The parametric response map is an imaging biomarker for early cancer treatment outcome

Parametric response mapping of co-registered intravoxel incoherent motion magnetic resonance imaging and positron emission tomography in locally advanced cervical cancer undergoing concurrent chemoradiation therapy

Background and Purpose

Intravoxel-incoherent-motion (IVIM) magnetic-resonance-imaging (MRI) and positron-emission-tomography (PET) have been investigated independently but not voxel-wise to evaluate tumor microenvironment in cervical carcinoma patients. Whether regionally combined information of IVIM and PET offers additional predictive benefit over each modality independently has not been explored. Here, we investigated parametric-response-mapping (PRM) of co-registered PET and IVIM in cervical cancer patients to identify sub-volumes that may predict tumor shrinkage to concurrent-chemoradiation-therapy (CCRT).

Materials and Methods

Twenty cervical cancer patients (age: 63[41-85]) were retrospectively evaluated. Diffusion-weighted-images (DWIs) were acquired on 3.0 T MRIs using a free-breathing single-shot-spin echo-planar-imaging (EPI) sequence. Pre- and on-treatment (∼after four-weeks of CCRT) MRI and pre-treatment FDG-PET/CT were acquired. IVIM model-fitting on the DWIs was performed using a Bayesian-fitting simplified two-compartment model. Three-dimensional rigidly-registered maps of PET/CT standardized-uptake-value (SUV) and IVIM diffusion-coefficient (D) and perfusion-fraction (f) were generated. Population-means of PET-SUV, IVIM-D and IVIM-f from pre-treatment-scans were calculated and used to generate PRM via a voxel-wise joint-histogram-analysis to classify voxels as high/low metabolic-activity and with high/low (hi/lo) cellular-density. Similar PRM maps were generated for SUV and f.

Results

Tumor-volume (p < 0.001) significantly decreased, while IVIM-f (p = 0.002) and IVIM-D (p = 0.03) significantly increased on-treatment. Pre-treatment tumor-volume (r = -0.45,p = 0.04) and PRM-SUVhi D lo (r = -0.65,p = 0.002) negatively correlated with ΔGTV, while pre-treatment IVIM-D (r = 0.64,p = 0.002), PRM-SUVlo f hi (r = 0.52,p = 0.02), and PRM-SUVlo D hi (r = 0.74,p < 0.001) positively correlated with ΔGTV.

Conclusion

IVIM and PET was performed on cervical cancer patients undergoing CCRT and we observed that both IVIM-f and IVIM-D increased during treatment. Additionally, PRM was applied, and sub-volumes were identified that were related to ΔGTV.

LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT- a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320×320×320 . We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease.

Ethiodized oil as an imaging biomarker after conventional transarterial chemoembolization

Conventional transarterial chemoembolization (cTACE) utilizing ethiodized oil as a chemotherapy carrier has become a standard treatment for intermediate-stage hepatocellular carcinoma (HCC) and has been adopted as a bridging and downstaging therapy for liver transplantation. Water-in-oil emulsion made up of ethiodized oil and chemotherapy solution is retained in tumor vasculature resulting in high tissue drug concentration and low systemic chemotherapy doses. The density and distribution pattern of ethiodized oil within the tumor on post-treatment imaging are predictive of the extent of tumor necrosis and duration of response to treatment. This review describes the multiple roles of ethiodized oil, particularly in its role as a biomarker of tumor response to cTACE. CLINICAL RELEVANCE: With the increasing complexity of locoregional therapy options, including the use of combination therapies, treatment response assessment has become challenging; Ethiodized oil deposition patterns can serve as an imaging biomarker for the prediction of treatment response, and perhaps predict post-treatment prognosis. KEY POINTS: • Treatment response assessment after locoregional therapy to hepatocellular carcinoma is fraught with multiple challenges given the varied post-treatment imaging appearance. • Ethiodized oil is unique in that its' radiopacity can serve as an imaging biomarker to help predict treatment response. • The pattern of deposition of ethiodozed oil has served as a mechanism to detect portions of tumor that are undertreated and can serve as an adjunct to enhancement in order to improve management in patients treated with intraarterial embolization with ethiodized oil.

Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system

PURPOSE

MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac.

METHODS AND MATERIALS

The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes.

RESULTS

The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared.

CONCLUSION

Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.

Pattern recognition of microcirculation with super-resolution ultrasound imaging provides markers for early tumor response to anti-angiogenic therapy

Rationale: Cancer treatment outcome is traditionally evaluated by tumor volume change in clinics, while tumor microvascular heterogeneity reflecting tumor response has not been fully explored due to technical limitations. Methods: We introduce a new paradigm in super-resolution ultrasound imaging, termed pattern recognition of microcirculation (PARM), which identifies both hemodynamic and morphological patterns of tumor microcirculation hidden in spatio-temporal space trajectories of microbubbles. Results: PARM demonstrates the ability to distinguish different local blood flow velocities separated by a distance of 24 μm. Compared with traditional vascular parameters, PARM-derived heterogeneity parameters prove to be more sensitive to microvascular changes following anti-angiogenic therapy. Particularly, PARM-identified "sentinel" microvasculature, exhibiting evident structural changes as early as 24 hours after treatment initiation, correlates significantly with subsequent tumor volume changes (|r| > 0.9, P < 0.05). This provides prognostic insight into tumor response much earlier than clinical criteria. Conclusions: The ability of PARM to noninvasively quantify tumor vascular heterogeneity at the microvascular level may shed new light on early-stage assessment of cancer therapy.

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network

BACKGROUND

Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction.

PURPOSE

We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available.

METHODS

A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics.

RESULTS

Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively.

CONCLUSIONS

Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

Can the apparent transverse relaxation rate (R2*) evaluate the efficacy of concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma? a preliminary experience

BACKGROUND

The use of the apparent transverse relaxation rate (R2^*) in nasopharyngeal carcinoma (NPC) has not been previously reported in the literature. The aim of this study was to investigate the role of the R2^* value in evaluating response to concurrent chemoradiotherapy (CCRT) in patients with NPC.

METHODS

Forty-one patients with locoregionally advanced NPC confirmed by pathology were examined by blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) before and after CCRT, and conventional MRI was performed 3 months after the completion of CCRT. All patients were divided into a responding group (RG) and a nonresponding group (NRG), according to MRI findings 3 months after the end of treatment. The R2^* values before (R2*_preT) and after (R2^*_postT) CCRT and the ΔR2^* (ΔR2^*=R2^*_postT - R2^*_preT) were calculated in the tumor.

