA (64.45 dB) was significantly lower than that for MPRAGE (78.27 dB, P<0.01) and DANTE-SPACE (80.18 dB, P<0.01).

Conclusions

PETRA achieves comparable detection of brain tumors with MPRAGE and is preferred for depicting osseous metastases and meningeal enhancement. DANTE-SPACE with blood vessel suppression showed improved detection of cerebral metastases compared with MPRAGE and PETRA, which could be helpful for the differential diagnosis of tumors.

Interest and Limits of [18F]ML-10 PET Imaging for Early Detection of Response to Conventional Chemotherapy

One of the current challenges in oncology is to develop imaging tools to early detect the response to conventional chemotherapy and adjust treatment strategies when necessary. Several studies evaluating PET imaging with 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) as a predictive tool of therapeutic response highlighted its insufficient specificity and sensitivity. The [¹⁸F]FDG uptake reflects only tumor metabolic activity and not treatment-induced cell death, which seems to be relevant for therapeutic evaluation. Therefore, to evaluate this parameter in vivo, several cell death radiotracers have been developed in the last years. However, few of them have reached the clinical trials. This systematic review focuses on the use of [¹⁸F]ML-10 (2-(5-[¹⁸F]fluoropentyl)-2-methylmalonic acid) as radiotracer of apoptosis and especially as a measure of tumor response to treatment. A comprehensive literature review concerning the preclinical and clinical investigations conducted with [¹⁸F]ML-10 was performed. The abilities and applications of this radiotracer as well as its clinical relevance and limitations were discussed. Most studies highlighted a good ability of the radiotracer to target apoptotic cells. However, the increase in apoptosis during treatment did not correlate with the radiotracer tumoral uptake, even using more advanced image analysis (voxel-based analysis). [¹⁸F]ML-10 PET imaging does not meet current clinical expectations for early detection of the therapeutic response to conventional chemotherapy. This review has pointed out the challenges of applying various apoptosis imaging strategies in clinical trials, the current methodologies available for image analysis and the future of molecular imaging to assess this therapeutic response.

Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma

RATIONALE AND OBJECTIVES

Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression.

MATERIALS AND METHODS

We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRM_rFLAIR+), decreased (PRM_rFLAIR-), or unchanged (PRM_rFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRM_rFLAIR between weeks 10 and 20 and continuously until the PRM_rFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection.

RESULTS

Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRM_rFLAIR+ (PFS: p <0.01; OS: p <0.001). PRM_rFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRM_rFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001).

CONCLUSION

PRM_rFLAIR may provide an early biomarker of disease progression in glioblastoma.

Apparent Diffusion Coefficient Can Predict Response to Chemotherapy of Liver Metastases in Colorectal Cancer

RATIONALE AND OBJECTIVES

The aim of this meta-analysis was to evaluate the suitability of apparent diffusion coefficient (ADC) as a predictor of response to systemic chemotherapy in patients with metastatic colorectal carcinoma (CRC).

MATERIALS AND METHODS

MEDLINE library, SCOPUS database, and EMBASE database were screened for relationships between pretreatment ADC values of hepatic CRC metastases and response to systemic chemotherapy. Overall, five eligible studies were identified. The following data were extracted: authors, year of publication, study design, number of patients, mean value ADC and standard-deviation, measure method, b-values, and Tesla-strength. The methodological quality of every study was checked according to the Quality Assessment of Diagnostic Studies-2 instrument. The meta-analysis was undertaken by employing RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used to account for heterogeneity. Mean ADC values including 95% confidence intervals were calculated.

RESULTS

Five studies (n = 114 patients) were included. The pretreatment mean ADC in the responder group was 1.15 × 10^-3 mm²/s (1.03, 1.28) and 1.37 × 10^-3 mm²/s (1.3, 1.44) in the nonresponder group. An ADC baseline threshold of 1.2 × 10^-3 mm²/s, below which no nonresponder was found, can distinguish both groups.

CONCLUSION

The results indicate ADC can serve as a predictor of response to chemotherapy for CRC patients.

