DCE MRI Research Articles

Evaluation of blood-brain barrier integrity by the analysis of dynamic contrast-enhanced MRI - a comparison of quantitative and semi-quantitative methods.

Disruption of the blood-brain barrier (BBB) is a key feature of various brain disorders. To assess its integrity a parametrization of dynamic magnetic resonance imaging (DCE MRI) with a contrast agent (CA) is broadly used. Parametrization can be done quantitatively or semi-quantitatively. Quantitative methods directly describe BBB permeability but exhibit several drawbacks such as high computation demands, reproducibility issues, or low robustness. Semi-quantitative methods are fast to compute, simply mathematically described, and robust, however, they do not describe the status of BBB directly but only as a variation of CA concentration in measured tissue. Our goal was to elucidate differences between five semi-quantitative parameters: maximal intensity (Imax), normalized permeability index (NPI), and difference in DCE values between three timepoints: baseline, 5 min, and 15 min (delta5-0, delta15-0, delta15-5) and two quantitative parameters: transfer constant (Ktrans) and an extravascular fraction (Ve). For the purpose of comparison, we analyzed DCE data of four patients 12-15 days after the stroke with visible CA enhancement. Calculated parameters showed abnormalities spatially corresponding with the ischemic lesion, however, findings in individual parameters morphometrically differed. Ktrans and Ve were highly correlated. Delta5-0 and delta15-0 were prominent in regions with rapid CA enhancement and highly correlated with Ktrans. Abnormalities in delta15-5 and NPI were more homogenous with less variable values, smoother borders, and less detail than Ktrans. Moreover, only delta15-5 and NPI were able to distinguish vessels from extravascular space. Our comparison provides important knowledge for understanding and interpreting parameters derived from DCE MRI by both quantitative and semi-quantitative methods.

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

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. 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. 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. 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 . 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.

“COMPARATIVE EVALUATION OF X RAY MAMMOGRAPHY WITH DYNAMIC CONTRAST MAGNETIC RESONANCE MAMMOGRAPHY IN FOCAL BREAST LESIONS”

Evaluation of breast cancer has undergone a significant change in patients with advent of X ray Mammography, USG, DCE MRI, scintigraphy, PET which helps in early detection and intervention. X ray mammogram serves as the best screening tool. DCE MRI useful in dense breast, inconclusive mammogram in high risk patients and as screening tool BRCA 1, BRCA 2, Cowdens syndrome patients. The main of DCEMRI is distinguish benign and malignant lesions to prevent unnecessary invasive procedures and to conserve breast in breast carcinomas

Breast MRI: does a clinical decision algorithm outweigh reader experience?

ObjectivesDue to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS.MethodsThree off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves.ResultsA total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05).ConclusionThe KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience.Key Points• Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting.• The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance.

Abstract 749: SimBioSys TumorScope accurately models and predicts response to neoadjuvant therapy in breast cancer - Validation study

