Quality control of quantitative diffusion-weighted magnetic resonance imaging: metrological problems

To evaluate the potential of apparent diffusion coefficients (ADCs) obtained at quantitative diffusion-weighted magnetic resonance (MR) imaging of the breast as a biomarker of low-grade ductal carcinoma in situ (DCIS). This retrospective study was approved by an institutional review board, and the requirement to obtain informed consent was waived. Twenty-two women (age range, 36-75 years; mean age, 56.4 years) with pure DCIS (seven with low-grade DCIS, five with intermediate-grade DCIS, and seven with high-grade DCIS) and three with microinvasion underwent breast MR imaging at 1.5 T between January 2008 and November 2010. MR examinations included contrast material-enhanced (gadoteridol) T1-weighted imaging and diffusion-weighted MR imaging with b values of 0 and 1000 sec/mm(2). ADC maps were generated. The distributions of the ADCs in regions of interest covering the lesions were compared among the three grades by using linear mixed-model analysis, and the discriminatory power of the lesion minimum ADC was determined with receiver operating characteristic analysis. The mean ADC was 1.42 × 10(-3) mm(2)/sec (95% confidence interval [CI]: 1.31 × 10(-3) mm(2)/sec, 1.54 × 10(-3) mm(2)/sec) for low-grade DCIS, 1.23 × 10(-3) mm(2)/sec (95% CI: 1.10 × 10(-3) mm(2)/sec, 1.36 × 10(-3) mm(2)/sec) for intermediate-grade DCIS, 1.19 × 10(-3) mm(2)/sec (95% CI: 1.08 × 10(-3) mm(2)/sec, 1.30 × 10(-3) mm(2)/sec) for high-grade DCIS, and 2.06 × 10(-3) mm(2)/sec (95% CI: 1.94 × 10(-3) mm(2)/sec, 2.18 × 10(-3) mm(2)/sec) for normal breast tissue. The mean ADCs for high- and intermediate-grade DCIS were significantly lower than that for low-grade DCIS (P < .01 and P = .03, respectively), and the mean ADC for low-grade DCIS was significantly lower than that for normal tissue (P < .001). The lesion minimum ADC for low-grade DCIS was also significantly higher than that for high- and intermediate-grade DCIS (P < .01). A threshold of 1.30 × 10(-3) mm(2)/sec for the minimum ADC in the diagnosis of low-grade DCIS had a specificity of 100% (12 of 12 patients; 95% CI: 73.5%, 100%) and a positive predictive value of 100% (four of four patients; 95% CI: 39.8%, 100%). These preliminary results suggest that quantitative diffusion-weighted MR imaging could be used to identify patients with low-grade DCIS with very high specificity. If the results of this study are confirmed, this approach could potentially spare those patients from invasive approaches such as mastectomy or axillary lymph node excision.

Continued improvements in diagnostic accuracy using magnetic resonance (MR) imaging will require development of methods for tissue analysis that complement traditional qualitative MR imaging studies. Quantitative MR imaging is based on measurement and interpretation of tissue-specific parameters independent of experimental design, compared with qualitative MR imaging, which relies on interpretation of tissue contrast that results from experimental pulse sequence parameters. Quantitative MR imaging represents a natural next step in the evolution of MR imaging practice, since quantitative MR imaging data can be acquired using currently available qualitative imaging pulse sequences without modifications to imaging equipment. The article presents a review of the basic physical concepts used in MR imaging and how quantitative MR imaging is distinct from qualitative MR imaging. Subsequently, the article reviews the hierarchical organization of major applicable pulse sequences used in this article, with the sequences organized into conventional, hybrid, and multispectral sequences capable of calculating the main tissue parameters of T1, T2, and proton density. While this new concept offers the potential for improved diagnostic accuracy and workflow, awareness of this extension to qualitative imaging is generally low. This article reviews the basic physical concepts in MR imaging, describes commonly measured tissue parameters in quantitative MR imaging, and presents the major available pulse sequences used for quantitative MR imaging, with a focus on the hierarchical organization of these sequences. ©RSNA, 2017.

Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitative magnetic resonance imaging (MRI) data acquired early in the course of NAT into a mechanism-based, biophysical model that predicts the eventual treatment response of breast tumors. Being able to predict which patient will respond effectively to NAT would have a fundamental and lasting impact on healthcare. However, the ultimate goal is to optimize therapy given the unique characteristics of each patient. Here we show that the detailed combination of advanced image analysis and rigorous mathematical modeling can accurately predict response for the individual patient. Further, we use the model to demonstrate the potential selection of personalized therapeutic regimens. This is accomplished by initializing the mathematical model with patient-specific characteristics and then varying, in silico, a range of treatment plans to achieve the greatest tumor control. Methods: Quantitative MRI was acquired from breast cancer patients (N = 11) at three time points during the course of NAT: 1) prior to NAT, 2) after 1 cycle of their initial chemotherapy, and 3) after the completion of the initial chemotherapy regimen. With these data, we implemented our recently established mechanically coupled, reaction-diffusion model at the tissue scale for predicting breast tumor response to therapy. The 3D model is initialized with patient-specific, diffusion-weighted MRI data characterizing tumor cellularity. Additionally, the model includes a tumor cell reduction term for local drug delivery as estimated from pharmacokinetic analysis of dynamic contrast-enhanced MRI data and population-derived plasma curves of therapeutic concentrations. The model’s predictive ability was assessed using three different measures. Using the first two scans, the model is calibrated and simulated forward to the third scan time to compare the predicted total tumor cellularity, volume, and longest axis to the actual values measured from the patient’s third scan. We then simulate alternate regimens using the same total dose each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Results: After calibrating the model using the first two imaging time points, the model’s predictions are significantly correlated to the measured tumor burden at scan three with Pearson Correlation Coefficients of 0.93, 0.89, 0.96 (p &amp;lt; 0.01) for total cellularity, total volume, and longest axis, respectively. The model predicts that for the alternative dosing regimens assessed, individual patients could have achieved an additional 0-43% reduction in total cellularity compared to the therapeutic regimens patients actually received. This indicates that standard regimens may not be the most effective for every patient. Discussion and future directions: These results demonstrate that the mathematical model can be predictive of tumor response using data at the earliest times of therapy regimens. The in silico results illustrate how for individual patients (depending on their unique tumor characteristics and vasculature—captured by the calibrated parameters of the model), therapy regimens can be tailored and even optimized (via established optimal control theory methods) to each patient using a mathematical model and simulation studies. The present investigation represents a significant first step towards personalizing patient regimens through quantitative imaging and mathematical modeling. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE Citation Format: Angela M Jarrett, David A Hormuth II, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-16-17.

Quantitative diffusion-weighted magnetic resonance imaging in the evaluation of parotid gland masses: a study with histopathological correlation