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth

Background Aortic diameter measurements in patients with a thoracic aortic aneurysm (TAA) show wide variation. There is no technique to quantify aortic growth in a three-dimensional (3D) manner. Purpose To validate a CT-based technique for quantification of 3D growth based on deformable registration in patients with TAA. Materials and Methods Patients with ascending and descending TAA with two or more CT angiography studies between 2006 and 2020 were retrospectively identified. The 3D aortic growth was quantified using vascular deformation mapping (VDM), a technique that uses deformable registration to warp a mesh constructed from baseline aortic anatomy. Growth assessments between VDM and clinical CT diameter measurements were compared. Aortic growth was quantified as the ratio of change in surface area at each mesh element (area ratio). Manual segmentations were performed by independent raters to assess interrater reproducibility. Registration error was assessed using manually placed landmarks. Agreement between VDM and clinical diameter measurements was assessed using Pearson correlation and Cohen κ coefficients. Results A total of 38 patients (68 surveillance intervals) were evaluated (mean age, 69 years ± 9 [standard deviation]; 21 women), with TAA involving the ascending aorta (n = 26), descending aorta (n = 10), or both (n = 2). VDM was technically successful in 35 of 38 (92%) patients and 58 of 68 intervals (85%). Median registration error was 0.77 mm (interquartile range, 0.54-1.10 mm). Interrater agreement was high for aortic segmentation (Dice similarity coefficient = 0.97 ± 0.02) and VDM-derived area ratio (bias = 0.0, limits of agreement: -0.03 to 0.03). There was strong agreement (r = 0.85, P < .001) between peak area ratio values and diameter change. VDM detected growth in 14 of 58 (24%) intervals. VDM revealed growth outside the maximally dilated segment in six of 14 (36%) growth intervals, none of which were detected with diameter measurements. Conclusion Vascular deformation mapping provided reliable and comprehensive quantitative assessment of three-dimensional aortic growth and growth patterns in patients with thoracic aortic aneurysms undergoing CT surveillance. Published under a CC BY 4.0 license Online supplemental material is available for this article. See also the editorial by Wieben in this issue.

Accuracy and precision of apparent diffusion coefficient measurements on a 1.5 T MR-Linac in central nervous system tumour patients

BACKGROUND AND PURPOSE

MRI linear accelerators (MR-Linacs) may allow treatment adaptation to be guided by quantitative MRI including diffusion-weighted imaging (DWI). The aim of this study was to evaluate the accuracy and precision of apparent diffusion coefficient (ADC) measurements from DWI on a 1.5 T MR-Linac in patients with central nervous system (CNS) tumours through comparison with a diagnostic scanner.

MATERIALS AND METHODS

CNS patients were treated using a 1.5 T Elekta Unity MR-Linac. DWI was acquired during MR-Linac treatment and on a Philips Ingenia 1.5 T. The agreement between the two scanners on median ADC over the gross tumour/clinical target volumes (GTV/CTV) and in brain regions (white/grey matter, cerebrospinal fluid (CSF)) was computed. Repeated scans were used to estimate ADC repeatability. Daily changes in ADC over the GTV of high-grade gliomas were characterized from MR-Linac scans.

RESULTS

DWI from 59 patients was analyzed. MR-Linac ADC measurements showed a small bias relative to Ingenia measurements in white matter, grey matter, GTV, and CTV (bias: -0.05 ± 0.03, -0.08 ± 0.05, -0.1 ± 0.1, -0.08 ± 0.07 μm²/ms). ADC differed substantially in CSF (bias: -0.5 ± 0.3 μm²/ms). The repeatability of MR-Linac ADC over white/grey matter was similar to previous reports (coefficients of variation for median ADC: 1.4%/1.8%). MR-Linac ADC changes in the GTV were detectable.

CONCLUSIONS

It is possible to obtain ADC measurements in the brain on a 1.5 T MR-Linac that are comparable to those of diagnostic-quality scanners. This technical validation study adds to the foundation for future studies that will correlate brain tumour ADC with clinical outcomes.