Abstract Neoadjuvant Therapy (NAT) for breast cancer results in higher rates of breast conservation surgery. Importantly, there is data supporting the use of multiple NAT regimens, with little guidance as to which one would be most effective for a given patient. Thus, tools that can accurately predict patient outcome before cancer treatment can provide guidance during treatment planning and increase the chances of therapy success. Here we present the first biophysical modelling platform, TumorScope (TS), capable of constructing a 3D model of the tumor microenvironment by integrating models for tumor morphology, metabolism, drug delivery and behavior, and vascularity. Thereby allowing visualization of response to multiple NATs for a given tumor. TS integrates patient data to create an in-silico tumor model capturing the tumor response trajectory, volume, dimensions, pathological complete response (pCR), residual cancer burden and tumor morphology, and risk of recurrence (ROR). To illustrate the clinical applicability and to validate TS we performed a single center validation study. Previously acquired pre-treatment standard of care (SOC) diagnostic data (demographics, drug regimen, imaging (DCE MRI), and pathology) was used as TS inputs. Using this baseline data only, TS predicted weekly volumetric response through the treatment time and used simulated residual volume to predict pCR and assessed the ROR. The validation was performed using ground truth from post treatment assessment of pCR, radiographic volumes extracted from MRIs and ROR information. A chart review was performed to identify consecutive patients who had received NAT and had a corresponding pre-treatment MRI at University of Cincinnati. A validation set was generated from this list and data processed through TS (n=81, after excluding 9 patients with poor quality DCE-MRIs). TS predicted pCR was predefined as a residual tumor volume < 0.01 cm3 or a 99.9% or greater reduction in tumor volume. Performance metrics of TS were calculated. TS response (pCR vs. residual) prediction accuracy had an area under the curve (AUC) =0.912 with sensitivity and specificity of 88.9% and 92.5%, respectively. The performance was robust across all subtypes. Using SOC diagnostic data TS predicted the reduction in tumor volume with a median absolute volumetric error of 3.4% vs radiographic volume from pre-surgery DCE MRIs (n=59). TS prognostic ROR (High vs Low) had a hazard ratio [HR] 12.705, p=0.01 which was comparable as a prognostic indicator to actual pCR which had a [HR] 5.81, p=0.08. In summary, TS accurately predicts patient-specific response to NAT in early breast cancer using only baseline SOC pre-treatment data. This approach to a comprehensive view of the tumor represents a major step-forward in personalized clinical decision making. The results from this study will be used in a multi-center meta-analysis of TS performance characteristics. Citation Format: Gregory H. Norris, John Pfeiffer, Yuhan Zhang, Hope R. Esslinger, The SimBioSys Team, Vinita Takiar. SimBioSys TumorScope accurately models and predicts response to neoadjuvant therapy in breast cancer - Validation study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 749.

Visual Evaluation of Ultrafast MRI in the Assessment of Residual Breast Cancer after Neoadjuvant Systemic Therapy: A Preliminary Study Association with Subtype.

The purpose of this study was to investigate the diagnostic performance of ultrafast DCE (UF-DCE) MRI after the completion of neoadjuvant systemic therapy (NST) in breast cancer. In this study, MR examinations of 55 post-NST breast cancers were retrospectively analyzed. Residual tumor sizes were measured in the 20th phase of UF-DCE MRI, early and delayed phases of conventional DCE MRI, and high spatial-resolution CE MRI (UF, early, delayed, and HR, respectively). The diagnostic performance for the detection of residual invasive cancer was calculated by ROC analysis. The size difference between MRI and pathological findings was analyzed using the Wilcoxon signed-rank test with the Bonferroni correction. The overall AUC was highest for UF (0.86 and 0.88 for readers 1 and 2, respectively). The difference in imaging and pathological sizes for UF (5.7 ± 8.2 mm) was significantly smaller than those for early, delayed, and HR (p < 0.01). For luminal subtype breast cancer, the size difference was significantly smaller for UF and early than for delayed (p < 0.01). UF-DCE MRI demonstrated higher AUC and specificity for the more accurate detection of residual cancer and the visualization of tumor extent than conventional DCE MRI.

Prediction of response to neoadjuvant therapy (NAT) in early breast cancer (EBC) at community hospitals: SimBioSys TumorScope Validation Study.