RESULTS

Among the 41 patients, 26 were in the RG and 15 were in the NRG. There was no statistical difference in the R2^*_preT between RG and NRG (P = 0.307); however, there were significant differences in R2^*_postT and ΔR2^* (P < 0.001). The area under the curve of R2^*_postT and ΔR2^* for predicting the therapeutic response of NPC was 0.897 and 0.954, respectively, with cutoff values of 40.95 and 5.50 Hz, respectively.

CONCLUSION

The R2^* value can be used as a potential imaging indicator to evaluate the therapeutic response of locoregionally advanced NPC.

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 PRM_Tmax-, PRM_rCBV-, and PRM_rCBV+ 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.

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.

Introducing a New Biomarker Named R2*-BOLD-MRI Parameter to Assess Treatment Response in Osteosarcoma

BACKGROUND

While histologic response to neoadjuvant chemotherapy (NChT) is the major prognostic factor for osteosarcoma treatment, evaluating that response is difficult.

PURPOSE

To evaluate the feasibility of the blood oxygen level-dependent (BOLD) technique to assess the response to NChT.

STUDY TYPE

Prospective.

POPULATION

Twelve patients with osteosarcoma undergoing NChT.

FIELD STRENGTH/SEQUENCE

3 T; T2*-weighted BOLD, dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) (b values of 0, 400, and 1400 seconds/mm² ) sequences.

ASSESSMENT

Examination was performed before treatment (first), after each cycle of treatment (second and third). At each time point, spin dephasing rates (R2*) from BOLD magnetic resonance imaging (MRI), parameters from DCE-MRI (volume transfer constant [K^trans ], reflux rate [k_ep ], volume fraction of the extravascular extracellular matrix [v_e ], and blood plasma volume [v_p ]), and the apparent diffusion coefficient (ADC) from DW-MRI were measured.

STATISTICAL TESTS

Wilcoxon's signed rank test, Spearman's correlation coefficient (ρ) were used. A P-value of <0.05 was considered statistically significant.

RESULTS

The difference and relative difference of the R2* values between the first/third MRIs in the extraosseous portion were statistically significant. Only the differences in the k_ep values between the first/second and between the first/third MRIs in the extraosseous portion were significant. The differences in the ADCs in the extraosseous and osseous portions were not statistically significant (P = 0.151, P = 0.733 each in extraosseous portion and P = 0.569, P = 0.129 each in osseous portion). The relative difference in R2* values in the extraosseous portion between the first/third MRI (ρ = 0.706) was significantly better correlated with the pathologic grade than those of k_ep and ADC over the same period (ρ = 0.286 and ρ = -0.091, respectively).

DATA CONCLUSION

The R2* from the BOLD MRI technique could be a useful biomarker for evaluating treatment response in osteosarcoma treated with NchT.

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 2.

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.

Low conductivity on electrical properties tomography demonstrates unique tumor habitats indicating progression in glioblastoma

OBJECTIVES

Tissue conductivity measurements made with electrical properties tomography (EPT) can be used to define temporal changes in tissue habitats on longitudinal multiparametric MRI. We aimed to demonstrate the added insights for identifying tumor habitats obtained by including EPT with diffusion- and perfusion-weighted MRI, and to evaluate the use of these tumor habitats for determining tumor treatment response in post-treatment glioblastoma.

METHODS

Tumor habitats were developed from EPT, diffusion-weighted, and perfusion-weighted MRI in 60 patients with glioblastoma who underwent concurrent chemoradiotherapy. Voxels from EPT, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) maps were clustered into habitats, and each habitat was serially examined to assess its temporal change. The usefulness of temporal changes in tumor habitats for diagnosing tumor progression and treatment-related change was investigated using logistic regression. The performance of significant predictors was measured using the area under the curve (AUC) from receiver-operating-characteristics analysis with 1000-fold bootstrapping.

RESULTS

Five tumor habitats were identified, and of these, the hypervascular cellular habitat (odds ratio [OR] 5.45; 95% CI, 1.75-31.42; p = .02), hypovascular low conductivity habitat (OR 2.00; 95% CI, 1.45-3.05; p < .001), and hypovascular intermediate habitat (OR 1.57; 95% CI, 1.18-2.30; p = .006) were predictive of tumor progression. Low EPT and low CBV reflected a unique hypovascular low conductivity habitat that showed the highest diagnostic performance (AUC 0.86; 95% CI, 0.76-0.96). The combined habitats showed high performance (AUC 0.90; 95% CI, 0.82-0.98) in the differentiation of tumor progression from treatment-related change.

CONCLUSION

EPT reveals low conductivity habitats that can improve the diagnosis of tumor progression in post-treatment glioblastoma.

KEY POINTS

• Electrical properties tomography (EPT) demonstrated lower conductivity in tumor progression than in treatment-related change. • EPT allowed identification of a unique hypovascular low conductivity habitat when combined with cerebral blood volume mapping. • Tumor habitats with a hypovascular low conductivity habitat, hypervascular cellular habitat, and hypovascular intermediate habitat yielded high diagnostic performance for diagnosing tumor progression.

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.

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.

Prediction of optimal contrast times post-imaging agent administration to inform personalized fluorescence-guided surgery

SIGNIFICANCE

Fluorescence guidance in cancer surgery (FGS) using molecular-targeted contrast agents is accelerating, yet the influence of individual patients' physiology on the optimal time to perform surgery post-agent-injection is not fully understood.

AIM

Develop a mathematical framework and analytical expressions to estimate patient-specific time-to-maximum contrast after imaging agent administration for single- and paired-agent (coadministration of targeted and control agents) protocols.

APPROACH

The framework was validated in mouse subcutaneous xenograft studies for three classes of imaging agents: peptide, antibody mimetic, and antibody. Analytical expressions estimating time-to-maximum-tumor-discrimination potential were evaluated over a range of parameters using the validated framework for human cancer parameters.

RESULTS

Correlations were observed between simulations and matched experiments and metrics of tumor discrimination potential (p  <  0.05). Based on human cancer physiology, times-to-maximum contrast for peptide and antibody mimetic agents were <200  min, >15  h for antibodies, on average. The analytical estimates of time-to-maximum tumor discrimination performance exhibited errors of <10  %   on average, whereas patient-to-patient variance is expected to be greater than 100%.