Bladder Cancer: New Insights Into Dose, Volume and Prognostic Biomarkers

Background: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accurately predict the ultimate pathological response. While these techniques have been incorporated into clinical trials in the academic setting, they have not yet been translated to the community setting, where most cancer patients receive their care. Implementation of quantitative MRI in the community setting widely expands the potential impact it can provide by 1) allowing community settings to participate in clinical trials that require quantitative MRI, and 2) advancing quantitative MRI towards standard-of-care for prediction of response in breast cancer. Methods: Women with locally advanced breast cancer (N = 28) were imaged four times during the course of NAT: 1) prior to the start of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after MRI #3. Imaging data was acquired on 3T Siemens Skyra scanners equipped with breast coils and sited in a community hospital and radiology clinic, respectively. DW-MRI and DCE-MRI were acquired over 10 slices of 5 mm thickness. DW-MRI was acquired with diagonal monopolar diffusion-encoding gradients with b-values of 0, 200, and 800 s/mm2 in a total scan time of 1 minute 39 seconds. Voxel wise tumor cellularity was quantified using the apparent diffusion coefficient (ADC). For DCE-MRI, a gadolinium-based contrast agent was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was acquired dynamically with a temporal resolution of 7.27 seconds for a total acquisition time of 8 minutes. The volume transfer constant Ktrans was calculated using Patlak analysis of DCE-MRI data to characterize the tumor vasculature. The tumor was semi-automatically segmented using a manually drawn region of interest followed by fuzzy c-means clustering of DCE-MRI data to identify a functional tumor volume. Measurements of tumor volume were combined with both ADC and Ktrans to yield metrics of tumor cellularity and bulk tumor flow, respectively. Results: Women who achieved pathological complete response at the time of surgery (pCR; n=8) displayed significantly different treatment-induced changes in MRI-derived tumor parameters versus women who did not achieve pCR (non-pCR, n=20). After 1 cycle of NAT, women who achieved pCR had smaller functional tumor volume and lower cellularity (p &amp;lt; 0.05) than non-pCR study participants. At the third and fourth MRI, tumor volume, ADC, Ktrans, cellularity, and bulk tumor flow were all significantly different between the pCR and non-pCR cohorts (p &amp;lt; 0.05). Of note, longest tumor diameter was not predictive of pCR at any time point in this study. Conclusions: This study demonstrates that quantitative DCE- and DW-MRI can be implemented successfully in community care facilities within standard-of-care settings for imaging locally advanced breast cancer. Metrics extracted from the change in DW-MRI (ADC) and DCE-MRI (Ktrans) can accurately predict pathological complete response to neoadjuvant therapy and may be more sensitive to tumor response than the RECIST criteria. Furthermore, incorporating quantitative metrics with tumor volume further increases the ability to predict pathological response to NAT in locally advanced breast cancer. While there are still challenges to address to effectively implement these quantitative metrics into the clinical workflow, this study is first in its kind to transition a decade’s worth of quantitative MRI advancements from academic settings into standard-of-care. Citation Format: John Virostko, Anna G Sorace, Kalina P Slavkova, Anum S Kazerouni, Angela M Jarrett, Julie C DiCarlo, Stefanie Woodard, Sarah Avery, Boone W Goodgame, Debra Patt, Thomas E Yankeelov. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-03.

Purpose: Quantification of accurate and early response to neoadjuvant therapy (NAT) provides the opportunity to replace an ineffective treatment with an alternative regimen, thereby potentially avoiding ineffective systemic therapy. Quantitative magnetic resonance imaging (MRI) has been shown to predict breast cancer response to treatment early during the course of NAT within academic medical centers. Integrating quantitative imaging techniques into community-based medical practices has the potential to reach a larger percentage of breast cancer patients, and allow more community center participation in clinical trials that require quantitative imaging. This study evaluated the reproducibility and accuracy of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) in the community setting and the ability to implement these techniques to predict the eventual response of breast tumors to NAT. Experiment Procedure: MRI was performed at two community imaging centers and one academic research facility using a 3T Siemens Skyra scanners equipped with a breast coil. To assess reproducibility across sites, normal subjects (N=3) were scanned at three imaging centers. Quantitative T1 (required for pharmacokinetic modeling of DCE-MRI) and apparent diffusion coefficient (ADC) maps were calculated of normal fibroglandular breast tissue. Accuracy was tested through evaluation of T1 and DW-MRI in phantoms. Women undergoing NAT for breast cancer (N=10) were scanned with DCE-MRI and DW-MRI at baseline (prior to beginning therapy) and three longitudinal time points during the course of NAT to evaluate early prediction of response to therapy. Quantitative measures of ADC (evaluating cellularity from DW-MRI) and Ktrans (evaluating vascular perfusion and permeability from DCE-MRI) were calculated for the segmented tumor volume. Imaging was compared to pathology reports at the conclusion of NAT. Results: Reproducibility scans of normal breast fibroglandular tissue yielded an average difference of 8.4% and 7.0% in T1 and ADC measurements, respectively, across sites. Phantom studies revealed accurate measurements of T1 mapping and ADC, with reproducibility measurements showing a difference of 2.8% and 1.6%, respectively. Patients achieving a pathological complete response (pCR) revealed a 13.8% ± 19.0% increase in the mean ADC values of the tumor from t1 (baseline, prior to beginning NAT) and t2 (following one round of NAT) and a 15.4% ± 40.9% decrease in mean Ktrans. Conversely, patients that did not achieve non-pCR had little change in ADC (-0.9% ± 12.6% change between t1 and t2) and a 15.3% ± 42.8% increase in Ktrans. Conclusions: Quantitative MRI has been shown to be accurate and reproducible across community medical centers. Furthermore, the preliminary results discussed above parallel those previously reported in the academic research setting. Citation Format: Anna Sorace, Jack Virostko, Chengyue Wu, Angela M. Jarrett, Stephanie L. Barnes, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Quantitative MRI during neoadjuvant therapy for predicting breast cancer response in the community setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3043.

