Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Sensitivity to Change and Responsiveness of the Upper Extremity Fugl-Meyer Assessment in Individuals With Moderate to Severe Acute Stroke.

BACKGROUND: The Fugl-Meyer Assessment-Upper Extremity (FMA-UE) is a widely used outcome measure for quantifying motor impairment in stroke recovery. Meaningful change (responsiveness) in the acute to subacute phase of stroke recovery has not been determined. OBJECTIVE: Determine responsiveness and sensitivity to change of the FMA-UE from 1-week to 6-weeks (subacute) after stroke in individuals with moderate to severe arm impairment who received standard clinical care. METHODS: A total of 51 participants with resulting moderate and severe UE hemiparesis after stroke had FMA-UE assessment at baseline (within 2 weeks of stroke) and 6-weeks later. Sensitivity to change was assessed using Glass's delta, standardized response means (SRM), standard error of measure (SEM), and minimal detectable change (MDC). Responsiveness was assessed with the minimal clinically important difference (MCID), estimated using receiver operating characteristic curve analysis with patient-reported global rating of change scales (GROC) and a provider-reported modified Rankin Scale (mRS) as anchors. RESULTS: The MCID estimates were 13, 12, and 9 anchored to the GROC Arm Weakness, GROC Recovery, and mRS. Glass's delta and the SRM revealed large effect sizes, indicating high sensitivity to change, (∆ = 1.24, 95% CI [0.64, 1.82], SRM = 1.10). Results for the SEM and MDC were 2.46 and 6.82, respectively. CONCLUSION: The estimated MCID for the FMA-UE for individuals with moderate to severe motor impairment from 1 to 6-weeks after stroke is 13. These estimates will provide clinical context for FMA-UE change scores by helping to identify the change in upper-extremity motor impairment that is both beyond measurement error and clinically meaningful.

Distinguishing Distinct Neural Systems for Proximal vs Distal Upper Extremity Motor Control After Acute Stroke.

BACKGROUND AND OBJECTIVES: The classic and singular pattern of distal greater than proximal upper extremity motor deficits after acute stroke does not account for the distinct structural and functional organization of circuits for proximal and distal motor control in the healthy CNS. We hypothesized that separate proximal and distal upper extremity clinical syndromes after acute stroke could be distinguished and that patterns of neuroanatomical injury leading to these 2 syndromes would reflect their distinct organization in the intact CNS. METHODS: Proximal and distal components of motor impairment (upper extremity Fugl-Meyer score) and strength (Shoulder Abduction Finger Extension score) were assessed in consecutively recruited patients within 7 days of acute stroke. Partial correlation analysis was used to assess the relationship between proximal and distal motor scores. Functional outcomes including the Box and Blocks Test (BBT), Barthel Index (BI), and modified Rankin scale (mRS) were examined in relation to proximal vs distal motor patterns of deficit. Voxel-based lesion-symptom mapping was used to identify regions of injury associated with proximal vs distal upper extremity motor deficits. RESULTS: A total of 141 consecutive patients (49% female) were assessed 4.0 ± 1.6 (mean ± SD) days after stroke onset. Separate proximal and distal upper extremity motor components were distinguishable after acute stroke (p = 0.002). A pattern of proximal more than distal injury (i.e., relatively preserved distal motor control) was not rare, observed in 23% of acute stroke patients. Patients with relatively preserved distal motor control, even after controlling for total extent of deficit, had better outcomes in the first week and at 90 days poststroke (BBT, ρ = 0.51, p < 0.001; BI, ρ = 0.41, p < 0.001; mRS, ρ = 0.38, p < 0.001). Deficits in proximal motor control were associated with widespread injury to subcortical white and gray matter, while deficits in distal motor control were associated with injury restricted to the posterior aspect of the precentral gyrus, consistent with the organization of proximal vs distal neural circuits in the healthy CNS. DISCUSSION: These results highlight that proximal and distal upper extremity motor systems can be selectively injured by acute stroke, with dissociable deficits and functional consequences. Our findings emphasize how disruption of distinct motor systems can contribute to separable components of poststroke upper extremity hemiparesis.

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.

Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in "triple-negative" breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.

Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke.