CONCLUSION

We demonstrated that analytical estimates of time-to-maximum contrast in FGS carried out patient-to-patient can outperform the population average time-to-maximum contrast used currently in clinical trials. Such estimates can be made with preoperative DCE-MRI (or similar) and knowledge of the targeted agent's binding affinity.

Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma

PURPOSE

Heterogeneity in glioblastomas is associated with poorer outcomes, and physiologic heterogeneity can be quantified with noninvasive imaging. We developed spatial habitats based on multiparametric physiologic MRI and evaluated associations between temporal changes in these habitats and progression-free survival (PFS) after concurrent chemoradiotherapy (CCRT) in patients with glioblastoma.

EXPERIMENTAL DESIGN

Ninety-seven patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma were enrolled and two serial MRI examinations after CCRT were analyzed. Cerebral blood volumes and apparent diffusion coefficients were grouped using k-means clustering into three spatial habitats. Associations between temporal changes in spatial habitats and PFS were investigated using Cox proportional hazard modeling. The performance of significant predictors for PFS and overall survival (OS) was measured using a discrete increase of habitat (habitat risk score) in a temporal validation set from a prospective registry (n = 53, ClinicalTrials.gov NCT02619890). The site of progression was matched with the spatiotemporal habitats.

RESULTS

Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. A short-term increase in the hypervascular cellular habitat (HR, 40.0; P = 0.001) and hypovascular cellular habitat was significantly associated with shorter PFS (HR, 3.78; P < 0.001) after CCRT. Combined with clinical predictors, the habitat risk score showed a C-index of 0.79 for PFS and 0.74 for OS and stratified patients with short, intermediate, and long PFS (P = 0.016). An increase in the hypovascular cellular habitat predicted tumor progression sites.

CONCLUSIONS

Hypovascular cellular habitats derived from multiparametric physiologic MRIs may be useful predictors of clinical outcomes in patients with posttreatment glioblastoma.

Decreased Relative Cerebral Blood Flow in Unmedicated Heroin-Dependent Individuals

Understanding the brain mechanisms of heroin dependence is invaluable for developing effective treatment. Measurement of regional cerebral blood flow (CBF) provides a method to visualize brain circuits that are functionally impaired by heroin dependence. This study examined regional CBF alterations and their clinical associations in unmedicated heroin-dependent individuals (HDIs) using a relatively large sample. Sixty-eight (42 males, 26 females; age: 40.9 ± 7.3 years) HDIs and forty-seven (34 males, 13 females; age: 39.3 ± 9.2 years) matched healthy controls (HCs) underwent high-resolution T1 and whole-brain arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) scans. Additionally, clinical characteristics were collected for neurocognitive assessments. HDIs showed worse neuropsychological performance than HCs and had decreased relative CBF (rCBF) in the bilateral middle frontal gyrus (MFG), inferior temporal gyrus, precuneus, posterior cerebellar lobe, cerebellar vermis, and the midbrain adjacent to the ventral tegmental area; right posterior cingulate gyrus, thalamus, and calcarine. rCBF in the bilateral MFG was negatively correlated with Trail Making Test time in HDIs. HDIs had limbic, frontal, and parietal hypoperfusion areas. Low CBF in the MFG indicated cognitive impairment in HDIs. Together, these findings suggest the MFG as a critical region in HDIs and suggest ASL-derived CBF as a potential marker for use in heroin addiction studies.

Parametric Response Mapping of Coregistered Positron Emission Tomography and Dynamic Contrast Enhanced Computed Tomography to Identify Radioresistant Subvolumes in Locally Advanced Cervical Cancer

PURPOSE

To identify subvolumes that may predict treatment response to definitive concurrent chemoradiation therapy using parametric response mapping (PRM) of coregistered positron emission tomography (PET) and dynamic contrast-enhanced (DCE) computed tomography (CT) in locally advanced cervical carcinoma.

METHODS AND MATERIALS

Pre- and midtreatment (after 23 ± 4 days of concurrent chemoradiation therapy) DCE CT and PET imaging were performed on 21 patients with cervical cancer who were enrolled in a pilot study to evaluate the prognostic value of CT perfusion for primary cervical cancer (NCT01805141). Three-dimensional coregistered maps of PET/CT standardized uptake value (SUV) and DCE CT blood flow (BF) were generated. PRM was performed using voxel-wise joint histogram analysis to classify voxels within the tumor as highly metabolic and perfused (SUV^hiBF^hi), highly metabolic and hypoxic (SUV^hiBF^lo), low metabolic activity and hypoxic (SUV^loBF^lo), or low metabolic activity and perfused (SUV^loBF^hi) tissue based on thresholds determined from population means of pretreatment PET SUV and DCE CT BF. Relationships between baseline pretreatment imaging metrics and relative changes in metabolic tumor volume (ΔMTV), calculated from before treatment and during treatment imaging, were determined using univariable and multivariable linear regression models.

RESULTS

The relative volume of three PRM subvolumes significantly changed during treatment (SUV^hiBF^hi: P = .04; SUV^hiBF^lo: P = .0008; SUV^loBF^hi: P = .02), whereas SUV^loBF^lo did not (P = .9). Pretreatment PET SUV_max (r = -.58, P = .006), PET SUV_mean (ρ = -.59, P = .005), DCE CT BF_mean (r = -.50, P = .02), tumor volume (ρ = -.65, P = .001) and PRM SUV^hiBF^hi (ρ = -.59, P = .004) were negatively correlated with ΔMTV, whereas PRM SUV^loBF^lo was positively related to ΔMTV (r = .77, P < .0001). In a multivariable model that predicted ΔMTV, PRM SUV^loBF^lo, which combines both PET/CT and DCE CT, was the only significant variable (β = 1.825, P = .03), dominating both imaging modalities independently.

CONCLUSIONS

PRM was applied in locally advanced cervical carcinoma treated definitively with chemoradiation, and radioresistant subvolumes were identified that correlated with changes in MTV and predicted treatment response. Identification of these subvolumes may assist in clinical decision making to tailor therapies, such as brachytherapy, in an effort to improve patient outcomes.

ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging

Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.