Background/Aims:The development of infection in pancreatitis increases the mortality rate up to 32%. Therefore, it is important to identify patients who are at high risk of developing infection, at an early stage. The objectives of the study were (a) to analyze the quantitative parameters of diffusion-weighted magnetic resonance imaging (DW-MRI) and apparent diffusion coefficient (ADC) in infected as well as sterile pancreatic collections (b) to establish “cut-off” values for ADC that can identify infected pancreatic collections.Materials and Methods:Prospective observational study of pancreatitis cases who underwent DW-MRI from August 2018 to July 2019 were enrolled in the study. The collections were analyzed for diffusion restriction. The average of the three ADC values from the wall and center of collection was noted.Results:Infected collections were seen in 7 and sterile collections observed in 11 cases. The optimal cut-off ADC value to differentiate sterile and infected collection in our study was 1.651 × 10-3 mm2/s (sensitivity of 81.8%; specificity of 100.0%). ROC curve for mean ADC from the wall showed a significant diagnostic accuracy with AUC: 0.91; 95% CI: 0.77-1.0 (P = 0.004).Conclusion:DW-MRI is a reliable noninvasive technique to differentiate sterile and infected pancreatic collections. ADC values from the periphery of the collection can predict infected pancreatic collections at an early stage. DW-MRI should not be considered as a substitute for aspiration cytology in patients with septic symptoms and absent diffusion restriction on MRI.

Quantitative Assessment of Rectal Cancer Response to Neoadjuvant Combined Chemotherapy and Radiation Therapy: Comparison of Three Methods of Positioning Region of Interest for ADC Measurements at Diffusion-weighted MR Imaging.

IntroductionQuantitative magnetic resonance imaging (MRI) analysis is currently used in multiple sclerosis (MS) clinical trials. Quantitative MRI (QMRI) data derived using formal analysis techniques is not used in routine MS...

Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data

Determining mechanisms of secondary stroke injury related to cerebral blood flow and the severity of microvascular injury contributing to edema and blood-brain barrier breakdown will be critical for the development of adjuvant therapies for revascularization treatment. To characterize the heterogeneity of the ischemic lesion using quantitative T2 imaging along with diffusion-weighted magnetic resonance imaging (DWI) within five hours of treatment. Quantitative T2 magnetic resonance imaging was acquired within 5 h (baseline) and at 24 h (follow-up) of stroke treatment in 29 patients. Dynamic contrast enhanced permeability imaging was performed at baseline in a subgroup of patients. Absolute volume change and lesion percent change was determined for the quantitative T2, DWI, and absolute volume change sequences. A Gaussian process with RRELIEFF feature selection algorithm was used for prediction of relative quantitative T2 and DWI lesion growth, baseline and follow-up quantitative T2/DWI lesion ratios, and also NIHSS at 24 h and change in NIHSS from admission to 24 h. In n = 27 patients, median (interquartile range) lesion percent change was 114.8% (48.9%, 259.1%) for quantitative T2, 48.2% (-12.6%, 179.6%) for absolute volume change, and 62.7% (26.3%, 230.9%) for DWI, respectively. Our model, consisting of baseline NIHSS, CT ASPECTS, and systolic blood pressure, showed a strong correlation with quantitative T2 percent change (cross correlation R2 = 0.80). There was a strong predictive ability for quantitative T2/DWI lesion ratio at 24 h using baseline NIHSS and last seen normal to 24 h magnetic resonance imaging time (cross correlation R2 = 0.93). Baseline dynamic contrast enhanced permeability was moderately correlated to the baseline quantitative T2 values (rho = 0.38). Quantitative T2 imaging provides critical information for development of therapeutic approaches that could ameliorate microvascular damage during ischemia reperfusion.