BACKGROUND AND OBJECTIVES: Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes. METHODS: We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage. RESULTS: We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (ß = 0.21; 95% CI 0.04-0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (ß = -0.28; 95% CI -0.41 to -0.15, p < 0.001) and across multiple domains of function (ß = -0.14; 95% CI -0.22 to -0.06, p < 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%-58%, p = 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01-1.08, p = 0.004). DISCUSSION: We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

SMS-text messaging for collecting outcome measures after acute stroke.

Introduction: Traditional methods for obtaining outcomes for patients after acute stroke are resource-intensive. This study aimed to examine the feasibility, reliability, cost, and acceptability of collecting outcomes after acute stroke with a short message service (SMS)-text messaging program. Methods: Patients were enrolled in an SMS-text messaging program at acute stroke hospitalization discharge. Participants were prompted to complete assessments including the modified Rankin scale (mRS) and Patient-Reported Outcomes Measurement (PROM) Information System Global-10 at 30, 60, and 90 days postdischarge via SMS-text. Agreement and cost of SMS-text data collection were compared to those obtained from traditional follow-up methods (via phone or in the clinic). Participant satisfaction was surveyed upon program conclusion. Results: Of the 350 patients who agreed to receive SMS texts, 40.5% responded to one or more assessments. Assessment responders were more likely to have English listed as their preferred language (p = 0.009), have a shorter length of hospital stay (p = 0.01), lower NIH stroke scale upon admission (p < 0.001), and be discharged home (p < 0.001) as compared to nonresponders. Weighted Cohen's kappa revealed that the agreement between SMS texting and traditional methods was almost perfect for dichotomized (good vs. poor) (κ = 0.8) and ordinal levels of the mRS score (κ = 0.8). Polychoric correlations revealed a significant association for PROM scores ( ρ = 0.4, p < 0.01 and ρ = 0.4, p < 0.01). A cost equation showed that gathering outcomes via SMS texting would be less costly than phone follow-up for cohorts with more than 181 patients. Nearly all participants (91%) found the program acceptable and not burdensome (94%), and most (53%) felt it was helpful. Poststroke outcome data collection via SMS texting is feasible, reliable, low-cost, and acceptable. Reliability was higher for functional outcomes as compared to PROMs. Conclusions: While further validation is required, our findings suggest that SMS texting is a feasible method for gathering outcomes after stroke at scale to evaluate the efficacy of acute stroke treatments.

Medication Profiles at Hospital Discharge Predict Poor Outcomes After Acute Ischemic Stroke.

Objectives: To examine the relationship between medications prescribed during the first 6-months post-stroke and functional outcome. Materials and Methods: A retrospective analysis of ischemic stroke survivors enrolled in an observational stroke recovery study from June-2017 to July-2019 was performed. Survivors with favorable outcomes (modified rankin scale (mRS) score 0-2) were compared to those with unfavorable outcomes (mRS ≥3) 6-months after stroke on the following: discharge medication classes prescribed, achievement of recommended targets for blood pressure control, glycemic control, and LDL ≤70 mg/dL, medication changes, medication interactions, and medication list discrepancies. Results: Unfavorable 6-month outcomes occurred in 36/78 (46.2%) of survivors. Survivors with unfavorable outcomes were more likely to be prescribed a central nervous system-acting agent (97.2% vs 71.4%; P = .0022) and/or an anti-hyperglycemic agent (25.0% vs 9.5%; P = .009) at discharge. After adjustment of baseline covariates, total number of medications prescribed was associated with unfavorable 6-month outcomes (OR 1.13, 95% CI 1.0-1.28). Secondary stroke prevention measures were not achieved in a high proportion of survivors. Medication changes during 6-month follow up were common and survivors with unfavorable outcomes were more likely to have clinically significant drug-drug interactions. Discussion: At 6-months, survivors with unfavorable outcomes were found to be prescribed more medications, particularly central nervous system-acting and anti-hyperglycemic agents. There were also more drug-drug interactions in the medications prescribed compared to those with favorable outcomes. Together, these data suggest the need for enhanced screening of high-risk stroke survivors focused on close monitoring of polypharmacy, drug-drug interactions, and adverse events with pharmacotherapy.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.