Developing a Pipeline for Multiparametric MRI-Guided Radiation Therapy: Initial Results from a Phase II Clinical Trial in Newly Diagnosed Glioblastoma

Quantitative mapping of hyperperfused and hypercellular regions of glioblastoma has been proposed to improve definition of tumor regions at risk for local recurrence following conventional radiation therapy. As the processing of the multiparametric dynamic contrast-enhanced (DCE-) and diffusion-weighted (DW-) magnetic resonance imaging (MRI) data for delineation of these subvolumes requires additional steps that go beyond the standard practices of target definition, we sought to devise a workflow to support the timely planning and treatment of patients. A phase II study implementing a multiparametric imaging biomarker for tumor hyperperfusion and hypercellularity consisting of DCE-MRI and high b-value DW-MRI to guide intensified (75 Gy/30 fractions) radiation therapy (RT) in patients with newly diagnosed glioblastoma was launched. In this report, the workflow and the initial imaging outcomes of the first 12 patients are described. Among all the first 12 patients, treatment was initiated within 6 weeks of surgery and within 2 weeks of simulation. On average, the combined hypercellular volume and high cerebral blood volume/tumor perfusion volume were 1.8 times smaller than the T1 gadolinium abnormality and 10 times smaller than the FLAIR abnormality. Hypercellular volume and high cerebral blood volume/tumor perfusion volume each identified largely distinct regions and showed 57% overlap with the enhancing abnormality, and minimal-to-no extension outside of the FLAIR. These results show the feasibility of implementing a workflow for multiparametric magnetic resonance-guided radiation therapy into clinical trials with a coordinated multidisciplinary team, and the unique and complementary tumor subregions identified by the combination of high b-value DW-MRI and DCE-MRI.

T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology

Background

This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma.

Methods

Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed.

Results

The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen's kappa = 0.75 [95% CI, 0.57-0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005).

Conclusions

This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.

The matrikine acetyl-proline-glycine-proline and clinical features of COPD: findings from SPIROMICS

BACKGROUND

Pulmonary and systemic inflammation are central features of chronic obstructive pulmonary disease (COPD). Previous studies have demonstrated relationships between biologically active extracellular matrix components, or matrikines, and COPD pathogenesis. We studied the relationships between the matrikine acetyl-proline-glycine-proline (AcPGP) in sputum and plasma and clinical features of COPD.

METHODS

Sputum and plasma samples were obtained from COPD participants in the SPIROMICS cohort at enrollment. AcPGP was isolated using solid phase extraction and measured by mass spectrometry. Demographics, spirometry, quality of life questionnaires, and quantitative computed tomography (CT) imaging with parametric response mapping (PRM) were obtained at baseline. Severe COPD exacerbations were recorded at 1-year of prospective follow-up. We used linear and logistic regression models to measure associations between AcPGP and features of COPD, and Kaplan-Meier analyses to measure time-to-first severe exacerbation.

RESULTS

The 182 COPD participants in the analysis were 66 ± 8 years old, 62% male, 84% White race, and 39% were current smokers. AcPGP concentrations were 0.61 ± 1.89 ng/mL (mean ± SD) in sputum and 0.60 ± 1.13 ng/mL in plasma. In adjusted linear regression models, sputum AcPGP was associated with FEV₁/FVC, spirometric GOLD stage, PRM-small airways disease, and PRM-emphysema. Sputum AcPGP also correlated with severe AECOPD, and elevated sputum AcPGP was associated with shorter time-to-first severe COPD exacerbation. In contrast, plasma AcPGP was not associated with symptoms, pulmonary function, or severe exacerbation risk.

CONCLUSIONS

In COPD, sputum but not plasma AcPGP concentrations are associated with the severity of airflow limitation, small airways disease, emphysema, and risk for severe AECOPD at 1-year of follow-up.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT01969344 (SPIROMICS).

Metabolic Tumor Volume Response Assessment Using (11)C-Methionine Positron Emission Tomography Identifies Glioblastoma Tumor Subregions That Predict Progression Better Than Baseline or Anatomic Magnetic Resonance Imaging Alone

Purpose

To evaluate whether response assessment of newly diagnosed glioblastoma at 3 months using ¹¹C-methionine-positron emission tomography (MET-PET) is better associated with patient outcome compared with baseline MET-PET or anatomic magnetic resonance imaging alone.

Methods and Materials

Patients included were participants in a phase I/II trial of dose-escalated chemoradiation based on anatomic magnetic resonance imaging. Automated segmentation of metabolic tumor volume (MTV) was performed at a threshold of 1.5 times mean cerebellar uptake. Progression-free (PFS) and overall survival were estimated with the Kaplan-Meier method and compared with log-rank tests. Multivariate analysis for PFS and overall survival was performed using Cox proportional hazards, and spatial overlap between imaging and recurrence volumes were analyzed.

Results

Among 37 patients, 15 had gross total resection, of whom 10 (67%) had residual MTV, 16 subtotal resection, and 6 biopsy alone. Median radiation therapy dose was 75 Gy (range, 66-81). Median baseline T1 Gd-enhanced tumor volume (GTV-Gd) was 38.0 cm³ (range, 8.0-81.5). Median pre-CRT MTV was 4.9 cm³ (range, 0-43.8). Among 25 patients with 3-month MET-PET, MTV was only 2.4 cm³ (range, 0.004-18.0) in patients with uptake. Patients with MTV = 0 cm³ at 3 months had superior PFS (18.2 vs 10.1 months, P = .03). On multivariate analysis, larger 3-month MTV (hazard ratio [HR] 2.4, 95% confidence interval [CI], 1.4-4.3, P = .03), persistent MET-PET subvolume (overlap of pre-CRT and 3 month MTV; HR 2.0, 95% CI, 1.2-3.4, P = .06), and increase in MTV (HR 1.8, 95% CI, 1.1-3.1, P = .09) were the only imaging factors significant for worse PFS. GTV-Gd at recurrence encompassed 97% of the persistent MET-PET subvolume (interquartile range 72%-100%), versus 71% (interquartile range 39%-93%) of baseline MTV, 54% of baseline GTV-Gd (18%-87%), and 78% of 3-month MTV (47%-95%).

Conclusions

The majority of patients with apparent gross total resection of glioblastoma have measurable postoperative MTV. Total and persisting MTV 3 months post-CRT were significant predictors of PFS, and persistent MET-PET subvolume was the strongest predictor for localizing tumor recurrence.

Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration

We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.

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.

Data-driven identification of tumor subregions based on intravoxel incoherent motion reveals association with proliferative activity

PURPOSE

Intravoxel incoherent motion (IVIM) analysis gives information on tissue diffusion and perfusion and may thus have a potential for e.g. tumor tissue characterization. This work aims to study if clustering based on IVIM parameter maps can identify tumor subregions, and to assess the relevance of obtained subregions by histological analysis.

METHODS

Fourteen mice with human neuroendocrine tumors were examined with diffusion-weighted imaging to obtain IVIM parameter maps. Gaussian mixture models with IVIM maps from all tumors as input were used to partition voxels into k clusters, where k = 2 was chosen for further analysis based on goodness of fit. Clustering was performed with and without the perfusion-related IVIM parameter D * , and with and without including spatial information. The validity of the clustering was assessed by comparison with corresponding histologically stained tumor sections. A Ki-67-based index quantifying the degree of tumor proliferation was considered appropriate for the comparison based on the obtained cluster characteristics.

RESULTS

The clustering resulted in one class with low diffusion and high perfusion and another with slightly higher diffusion and low perfusion. Strong agreement was found between tumor subregions identified by clustering and subregions identified by histological analysis, both regarding size and spatial agreement. Neither D * nor spatial information had substantial effects on the clustering results.

CONCLUSIONS

The results of this study show that IVIM parameter maps can be used to identify tumor subregions using a data-driven framework based on Gaussian mixture models. In the studied tumor model, the obtained subregions showed agreement with proliferative activity.

Parametric response mapping cut-off values that predict survival of hepatocellular carcinoma patients after TACE

PURPOSE

Parametric response mapping (PRM) is a novel image-analysis technique applicable to assess tumor viability and predict intrahepatic recurrence of hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE). However, to date, the prognostic value of PRM for prediction of overall survival in HCC patients undergoing TACE is unclear. The objective of this explorative, single-center study was to identify cut-off values for voxel-specific PRM parameters that predict the post TACE overall survival in HCC patients.

METHODS

PRM was applied to biphasic CT data obtained at baseline and following 3 TACE treatments of 20 patients with HCC tumors ≥ 2 cm. The individual portal venous phases were registered to the arterial phases followed by segmentation of the largest lesion, i.e., the region of interest (ROI). Segmented voxels with their respective arterial and portal venous phase density values were displayed as a scatter plot. Voxel-specific PRM parameters were calculated and compared to patients' survival at 1, 2, and 3 years post treatment to identify the maximal predictive parameters.

RESULTS

The hypervascularized tissue portion of the ROI was found to represent an independent predictor of the post TACE overall survival. For this parameter, cut-off values of 3650, 2057, and 2057 voxels, respectively, were determined to be optimal to predict overall survival at 1, 2, and 3 years after TACE. Using these cut points, patients were correctly classified as having died with a sensitivity of 80, 92, and 86% and as still being alive with a specificity of 60, 75, and 83%, respectively. The prognostic accuracy measured by area under the curve (AUC) values ranged from 0.73 to 0.87.

CONCLUSION

PRM may have prognostic value to predict post TACE overall survival in HCC patients.

The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy

OBJECTIVE:

Stereotactic ablative radiotherapy (SABR) is being increasingly used as a non-invasive treatment for early-stage non-small cell lung cancer (NSCLC). A non-invasive method to estimate treatment outcomes in these patients would be valuable, especially since access to tissue specimens is often difficult in these cases.

METHODS:

We developed a method to predict survival following SABR in NSCLC patients using analysis of quantitative image features on pre-treatment CT images. We developed a Cox Lasso model based on two-dimensional Riesz wavelet quantitative texture features on CT scans with the goal of separating patients based on survival.

RESULTS:

The median log-rank p-value for 1000 cross-validations was 0.030. Our model was able to separate patients based upon predicted survival. When we added tumor size into the model, the p-value lost its significance, demonstrating that tumor size is not a key feature in the model but rather decreases significance likely due to the relatively small number of events in the dataset. Furthermore, running the model using Riesz features extracted either from the solid component of the tumor or from the ground glass opacity (GGO) component of the tumor maintained statistical significance. However, the p-value improved when combining features from the solid and the GGO components, demonstrating that there are important data that can be extracted from the entire tumor.

CONCLUSIONS:

The model predicting patient survival following SABR in NSCLC may be useful in future studies by enabling prediction of survival-based outcomes using radiomics features in CT images.

ADVANCES IN KNOWLEDGE:

Quantitative image features from NSCLC nodules on CT images have been found to significantly separate patient populations based on overall survival (p = 0.04). In the long term, a non-invasive method to estimate treatment outcomes in patients undergoing SABR would be valuable, especially since access to tissue specimens is often difficult in these cases.

Probing tumor microenvironment in patients with newly diagnosed glioblastoma during chemoradiation and adjuvant temozolomide with functional MRI

Functional MRI may identify critical windows of opportunity for drug delivery and distinguish between early treatment responders and non-responders. Using diffusion-weighted, dynamic contrast-enhanced, and dynamic susceptibility contrast MRI, as well as pro-angiogenic and pro-inflammatory blood markers, we prospectively studied the physiologic tumor-related changes in fourteen newly diagnosed glioblastoma patients during standard therapy. 153 MRI scans and blood collection were performed before chemoradiation (baseline), weekly during chemoradiation (week 1–6), monthly before each cycle of adjuvant temozolomide (pre-C1-C6), and after cycle 6. The apparent diffusion coefficient, volume transfer coefficient (K^trans), and relative cerebral blood volume (rCBV) and flow (rCBF) were calculated within the tumor and edema regions and compared to baseline. Cox regression analysis was used to assess the effect of clinical variables, imaging, and blood markers on progression-free (PFS) and overall survival (OS). After controlling for additional covariates, high baseline rCBV and rCBF within the edema region were associated with worse PFS (microvessel rCBF: HR = 7.849, p = 0.044; panvessel rCBV: HR = 3.763, p = 0.032; panvessel rCBF: HR = 3.984; p = 0.049). The same applied to high week 5 and pre-C1 K^trans within the tumor region (week 5 K^trans: HR = 1.038, p = 0.003; pre-C1 K^trans: HR = 1.029, p = 0.004). Elevated week 6 VEGF levels were associated with worse OS (HR = 1.034; p = 0.004). Our findings suggest a role for rCBV and rCBF at baseline and K^trans and VEGF levels during treatment as markers of response. Functional imaging changes can differ substantially between tumor and edema regions, highlighting the variable biologic and vascular state of tumor microenvironment during therapy.