598 Background: While most cancer patients are treated at community hospitals, specialized equipment, diagnostic tests, and therapeutic regimens are generally validated at academic settings. Here we present a novel approach to precision medicine to improve outcomes in breast cancer care in the community setting. TumorScope (TS) is a biophysical modelling platform that uses only pretreatment standard of care (SoC) diagnostic data (demographics, drug regimen, imaging (DCE MRI) &amp; pathology) to construct a 3D model of the tumor. TS integrates models for tumor morphology, metabolism, vascularity, and drug behavior and simulates predicted tumor response longitudinally to NAT. With this information TS predicts the reduction in tumor volume and the pathological complete response (pCR), a surrogate marker of long-term outcome, to anticipated NAT in EBC. Methods: We performed a single center validation study to show the clinical applicability of TS. Patients at Northwest Community Healthcare that received NAT with corresponding pretreatment MRI were identified in a chart review. A validation set, independent from the training set, was generated from this list and data processed through TS (n=50). Pretreatment patient SoC diagnostic data was loaded into TS. TS predicted the weekly volumetric response throughout the treatment, and it simulated residual volume to predict pCR. The validation was performed using ground truth from post treatment assessment of pCR, radiographic volumes extracted from MRIs. Results: TS predicted pCR, prospectively defined as a simulated residual tumor volume &lt;0.01 cm3 or a 99.9% or greater reduction in tumor volume. Performance metrics of TS were calculated. TS tumor volume prediction accuracy had an area under the curve (AUC)=0.947 with sensitivity and specificity of 93.3% and 94.3% respectively. Performance was robust across all subtypes (See Table 1). TS predicted the reduction in tumor volume with a median absolute volumetric error of 3.4% as compared to radiographic volume from pre-surgery MRIs (n=50). Conclusions: In summary, TS accurately predicts patient-specific tumor volume reduction and pCR prior to NAT in EBC using only SoC pretreatment data, which could lead to a more personalized cancer care for the patient. Moreover, we demonstrate the feasibility of implementing TS at community hospitals. [Table: see text]

Normal pituitary and microadenoma T1 signal measurements

Background: The most often used technique for detecting pituitary microadenoma is dynamic contrast enhanced magnetic resonance imaging (DCE MRI). However, the high rate of false positivity in its interpretation is a problem due to differential amplification of the normal pituitary. Purpose: The purpose of this study was to see if the precontrast T1 signal intensity ratio (SIR) of lesions identified on DCE MRI could be used to predict the existence of microadenoma. Materials and Methods: We looked at the MRIs of 23 patients who were referred for DCE MRI of the pituitary gland (group 1, 15 patients with diagnosis of pituitary microadenoma; and group 2, patients not clinically labelled as microadenoma). STC were plotted, and T1 SIR was acquired at t = 0 s at the suspect zone of differential augmentation (SIR T) and normal pituitary (SIR P). For each patient, the SIR difference (SIR P SIR T) and relative SIR difference (SIR P SIR T/SIR P) were calculated and compared between the two groups. Results: Patients with microadenoma have a lower mean T1 SIR than those without (P = 0.065). Patients with microadenoma had a greater SIR difference and relative SIR difference (P = 0.003 and 0.005, respectively).

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors.

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.

Reproducibility of dynamic contrast enhanced MRI derived transfer coefficient Ktrans in lung cancer

Dynamic contrast enhanced MRI (DCE-MRI) is a useful method to monitor therapy assessment in malignancies but must be reliable and comparable for successful clinical use. The aim of this study was to evaluate the inter- and intrarater reproducibility of DCE-MRI in lung cancer. At this IRB approved single centre study 40 patients with lung cancer underwent up to 5 sequential DCE-MRI examinations. DCE-MRI were performed using a 3.0T system. The volume transfer constant Ktrans was assessed by three readers using the two-compartment Tofts model. Inter- and intrarater reliability and agreement was calculated by wCV, ICC and their 95% confident intervals. DCE-MRI allowed a quantitative measurement of Ktrans in 107 tumors where 91 were primary carcinomas or intrapulmonary metastases and 16 were extrapulmonary metastases. Ktrans showed moderate to good interrater reliability in overall measurements (ICC 0.716-0.841; wCV 30.3-38.4%). Ktrans in pulmonary lesions ≥ 3 cm showed a good to excellent reliability (ICC 0.773-0.907; wCV 23.0-29.4%) compared to pulmonary lesions < 3 cm showing a moderate to good reliability (ICC 0.710-0.889; wCV 31.6-48.7%). Ktrans in intrapulmonary lesions showed a good reliability (ICC 0.761-0.873; wCV 28.9-37.5%) compared to extrapulmonary lesions with a poor to moderate reliability (ICC 0.018-0.680; wCV 28.1-51.8%). The overall intrarater agreement was moderate to good (ICC 0.607-0.795; wCV 24.6-30.4%). With Ktrans, DCE MRI offers a reliable quantitative biomarker for early non-invasive therapy assessment in lung cancer patients, but with a coefficient of variation of up to 48.7% in smaller lung lesions.