Branching development of early post-implantation human embryonic-like tissues in 3D stem cell culture

Human embryonic stem cells (hESCs) have the intrinsic capacity to self-organize and generate patterned tissues. In vitro models that coax hESCs to form embryonic-like structures by modulating physical environments and priming with chemical signals have become a powerful tool for dissecting the regulatory mechanisms underlying early human development. Here we present a 3D suspension culture system of hESCs that can generate post-implantation, pre-gastrulation embryonic-like tissues in an efficient and controllable manner. The efficiency of the development of asymmetric tissues, which mimic the post-implantation, pre-gastrulation amniotic sac, was about 50% in the 3D suspension culture. Quantitative imaging profiling and unsupervised trajectory analysis revealed that hESC aggregates first entered into a transitional stage expressing Brachyury (or T), before their development branched into different paths to develop into asymmetric embryonic-like tissues, amniotic-like tissues, and mesodermal-like tissues, respectively. Moreover, the branching developmental trajectory of embryonic-like structures was affected by the initial cell seeding density or cluster size of hESCs. A higher percentage of amniotic-like tissues was observed under a small initial cell seeding density of hESCs. Conversely, a large initial cell seeding density of hESCs promoted the development of mesodermal-like tissues. Intermediate cell seeding densities of hESCs in the 3D suspension culture promoted the development of asymmetric embryonic-like tissues. Our results suggest that hESCs have the intrinsic capability to sense the initial cell population size, which in turn regulates their differentiation and self-organization into different embryonic-like tissues. Our 3D suspension culture thus provides a promising experimental tool to study the interplay between tissue topology and self-organization and progressive embryonic development using in vitro hESC-based models.

Imaging Surveillance of Gliomas: Role of Basic and Advanced Imaging Techniques

It is essential to be aware of widely accepted criteria for grading of treatment response in both high-grade and low-grade gliomas. These criteria primarily take into account responses of measurable and nonmeasurable lesions on T2-weighted, fluid-attenuated inversion recovery, and postcontrast images to determine a final category of response for the patient. The additional role that other advanced imaging techniques, such as diffusion and perfusion imaging, can play in the surveillance of these tumors is discussed in this article.

MR-Guided Radiotherapy for Brain and Spine Tumors

MRI is the standard modality to assess anatomy and response to treatment in brain and spine tumors given its superb anatomic soft tissue contrast (e.g., T1 and T2) and numerous additional intrinsic contrast mechanisms that can be used to investigate physiology (e.g., diffusion, perfusion, spectroscopy). As such, hybrid MRI and radiotherapy (RT) devices hold unique promise for Magnetic Resonance guided Radiation Therapy (MRgRT). In the brain, MRgRT provides daily visualizations of evolving tumors that are not seen with cone beam CT guidance and cannot be fully characterized with occasional standalone MRI scans. Significant evolving anatomic changes during radiotherapy can be observed in patients with glioblastoma during the 6-week fractionated MRIgRT course. In this review, a case of rapidly changing symptomatic tumor is demonstrated for possible therapy adaptation. For stereotactic body RT of the spine, MRgRT acquires clear isotropic images of tumor in relation to spinal cord, cerebral spinal fluid, and nearby moving organs at risk such as bowel. This visualization allows for setup reassurance and the possibility of adaptive radiotherapy based on anatomy in difficult cases. A review of the literature for MR relaxometry, diffusion, perfusion, and spectroscopy during RT is also presented. These techniques are known to correlate with physiologic changes in the tumor such as cellularity, necrosis, and metabolism, and serve as early biomarkers of chemotherapy and RT response correlating with patient survival. While physiologic tumor investigations during RT have been limited by the feasibility and cost of obtaining frequent standalone MRIs, MRIgRT systems have enabled daily and widespread physiologic measurements. We demonstrate an example case of a poorly responding tumor on the 0.35 T MRIgRT system with relaxometry and diffusion measured several times per week. Future studies must elucidate which changes in MR-based physiologic metrics and at which timepoints best predict patient outcomes. This will lead to early treatment intensification for tumors identified to have the worst physiologic responses during RT in efforts to improve glioblastoma survival.