Cluster versus ROI analysis to assess combined antiangiogenic therapy and radiotherapy in the F98 rat-glioma model

For glioblastoma (GBM), current therapeutic approaches focus on the combination of several therapies, each of them individually approved for GBM or other tumor types. Many efforts are made to decipher the best sequence of treatments that would ultimately promote the most efficient tumor response. There is therefore a strong interest in developing new clinical in vivo imaging procedures that can rapidly detect treatment efficacy and allow individual modulation of the treatment. In this preclinical study, we propose to evaluate tumor tissue changes under combined therapies, tumor vascular normalization under antiangiogenic treatment followed by radiotherapy, using a voxel-based clustering approach. This approach was applied to a rat model of glioma (F98). Six MRI parameters were mapped: apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissue oxygen saturation and vessel size index. We compared the classical region of interest (ROI)-based analysis with a cluster-based analysis. Five clusters, defined by their MRI features, were sufficient to characterize tumor progression and tumor changes during treatments. These results suggest that the cluster-based analysis was as efficient as the ROI-based analysis to assess tumor physiological changes during treatment, but also gave additional information regarding the voxels impacted by treatments and their localization within the tumor. Overall, cluster-based analysis appears to be a powerful tool for subtle monitoring of tumor changes during combined therapies.

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

We designed and generated pulmonary imaging biomarker pipelines to facilitate high-throughput research and point-of-care use in patients with chronic lung disease. Image processing modules and algorithm pipelines were embedded within a graphical user interface (based on the .NET framework) for pulmonary magnetic resonance imaging (MRI) and x-ray computed-tomography (CT) datasets. The software pipelines were generated using C++ and included: (1) inhaled He3/Xe129 MRI ventilation and apparent diffusion coefficients, (2) CT-MRI coregistration for lobar and segmental ventilation and perfusion measurements, (3) ultrashort echo-time H1 MRI proton density measurements, (4) free-breathing Fourier-decomposition H1 MRI ventilation/perfusion and free-breathing H1 MRI specific ventilation, (5) multivolume CT and MRI parametric response maps, and (6) MRI and CT texture analysis and radiomics. The image analysis framework was implemented on a desktop workstation/tablet to generate biomarkers of regional lung structure and function related to ventilation, perfusion, lung tissue texture, and integrity as well as multiparametric measures of gas trapping and airspace enlargement. All biomarkers were generated within 10 min with measurement reproducibility consistent with clinical and research requirements. The resultant pulmonary imaging biomarker pipeline provides real-time and automated lung imaging measurements for point-of-care and high-throughput research.

Evaluation of Parametric Response Mapping to Assess Therapeutic Response to Human Mesenchymal Stem Cells after Experimental Stroke

Stroke is the leading cause of disability in adults. After the very narrow time frame during which treatment by thrombolysis and mechanical thrombectomy is possible, cell therapy has huge potential for enhancing stroke recovery. Accurate analysis of the response to new therapy using imaging biomarkers is needed to assess therapeutic efficacy. The aim of this study was to compare 2 analysis techniques: the parametric response map (PRM), a voxel-based technique, and the standard whole-lesion approach. These 2 analyses were performed on data collected at 4 time points in a transient middle cerebral artery occlusion (MCAo) model, which was treated with human mesenchymal stem cells (hMSCs). The apparent diffusion coefficient (ADC), cerebral blood volume (CBV), and vessel size index (VSI) were mapped using magnetic resonance imaging (MRI). Two groups of rats received an intravenous injection of either 1 mL phosphate-buffered saline (PBS)-glutamine (MCAo-PBS, n = 10) or 3 million hMSCs (MCAo-hMSC, n = 10). One sham group was given PBS-glutamine (sham, n = 12). Each MRI parameter was analyzed by both the PRM and the whole-lesion approach. At day 9, 1 d after grafting, PRM revealed that hMSCs had reduced the fraction of decreased ADC (PRM_ADC−: MCAo-PBS 6.7% ± 1.7% vs. MCAo-hMSC 3.3% ± 2.4%), abolished the fraction of increased CBV (PRM_CBV+: MCAo-PBS 16.1% ± 3.7% vs. MCAo-hMSC 6.4% ± 2.6%), and delayed the fraction of increased VSI (PRM_VSI+: MCAo-PBS 17.5% ± 6.3% vs. MCAo-hMSC 5.4% ± 2.6%). The whole-lesion approach was, however, insensitive to these early modifications. PRM thus appears to be a promising technique for the detection of early brain changes following treatments such as cell therapy.

Quantitative sodium MR imaging: A review of its evolving role in medicine

Sodium magnetic resonance (MR) imaging in humans has promised metabolic information that can improve medical management in important diseases. This technology has yet to find a role in clinical practice, lagging proton MR imaging by decades. This review covers the literature that demonstrates that this delay is explained by initial challenges of low sensitivity at low magnetic fields and the limited performance of gradients and electronics available in the 1980s. These constraints were removed by the introduction of 3T and now ultrahigh (≥7T) magnetic field scanners with superior gradients and electronics for proton MR imaging. New projection pulse sequence designs have greatly improved sodium acquisition efficiency. The increased field strength has provided the expected increased sensitivity to achieve resolutions acceptable for metabolic interpretation even in small target tissues. Consistency of quantification of the sodium MR image to provide metabolic parametric maps has been demonstrated by several different pulse sequences and calibration procedures. The vital roles of sodium ion in membrane transport and the extracellular matrix will be reviewed to indicate the broad opportunities that now exist for clinical sodium MR imaging. The final challenge is for the technology to be supplied on clinical ≥3T scanners.