Preoperative imaging of salivary gland tumors is important for predicting and differentiating benign from malignant tumors, and for aiding management planning. We aimed to investigate the accuracy of combined quantitative diffusion-weighted magnetic resonance imaging (MRI) and routine contrast-enhanced MRI in the evaluation of salivary gland tumors and the differentiation of benign from malignant tumors. This study included 51 patients with a total of 16 benign and 35 malignant lesions that were detected by histopathological analysis. There was a statistically significant difference between the apparent diffusion coefficient values (ADC) of malignant and benign lesions (0.69 ± 0.22 × 10-3 mm2/s and 1.39 ± 0.52 × 10-3 mm2/s respectively). The optimal cut-off ADC value was 1.08 with 75% specificity and 97% sensitivity. The routine contrast-enhanced MRI had predicted benign and malignant tumors with 65% sensitivity and 44% specificity. The sensitivity and specificity were greatly increased when quantitative diffusion-weighted MRI was combined with routine contrast-enhanced MRI: 100%, and 88% respectively. A receiver operating curve was generated. The area under curve was 0.88 (p < 0.001, 95% CI: 0.76-0.99). Combined quantitative diffusion-weighted MRI with ADC measurements and routine contrast-enhanced magnetic resonance imaging are helpful tools for the evaluation of salivary gland tumors and help differentiate benign from malignant lesions.

To identify myocardial fibrosis in hypertrophic cardiomyopathy (HCM) subjects using quantitative cardiac diffusion-weighted imaging (DWI) and to compare its performance with native T1 mapping and extracellular volume (ECV). Thirty-eight HCM subjects (mean age, 53 ± 9years) and 14 normal controls (mean age, 51 ± 8years) underwent cardiac magnetic resonance imaging (CMRI) on a 3.0T magnetic resonance (MR) machine with DWI, T1 mapping and late gadolinium enhancement (LGE) imaging as the reference standard. The mean apparent diffusion coefficient (ADC), native T1 value and ECV were determined for each subject. Overall, the HCM subjects exhibited an increased native T1 value (1241.04 ± 78.50ms), ECV (0.31 ± 0.03) and ADC (2.36 ± 0.34s/mm(2)) compared with the normal controls (1114.60 ± 37.99ms, 0.24 ± 0.04, and 1.62 ± 0.38s/mm(2), respectively) (p < 0.05). DWI differentiated healthy and fibrotic myocardia with an area under the curve (AUC) of 0.93, while the AUCs of the native T1 values (0.93), (p > 0.05) and ECV (0.94), (p > 0.05) exhibited an equal differentiation ability. Both HCM LGE+ and HCM LGE- subjects had an increased native T1 value, ECV and ADC compared to the normal controls (p < 0.05). HCM LGE+ subjects exhibited an increased ECV (0.31 ± 0.04) and ADC (2.43 ± 0.36s/mm(2)) compared to HCM LGE- subjects (p < 0.05). HCM LGE+ and HCM LGE- subjects had similar native T1 values (1250 ± 76.36ms vs. 1213.98 ± 92.30ms, respectively) (p > 0.05). ADC values were linearly associated with increased ECV (R(2) = 0.36) and native T1 values (R(2) = 0.40) among all subjects. DWI is a feasible alternative to native T1 mapping and ECV for the identification of myocardial fibrosis in patients with HCM. DWI and ECV can quantitatively characterize the extent of fibrosis in HCM LGE+ and HCM LGE- patients.

The added value of qualitative and quantitative diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating benign from malignant breast lesions