Advanced Imaging Techniques for Radiotherapy Planning of Gliomas

The accuracy of target delineation in radiation treatment (RT) planning of cerebral gliomas is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Conventional magnetic resonance imaging (MRI), including contrast-enhanced T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, represents the current standard imaging modality for target volume delineation of gliomas. However, conventional sequences have limited capability to discriminate treatment-related changes from viable tumors, owing to the low specificity of increased blood-brain barrier permeability and peritumoral edema. Advanced physiology-based MRI techniques, such as MR spectroscopy, diffusion MRI and perfusion MRI, have been developed for the biological characterization of gliomas and may circumvent these limitations, providing additional metabolic, structural, and hemodynamic information for treatment planning and monitoring. Radionuclide imaging techniques, such as positron emission tomography (PET) with amino acid radiopharmaceuticals, are also increasingly used in the workup of primary brain tumors, and their integration in RT planning is being evaluated in specialized centers. This review focuses on the basic principles and clinical results of advanced MRI and PET imaging techniques that have promise as a complement to RT planning of gliomas.

Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

PURPOSE OF REVIEW

This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET).

RECENT FINDINGS

Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.

Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI

PURPOSE

Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment.

METHODS

DW-MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 months after (n = 25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW-MRI signals were simulated from packings of synthetic cells built with well-defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρ_app ) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low- and high-grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment.

RESULTS

Significant (P < 0.05) differences in median ADC, D, vf, R, and ρ_app values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High- and low-risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρ_app was the parameter with highest AUC (0.90) and sensitivity (0.75).

CONCLUSIONS

Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient-specific proton therapy workflows.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

Recent Advances in Nanomedicine for the Diagnosis and Treatment of Prostate Cancer Bone Metastasis

Patients with advanced prostate cancer can develop painful and debilitating bone metastases. Currently available interventions for prostate cancer bone metastases, including chemotherapy, bisphosphonates, and radiopharmaceuticals, are only palliative. They can relieve pain, reduce complications (e.g., bone fractures), and improve quality of life, but they do not significantly improve survival times. Therefore, additional strategies to enhance the diagnosis and treatment of prostate cancer bone metastases are needed. Nanotechnology is a versatile platform that has been used to increase the specificity and therapeutic efficacy of various treatments for prostate cancer bone metastases. In this review, we summarize preclinical research that utilizes nanotechnology to develop novel diagnostic imaging tools, translational models, and therapies to combat prostate cancer bone metastases.

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.

Evaluation of metronomic chemotherapy response using diffusion and dynamic contrast-enhanced MRI

PURPOSE

To investigate the feasibility of using diffusion MRI (dMRI) and dynamic contrast-enhanced (DCE) MRI to evaluate the treatment response of metronomic chemotherapy (MCT) in the 4T1 mammary tumor model of locally advanced breast cancer.

METHODS

Twelve Balb/c mice with metastatic breast cancer were divided into treated and untreated (control) groups. The treated group (n = 6) received five treatments of anti-metabolite agent 5-Fluorouracil (5FU) in the span of two weeks. dMRI and DCE-MRI were acquired for both treated and control groups before and after MCT. Immunohistochemically staining and measurements were performed after the post-MRI measurements for comparison.

RESULTS

The control mice had significantly (p<0.005) larger tumors than the MCT treated mice. The DCE-MRI analysis showed a decrease in contrast enhancement for the control group, whereas the MCT mice had a more stable enhancement between the pre-chemo and post-chemo time points. This confirms the antiangiogenic effects of 5FU treatment. Comparing amplitude of enhancement revealed a significantly (p<0.05) higher enhancement in the MCT tumors than in the controls. Moreover, the MCT uptake rate was significantly (p<0.001) slower than the controls. dMRI analysis showed the MCT ADC values were significantly larger than the control group at the post-scan time point.

CONCLUSION

dMRI and DCE-MRI can be used as potential biomarkers for assessing the treatment response of MCT. The MRI and pathology observations suggested that in addition to the cytotoxic effect of cell kills, the MCT with a cytotoxic drug, 5FU, induced changes in the tumor vasculature similar to the anti-angiogenic effect.