Multiplexed Profiling of Single Extracellular Vesicles

Extracellular vesicles (EV) are a family of cell-originating, membrane-enveloped nanoparticles with diverse biological function, diagnostic potential, and therapeutic applications. While EV can be abundant in circulation, their small size (∼4 order of magnitude smaller than cells) has necessitated bulk analyses, making many more nuanced biological explorations, cell of origin questions, or heterogeneity investigations impossible. Here we describe a single EV analysis (SEA) technique which is simple, sensitive, multiplexable, and practical. We profiled glioblastoma EV and discovered surprising variations in putative pan-EV as well as tumor cell markers on EV. These analyses shed light on the heterogeneous biomarker profiles of EV. The SEA technology has the potential to address fundamental questions in vesicle biology and clinical applications.

A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma

PURPOSE

Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method.

METHODS

MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast-enhancing lesion (CEL) and a 1 cm shell of surrounding peri-tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave-one-out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P-values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months.

RESULTS

Single-parameter PRM and multi-parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single-parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri-tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01).

CONCLUSIONS

The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single-parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi-parametric response into consideration and enables visualization. MPRM analysis of peri-tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset.

Correcting Nonpathological Variation in Longitudinal Parametric Response Maps of CT Scans in COPD Subjects: SPIROMICS

Small airways disease (SAD) is one of the leading causes of airflow limitations in patients diagnosed with chronic obstructive pulmonary disease (COPD). Parametric response mapping (PRM) of computed tomography (CT) scans allows for the quantification of this previously invisible COPD component. Although PRM is being investigated as a diagnostic tool for COPD, variability in the longitudinal measurements of SAD by PRM has been reported. Here, we show a method for correcting longitudinal PRM data because of non-pathological variations in serial CT scans. In this study, serial whole-lung high-resolution CT scans over a 30-day interval were obtained from 90 subjects with and without COPD accrued as part of SPIROMICS. It was assumed in all subjects that the COPD did not progress between examinations. CT scans were acquired at inspiration and expiration, spatially aligned to a single geometric frame, and analyzed using PRM. By modeling variability in longitudinal CT scans, our method could identify, at the voxel-level, shifts in PRM classification over the 30-day interval. In the absence of any correction, PRM generated serial percent volumes of functional SAD with differences as high as 15%. Applying the correction strategy significantly mitigated this effect with differences ~1%. At the voxel-level, significant differences were found between baseline PRM classifications and the follow-up map computed with and without correction (P <. 01 over GOLD). This strategy of accounting for nonpathological sources of variability in longitudinal PRM may improve the quantification of COPD phenotypes transitioning with disease progression.

Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study

OBJECTIVE

To find early predictors of histologic response in soft tissue sarcoma through volume transfer constant (K^trans) analysis based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

11 Patients with soft tissue sarcoma of the lower extremity that underwent preoperative chemoradiotherapy followed by limb salvage surgery were included in this retrospective study. For each patient, DCE-MRI data sets were collected before and two weeks after therapy initiation, and histologic tumor cell necrosis rate (TCNR) was reported at surgery. The DCE-MRI volumes were aligned by registration. Then, the aligned volumes were used to obtain the K^trans variation map. Accordingly, three sub-volumes (with increased, decreased or unchanged K^trans) were defined and identified, and fractions of the sub-volumes, denoted as F₊, F_- and F₀, respectively, were calculated. The predictive ability of volume fractions was determined by using area under a receiver operating characteristic curve (AUC). Linear regression analysis was performed to investigate the relationship between TCNR and volume fractions. In addition, the K^trans values of the sub-volumes were compared.

RESULTS

The AUC for F_- (0.896) and F₀ (0.833) were larger than that for change of tumor longest diameter ΔD (0.625) and the change of mean K^transΔK^trans¯ (0.792). Moreover, the regression results indicated that TCNR was directly proportional to F₀ (R²=0.75, P=0.0003), while it was inversely proportional to F_- (R²=0.77, P=0.0002). However, TCNR had relatively weak linear relationship with ΔK^trans¯ (R²=0.64, P=0.0018). Additionally, TCNR did not have linear relationship with DD (R²=0.16, P=0.1246).

CONCLUSION

The volume fraction F_- and F₀ have potential as early predictors of soft tissue sarcoma histologic response.

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping

PURPOSE

To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver.

METHODS

We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time.

RESULTS

Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters.

CONCLUSIONS

Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Parametric Response Mapping as an Imaging Biomarker in Lung Transplant Recipients

RATIONALE

The predominant cause of chronic lung allograft failure is small airway obstruction arising from bronchiolitis obliterans. However, clinical methodologies for evaluating presence and degree of small airway disease are lacking.

OBJECTIVES

To determine if parametric response mapping (PRM), a novel computed tomography voxel-wise methodology, can offer insight into chronic allograft failure phenotypes and provide prognostic information following spirometric decline.

METHODS

PRM-based computed tomography metrics quantifying functional small airways disease (PRM^fSAD) and parenchymal disease (PRM^PD) were compared between bilateral lung transplant recipients with irreversible spirometric decline and control subjects matched by time post-transplant (n = 22). PRM^fSAD at spirometric decline was evaluated as a prognostic marker for mortality in a cohort study via multivariable restricted mean models (n = 52).

MEASUREMENTS AND MAIN RESULTS

Patients presenting with an isolated decline in FEV₁ (FEV₁ First) had significantly higher PRM^fSAD than control subjects (28% vs. 15%; P = 0.005), whereas patients with concurrent decline in FEV₁ and FVC had significantly higher PRM^PD than control subjects (39% vs. 20%; P = 0.02). Over 8.3 years of follow-up, FEV₁ First patients with PRM^fSAD greater than or equal to 30% at spirometric decline lived on average 2.6 years less than those with PRM^fSAD less than 30% (P = 0.004). In this group, PRM^fSAD greater than or equal to 30% was the strongest predictor of survival in a multivariable model including bronchiolitis obliterans syndrome grade and baseline FEV_1% predicted (P = 0.04).

CONCLUSIONS

PRM is a novel imaging tool for lung transplant recipients presenting with spirometric decline. Quantifying underlying small airway obstruction via PRM^fSAD helps further stratify the risk of death in patients with diverse spirometric decline patterns.

Parametric response mapping of dynamic CT: enhanced prediction of survival in hepatocellular carcinoma patients treated with transarterial chemoembolization

PURPOSE

The aim of this study was to evaluate the prognostic significance of parametric response mapping (PRM) analysis for hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE).