PURPOSE: A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters. METHODS: The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. RESULTS: The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve . CONCLUSION: It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. METHODS: The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized RMS error, the concordance correlation coefficient, and the structural similarity index. RESULTS: The CD + r reconstructions, chosen using the stopping condition, yielded structural similarity indexs that were similar to the CD (p = 0.47) and LR structural similarity indexs (p = 0.95) across R and that were significantly higher than the L1 structural similarity indexs (p = 0.04). The concordance correlation coefficient values for the CD + r T1 maps across all R and subjects were greater than those corresponding to the L1 (p = 0.15) and LR (p = 0.13) T1 maps, respectively. For R ≥ 12 (≤4.2 min scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD + r. CONCLUSION: The use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.

Novel Platform for Quantitative Assessment of Functional Object Interactions After Stroke.

Many persons with stroke exhibit upper extremity motor impairments. These impairments often lead to dysfunction and affect performance in activities of daily living, where successful manipulation of objects is essential. Hence, understanding how upper extremity motor deficits manifest in functional interactions with objects is critical for rehabilitation. However, quantifying skill in these tasks has been a challenge. Traditional rehabilitation assessments require highly trained clinicians, are time-consuming, and yield subjective scores. This paper introduces a custom-designed device, the "MAGIC Table", that can record real-time kinematics of persons with stroke during interaction with objects, specifically a 'cup of coffee'. The task and its quantitative assessments were derived from previous basic-science studies. Six participants after stroke and six able-bodied participants moved a 3D-printed cup with a rolling ball inside, representing sloshing coffee, with 3 levels of difficulty. Movements were captured via a high-resolution camera above the table. Conventional kinematic metrics (movement time and smoothness) and novel kinematic metrics accounting for object interaction (risk and predictability) evaluated performance. Expectedly, persons with stroke moved more slowly and less smoothly than able-bodied participants, in both simple reaches and during transport of the cup-and-ball system. However, the more sensitive metric was mutual information, which captured the predictability of interactions, essential in cup transport as shown in previous theoretical research. Predictability sensitively measured differences in performance with increasing levels of difficulty. It also showed the best intraclass consistency, promising sensitive differentiation between different levels of impairment. This study highlights the feasibility of this new device and indicates that examining dynamic object interaction may provide valuable insights into upper extremity function after stroke useful for assessment and rehabilitation.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis.

Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P<0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. In exploratory analyses, we tested for a sensorimotor impairment and sex interaction and relationships between lesion volume, sensorimotor damage, and hippocampal volume. Greater sensorimotor impairment was significantly associated with ipsilesional (P=0.005; ß=0.16) but not contralesional (P=0.96; ß=0.003) hippocampal volume, independent of lesion volume and other covariates (P=0.001; ß=0.26). Women showed progressively worsening sensorimotor impairment with smaller ipsilesional (P=0.008; ß=-0.26) and contralesional (P=0.006; ß=-0.27) hippocampal volumes compared with men. Hippocampal volume was associated with lesion size (P<0.001; ß=-0.21) and extent of sensorimotor damage (P=0.003; ß=-0.15). Conclusions The present study identifies novel associations between chronic poststroke sensorimotor impairment and ipsilesional hippocampal volume that are not caused by lesion size and may be stronger in women.

Association of Modified Rankin Scale With Recovery Phenotypes in Patients With Upper Extremity Weakness After Stroke.

BACKGROUND AND OBJECTIVES: Precise measurement of outcomes is essential for stroke trials and clinical care. Prior research has highlighted conceptual differences between global outcome measures such as the modified Rankin Scale (mRS) and domain-specific measures (e.g., motor, sensory, language or cognitive function). This study related motor phenotypes to the mRS, specifically aiming to determine whether mRS levels distinguish motor impairment and function phenotypes, and to compare mRS outcomes to meaningful changes in impairment and function from acute to subacute recovery after stroke. METHODS: Patients with upper extremity weakness after ischemic stroke were assessed with a battery of impairment and functional measures within the first week and at 90 days after stroke. Impairment and functional outcomes were examined in relation to 90-day mRS scores. Clinically meaningful changes in motor impairment, activities of daily living, and mobility were examined in relation to 90-day mRS score. RESULTS: In this cohort of 73 patients with stroke, impairment and functional outcomes were associated with 90-day mRS scores but showed substantial variability within individual mRS levels: within mRS level 2, upper extremity impairment ranged from near hemiplegia (with an upper extremity Fugl-Meyer score 8) to no deficits (upper extremity Fugl-Meyer score 66). Overall, there were few differences in impairment and functional outcomes between adjacent mRS levels. While some outcome measures were significantly different between mRS levels 3 and 4 (Nine-Hole Peg, Leg Motor, gait velocity, Timed Up and Go, NIH Stroke Scale, and Barthel Index), none of the outcome measures differed between mRS levels 1 and 2. Fugl-Meyer and grip strength were not different between any adjacent mRS levels. A substantial number of patients experienced clinically meaningful changes in impairment and function in the first 90 days after stroke but did not achieve good mRS outcome (mRS score ≤ 2). DISCUSSION: The mRS broadly relates to domain-specific outcomes after stroke, confirming its established value in stroke trials, but it does not precisely distinguish differences in impairment and function, nor does it sufficiently capture meaningful clinical changes across impairment, activities of daily living status, and mobility. These findings underscore the potential utility of incorporating detailed phenotypic measures along with the mRS in future stroke trials.