Association between MRI histogram features and treatment response in locally advanced cervical cancer treated by chemoradiotherapy

OBJECTIVE

To examine the associations of histogram features of T2-weighted (T2W) images and apparent diffusion coefficient (ADC) with treatment response in locally advanced cervical cancer (LACC) following concurrent chemoradiotherapy (CCRT).

MATERIALS AND METHODS

Fifty-eight patients who underwent a 4-week CCRT regimen with MRI prior to treatment (pre-CCRT) and after treatment (post-CCRT) were retrospectively analysed. Histogram features were calculated from volumes of interest (VOIs) from one radiologist on T2W images and ADC maps. VOIs from two radiologists were used to assess observer repeatability in delineation and feature values at both time-points with the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). Treatment response was defined as a 90% reduction in tumour volume. Paired Mann-Whitney U tests were used to determine if features changed significantly between examinations. Two-sample Mann-Whitney U tests were used to identify features that were significantly different between response groups. Receiver operating characteristic (ROC) analysis was done on significantly different MRI features between treatment response groups.

RESULTS

Pre-CCRT delineation and feature repeatability were generally good (DSC > 0.700; ICC > 0.750). Post-CCRT repeatability was low (DSC < 0.700; ICC < 0.750), but ADC mean and percentiles retained good ICC scores. All features, except for T2W_Kurtosis, significantly changed between examinations. Post-CCRT ADC₅₀ was the only feature that demonstrated both good observer variability and significant differences between treatment response groups (p = 0.036) and had an AUC of 0.701 with a cut-off of 1.357 × 10^-6 mm²/s.

CONCLUSION

ADC and T2W histogram features could be used to track changes in LACC tumours undergoing CCRT. Post-CCRT ADC₅₀ was associated with treatment response with good observer repeatability.

KEY POINTS

• Pre-treatment tumour delineation and histogram feature values had good observer repeatability, while these were less repeatable at post-treatment. • MRI histogram analysis could be used to track changes in the tumour as it undergoes concurrent chemoradiotherapy. • Post-treatment median ADC was associated with treatment response and had good repeatability.

Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response

PURPOSE

MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments.

METHODS

DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison.

RESULTS

Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes.

CONCLUSIONS

There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.

Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs

The objective of this study was to explore radiomics features from longitudinal diffusion-weighted MRIs (DWIs) for pathologic treatment effect prediction in patients with localized soft tissue sarcoma (STS) undergoing hypofractionated preoperative radiotherapy (RT). Thirty patients with localized STS treated with preoperative hypofractionated RT were recruited to this longitudinal imaging study. DWIs were acquired at three time points using a 0.35 T MRI-guided radiotherapy system. Treatment effect score (TES) was obtained from the post-surgery pathology as a surrogate of treatment outcome. Patients were divided into two groups based on TES. Response prediction was first performed using a support vector machine (SVM) with only mean apparent diffusion coefficient (ADC) or delta ADC to serve as the benchmark. Radiomics features were then extracted from tumor ADC maps at each of the three time points. Logistic regression and SVM were constructed to predict the TES group using features selected by univariate analysis and sequential forward selection. Classification performance using SVM with features from different time points and with or without delta radiomics were evaluated. Prediction performance using only mean ADC or delta ADC was poor (area under the curve (AUC) < 0.7). For the radiomics study using features from all time points and corresponding delta radiomics, SVM significantly outperformed logistic regression (AUC of 0.91 ± 0.05 v.s. 0.85 ± 0.06). Prediction AUC values using single or multiple time points without delta radiomics were all below 0.74. Including delta radiomics of mid- or post-treatment relative to the baseline drastically boosted the prediction. In this work, an SVM model was built to predict the TES using radiomics features from longitudinal DWI. Based on this study, we found that use of mean ADC, delta ADC, or radiomics features alone was not sufficient for response prediction, and including delta radiomics features of mid- or post-treatment relative to the baseline can optimize the prediction of TES, a pathologic and clinical endpoint.