METHODS

We recruited 65 HCC patients who underwent TACE. These patients underwent longitudinal multiphasic CT before and after TACE. We applied PRM analysis to the baseline CT before TACE and first/second follow-up CTs. The results of PRM analyses were used to stratify patients into responders and non-responders. Overall survival was compared between the two groups. An independent survival analysis using conventional radiological assessments was performed, and the results were compared with PRM results. Univariate and multivariate analyses were performed to identify clinical factors affecting survival.

RESULTS

The PRM analyses demonstrated that the responding group had a median survival of 529 days, while the non-responding group had a median survival of 263 days [hazard ratio (HR) 12.9, p < 0.05 for differences in survival]. The manual analyses indicated median survivals of 491 and 329 days for the responding and non-responding groups, respectively (HR 2.7, p < 0.05). Tumor size, albumin level, and PRM values were found to be significantly related to overall survival after univariate and multivariate analyses.

CONCLUSIONS

The PRM analysis could be a better predictor of overall survival for patients with HCC undergoing TACE than conventional radiological assessments.

The long and winding road to translation for imaging biomarker development: the case for arterial spin labelling (ASL)

Radiology is facing many challenges nowadays, and certainly needs to keep up with the fast pace of developments taking place in this field. This editorial aims at drawing the attention of the reader to the current establishment of quantitative imaging biomarkers, in particular through the efforts of the Quantitative Imaging Biomarker Alliance (QIBA) from the Radiological Society of North America (RSNA), as well as the European Imaging Biomarker Alliance (EIBALL) from the European Society of Radiology (ESR). The case of arterial spin labelling (ASL) is used as an example of the long and winding road to translate a good imaging technique into a clinically relevant imaging biomarker.

MR-guided radiation therapy: transformative technology and its role in the central nervous system

This review article describes advancement of magnetic resonance imaging technologies in radiation therapy planning, guidance, and adaptation of brain tumors. The potential for MR-guided radiation therapy to improve outcomes and the challenges in its adoption are discussed.

Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with head and neck tumors, a systematic review and meta-analysis

Background

Novel advanced MRI techniques are investigated in patients treated for head and neck tumors as conventional anatomical MRI is unreliable to differentiate tumor from treatment related imaging changes.

Purpose

As the diagnostic accuracy of MRI techniques to detect tumor residual or recurrence during or after treatment is variable reported in the literature, we performed a systematic meta-analysis.

Data sources

Pubmed, EMBASE and Web of Science were searched from their first record to September 23^th 2014.

Study selection

Studies reporting diagnostic accuracy of anatomical, ADC, perfusion or spectroscopy to identify tumor response confirmed by histology or follow-up in treated patients for head and neck tumors were selected by two authors independently.

Data analysis

Two authors independently performed data extraction including true positives, false positives, true negatives, false negatives and general study characteristics. Meta-analysis was performed using bivariate random effect models when ≥5 studies per test were included.

Data synthesis

We identified 16 relevant studies with anatomical MRI and ADC. No perfusion or spectroscopy studies were identified. Pooled analysis of anatomical MRI of the primary site (11 studies, N = 854) displayed a sensitivity of 84% (95%CI 72–92) and specificity of 82% (71–89). ADC of the primary site (6 studies, N = 287) showed a pooled sensitivity of 89% (74–96) and specificity of 86% (69–94).

Limitations

Main limitation are the low, but comparable quality of the included studies and the variability between the studies.

Conclusions

The higher diagnostic accuracy of ADC values over anatomical MRI for the primary tumor location emphases the relevance to include DWI with ADC for response evaluation of treated head and neck tumor patients.

Perfusion parameters as potential imaging biomarkers for the early prediction of radiotherapy response in a rat tumor model

PURPOSE

We aimed to compare various tumor-related radiologic morphometric changes and computed tomography (CT) perfusion parameters before and after treatment, and to determine the optimal imaging assessment technique for the prediction of early response in a rat tumor model treated with radiotherapy.

METHODS

Among paired tumors of FN13762 murine breast cancer cells implanted bilaterally in the necks of eight Fischer rats, tumors on the right side were treated with a single 20 Gy dose of radiotherapy. Perfusion CT studies were performed on day 0 before radiotherapy, and on days 1 and 5 after radiotherapy. Variables based on the size, including the longest diameter, tumor area, and volume, were measured. Quantitative perfusion analysis was performed for the whole tumor volume and permeabilities and blood volumes (BVs) were obtained. The area under the curve (AUC) difference in the histograms of perfusion parameters and texture analyses of uniformity and entropy were quantified. Apoptotic cell density was measured on pathology specimens immediately after perfusion imaging on day 5.

RESULTS

On day 1 after radiotherapy, differences in size between the irradiated and nonirradiated tumors were not significant. In terms of percent changes in the uniformity of permeabilities between tumors before irradiation and on day 1 after radiotherapy, the changes were significantly higher in the irradiated tumors than in the nonirradiated tumors (0.085 [-0.417, 0.331] vs. -0.131 [-0.536, 0.261], respectively; P = 0.042). The differences in AUCs of the histogram of voxel-by-voxel vascular permeability and BV in tumors between day 0 and day 1 were significantly higher in treated tumors compared with the control group (permeability, 21.4 [-2.2, 37.5] vs. 9.5 [-8.9, 33.8], respectively, P = 0.030; BV, 52.9 [-6186.0, 419.2] vs. 11.9 [-198.3, 346.7], respectively, P = 0.049). Apoptotic cell density showed a significantly positive correlation with the AUC difference of BV, the percent change of uniformity in permeability and BV (r=0.202, r=0.644, and r=0.706, respectively).

CONCLUSION

By enabling earlier tumor response prediction than morphometric evaluation, the histogram analysis of CT perfusion parameters appears to have a potential in providing prognostic predictive information in an irradiated rat model.

Neurologic Applications of PET/MR Imaging

PET/MR imaging benefits neurologic clinical care and research by providing spatially and temporally matched anatomic MR imaging, advanced MR physiologic imaging, and metabolic PET imaging. MR imaging sequences and PET tracers can be modified to target physiology specific to a neurologic disease process, with applications in neurooncology, epilepsy, dementia, cerebrovascular disease, and psychiatric and neurologic research. Simultaneous PET/MR imaging provides efficient acquisition of multiple temporally matched datasets, and opportunities for motion correction and improved anatomic assignment of PET data. Current challenges include optimizing MR imaging-based attenuation correction and necessity for dual expertise in PET and MR imaging.