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting.

BACKGROUND: The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. METHODS: Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. RESULTS: Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. CONCLUSIONS: Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer.

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

Cognitive Demands Influence Upper Extremity Motor Performance During Recovery From Acute Stroke.

OBJECTIVE: To test the hypothesis that cognitive demands influence motor performance during recovery from acute stroke, we tested patients with acute stroke on 2 motor tasks with different cognitive demands and related task performance to cognitive impairment and neuroanatomic injury. METHODS: We assessed the contralesional and ipsilesional upper extremities of a cohort of 50 patients with weakness after unilateral acute ischemic stroke at 3 time points with 2 tasks: the Box & Blocks Test, a task with greater cognitive demand, and Grip Strength, a simple and ballistic motor task. We compared performance on the 2 tasks, related motor performance to cognitive dysfunction, and used voxel-based lesion symptom mapping to determine neuroanatomic sites associated with motor performance. RESULTS: Consistent across contralesional and ipsilesional upper extremities and most pronounced immediately after stroke, Box & Blocks scores were significantly more impaired than Grip Strength scores. The presence of cognitive dysfunction significantly explained up to 33% of variance in Box & Blocks performance but was not associated with Grip Strength performance. While Grip Strength performance was associated with injury largely restricted to sensorimotor regions, Box & Blocks performance was associated with broad injury outside sensorimotor structures, particularly the dorsal anterior insula, a region known to be important for complex cognitive function. CONCLUSIONS: Together, these results suggest that cognitive demands influence upper extremity motor performance during recovery from acute stroke. Our findings emphasize the integrated nature of motor and cognitive systems and suggest that it is critical to consider cognitive demands during motor testing and neurorehabilitation after stroke.

Reimagining Stroke Rehabilitation and Recovery Across the Care Continuum: Results From a Design-Thinking Workshop to Identify Challenges and Propose Solutions.

Systems for stroke rehabilitation and recovery are variable and fragmented; stroke survivors often experience gaps in care with detrimental effects on their recovery. We designed and hosted a multidisciplinary and interactive workshop to discuss challenges facing patients recovering from stroke and to brainstorm solutions. Forty-one participants including clinicians, researchers, and stroke survivors attended the workshop. Participants were surveyed beforehand about challenges facing stroke recovery and results were tabulated as a word cloud. An interactive, design-thinking exercise was conducted that involved completing workbooks, hands-on prototype designing, and presentations, which were then analyzed through qualitative content analysis using an inductive approach. High frequency words in the word cloud of survey responses included access, fragmented, and uncertainty. Qualitative analysis revealed 6 major challenge themes including poor (1) transitions in and (2) access to care; (3) barriers to health insurance; (4) lack of patient support; (5) knowledge gaps; and (6) lack of standardized outcomes. Eleven unique solutions were proposed that centered around new technologies, health care system changes, and the creation of new support roles. Analysis of the alignment between the challenges and solutions revealed that the single proposed solution that solved the most identified challenges was a "comprehensive stroke clinic with follow-up programs, cutting edge treatments, patient advocation and research." Through our interactive design-thinking workshop process and inductive thematic analysis, we identified major challenges facing patients recovering from stroke, collaboratively proposed solutions, and analyzed their alignment. This process offers an innovative approach to reaching consensus among interdisciplinary stakeholders.