Abstract P1-08-03: Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers

Abstract Introduction: Neoadjuvant chemotherapy (NACT) is becoming standard of care for presurgical treatment of triple negative breast cancer (TNBC) patients. Achievement of pathological complete response (pCR) after NACT is associated with improved outcomes. There is currently an unmet need in development of imaging and clinical tools for prediction of pCR to NACT in TNBC. We investigated use of deep learning convolution neural networks (CNNs) for early prediction of pCR in a TNBC cohort on the basis of MRI acquired before the initiation and at the midpoint, after completion of four cycles of NACT (C4). Materials and Methods: Baseline and C4 MRIs of 112 TNBC patients were collected from an ongoing prospective clinical trial (NCT02276443). Four patients were excluded because they underwent different treatment for the second regimen. Among the 108 patients, 52 patients (48%) had pCR confirmed at surgery. Positive enhancement integral (PEI) derived from the early phases of DCE MRI, and apparent diffusion coefficients (ADC) derived from DWI MRI (b = 100 and 800 s/mm2), were used for our investigation. The images were aligned and the tumor regions were cropped from all images. All tumor patches were normalized between [0, 1], and were padded to form matrices of the same size of 192×192×64 for PEI, or the size of 192×192×16 for ADC. The CNN was constructed using stacked 3D convolution and MaxPooling layers. It consisted of up to four channels for the inputs (baseline and C4 PEI and ADC). Features extracted from each channel were concatenated and regressed for pCR prediction via three densely connected layers. Binary cross-entropy was used as the loss function for CNN training, and the loss was optimized using an Adam optimizer with the initial learning rate of 0.0001. Because of the currently limited sample size, four-fold cross-validation was used for CNN training and evaluation. The patients were divided into four groups, each group had 27 patients and the pCR:non-pCR ratio was controlled as 13:14. For each fold, one group was reserved as the independent testing group, and the other three groups were combined for network training and internal validation. Receiver operating characteristic (ROC) curve was plotted for each fold of testing, and area under the curve (AUC) was calculated. Final performance of the CNN was determined by averaging the AUCs of the four testing folds. Additionally, to test the prediction efficacy of each input, we trained the CNN under the same settings but used PEI or ADC only as input, and the results were compared. Results: The CNN trained with PEI only achieved an average AUC of 0.65 ± 0.09. The second CNN trained with ADC only achieved an average AUC of 0.72 ± 0.07. The third CNN trained with both PEI and ADC achieved an average AUC of 0.73 ± 0.06. Conclusion and Discussion: Using baseline and mid-treatment MRIs, deep learning CNN showed promising performance to predict pCR in the early course of NACT. The prediction AUC for the independent testing groups was largely improved by using ADC to train the network, indicating that ADC can have more critical information than PEI in assisting pCR prediction during the early course of NACT. Future work includes curation of a larger patient data for network training and evaluation to improve the prediction performance and further validate generalization of the network. We will also explore more advanced network structures, through which the prediction performance can be improved. Four-fold cross-validation AUCs of the network using different data as inputs.PEIADCPEI+ADCFold 10.570.640.66Fold 20.760.800.77Fold 30.660.700.68Fold 40.590.740.79Average0.65 ± 0.090.72 ± 0.070.73 ± 0.06 Citation Format: Zijian Zhou, Nabil A Elshafeey, David E Rauch, Beatriz E Adrada, Rosalind P Candelaria, Mary S Guirguis, Wei Yang, Medine Boge, Rania M Mohamed, Gary J Whitman, Deanna L Lane, Huong C Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral M Patel, Frances Perez, Debu Tripathy, Vicente Valero, Clinton Yam, Stacy Moulder, Jason B White, Jong Bum Son, Mark D Pagel, Jingfei Ma. Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers [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 P1-08-03.

Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

BackgroundTo prospectively explore the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters of sacroiliitis in patients with axial spondyloarthritis (axSpA).MethodsPatients with initially diagnosed axSpA prospectively underwent on 3.0 T MRI of sacroiliac joint (SIJ). The IVIM parameters (D, f, D*) were calculated using biexponential analysis. Ktrans, Kep, Ve, and Vp from DCE-MRI were obtained in SIJ. The uni-variable and multi-variable linear regression analyses were used to evaluate the correlation between the parameters from these two imaging methods after controlling confounders, such as bone marrow edema (BME), age, agenda, scopes, and localization of lesions, and course of the disease. Then, their correlations were measured by calculating the Pearson's correlation coefficient (r).ResultsThe study eventually enrolled 234 patients (178 men, 56 women; mean age, 28.51 ± 9.50 years) with axSpA. With controlling confounders, D was independently related to Ktrans (regression coefficient [b] = 27.593, p < 0.001), Kep (b = −6.707, p = 0.021), and Ve (b = 131.074, p = 0.003), whereas f and D* had no independent correlation with the parameters from DCE MRI. The correlations above were exhibited with Pearson's correlation coefficients (r) (r = 0.662, −0.408, and 0.396, respectively, all p < 0.001).ConclusionThere were independent correlations between D derived from IVIM DWI and Ktrans, Kep, and Ve derived from DCE-MRI. The factors which affect their correlations mainly included BME, gender, and scopes of lesions.

Value of diffusion-weighted MRI in predicting early response to neoadjuvant chemotherapy of breast cancer: comparison between ROI-ADC and whole-lesion-ADC measurements.

The aim of the study was to assess DWI with ROI-ADC and WL-ADC measurements in early response after NAC in breast cancer. Between January 2016 and December 2019, 55 women were enrolled in this prospective single-center study. MRI was performed at three time points for each patient: before treatment (MRI 1: DW and DCE MRI), after one cycle of NAC (MRI 2: noncontrast DW MRI), and after completion of NAC before surgery (MRI 3: DW and DCE MRI). ROI-ADC and WL-ADC measurements were obtained on MRI and were compared to histology findings and to the RCB class. Patients were categorized as having pCR or non-pCR. Among 48 patients, 9 experienced pCR. An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 88.9% and a specificity of 63.4% in predicting pCR, whereas WL-ADC did not predict pCR. An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 83.3% and a specificity of 64.9% in predicting radiologic complete response. An increase of WL-ADC between MRI 1 and 2 of more than 25.5% had a sensitivity of 83.3% and a specificity of 75.5% in predicting radiologic complete response. After one cycle of NAC, a significant increase in breast tumor ROI-ADC at DWI predicted complete pathologic and radiologic responses. • An increase of WL-ADC between MRI 1 and 2 of more than 25.5% had a sensitivity of 83.3% and a specificity of 75.5% in predicting radiologic complete response. • An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 88.9% and a specificity of 63.4% in predicting pCR, and a sensitivity of 83.3% and a specificity of 64.9% in predicting radiologic complete response. • A significant increase in breast tumor ROI-ADC at DWI predicted complete pathologic and radiologic responses.

Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis.

To provide data regarding relationships between quantitative dynamic contrast enhanced magnetic resonance imaging (DCE MRI) and prognostic factors in breast cancer (BC). Data from 4 Centers (200 female patients, mean age, 51.2±11.5 years) were acquired. The following data were collected: histopathological diagnosis, tumor grade, stage, hormone receptor status, KI 67, and DCE MRI values including Ktrans (volume transfer constant), Ve (volume of the extravascular extracellular leakage space (EES) and Kep (diffusion of contrast medium from the EES back to the plasma). DCE MRI values between different groups were compared using the Mann-Whitney U-test and by the Kruskal-Wallis H test. The association between DCE MRI and Ki 67 values was calculated by the Spearman's rank correlation coefficient. DCE MRI values of different tumor subtypes overlapped significantly. There were no statistically significant differences of DCE MRI values between different tumor grades. All DCE MRI parameters correlated with KI-67: Ktrans, r=0.44, p=0.0001; Ve, r=0.34, p=0.0001; Kep, r=0.28, p=0.002. ROC analysis identified a Ktrans threshold of 0.3 min-1 for discrimination of tumors with low KI-67 expression (<25%) and high KI-67 expression (≥25%): sensitivity, 75.5%, specificity, 73.0%, accuracy, 74.0%, AUC, 0.78. DCE MRI values overlapped between tumors with different T and N stages. Ktrans, Kep, and Ve cannot be used as reliable a surrogate marker for hormone receptor status, tumor stage and grade in BC. Ktrans may discriminate lesions with high and lower proliferation activity.

Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm3; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm3; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (Ktrans, Vp, and Ve). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95th percentile Ktrans and Ve from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

Dynamic contrast-enhanced CEST MRI using a low molecular weight dextran.

Natural and synthetic sugars have great potential for developing highly biocompatible and translatable chemical exchange saturation transfer (CEST) MRI contrast agents. In this study, we aimed to develop the smallest clinically available form of dextran, Dex1 (molecular weight, MW ~ 1 kDa), as a new CEST agent. We first characterized the CEST properties of Dex1 in vitro at 11.7 T and showed that the Dex1 had a detectable CEST signal at ~1.2 ppm, attributed to hydroxyl protons. In vivo CEST MRI studies were then carried out on C57BL6 mice bearing orthotopic GL261 brain tumors (n= 5) using a Bruker BioSpec 11.7 T MRI scanner. Both steady-state full Z-spectral images and single offset (1.2 ppm) dynamic dextran-enhanced (DDE) images were acquired before and after the intravenous injection of Dex1 (2 g/kg). The steady-state Z-spectral analysis showed a significantly higher CEST contrast enhancement in the tumor than in contralateral brain (∆MTRasym1.2 ppm = 0.010 ± 0.006 versus 0.002 ± 0.008, P= 0.0069) at 20 min after the injection of Dex1. Pharmacokinetic analyses of DDE were performed using the area under the curve (AUC) in the first 10 min after Dex1 injection, revealing a significantly higher uptake of Dex1 in the tumor than in brain tissue for tumor-bearing mice (AUC[0-10 min] = 21.9 ± 4.2 versus 5.3 ± 6.4%·min, P= 0.0294). In contrast, no Dex1 uptake was foundling in the brains of non-tumor-bearing mice (AUC[0-10 min] = -1.59 ± 2.43%·min). Importantly, the CEST MRI findings were consistent with the measurements obtained using DCE MRI and fluorescence microscopy, demonstrating the potential of Dex1 as a highly translatable CEST MRI contrast agent for assessing tumor hemodynamics.

Comparison of NTCP Models Using Liver Function Obtained From Different Contrast Agent-Based DCE-MRI after SBRT in Hepatocellular Carcinoma

<h3>Purpose/Objective(s)</h3> To develop improved NTCP models of liver function using pre-/during-treatment DCE-MRI for adaptation of radiation therapy (RT) in hepatocellular cancer (HCC) patients who receive SBRT. The liver function map based on Gadoxetate uptake rate (k1) is compared to portal venous perfusion for HCC NTCP models. <h3>Materials/Methods</h3> 194 HCC patients who received SBRT were evaluated. Subcohorts of patients (n = 23, each) with either portal venous perfusion or Gadoxetate uptake rate k1 liver function maps calculated from DCE MRI using respective Gd-DTPA or Gadoxetic-Acid were analyzed. In addition, patients with pre-RT Child-Pugh (C-P) score > 8 for the Gadoxetate map subgroup were excluded and the remaining patients (n = 18) analyzed to explore the effect of pre-RT liver condition to the models. Median prescription doses of 50 Gy were delivered in 3 fractions or 5 fractions (split course with a 1-month break). Physical doses were converted into EQD2 for analysis. A logistic dose-response model was used to estimate the fraction of liver functional loss (change of k1 between pre-/during-RT) as a function of subregional liver dose. NTCP was estimated using the cumulative functional reserve model for change in C-P scores (2-point change in the score). Model parameters were calculated using maximum-likelihood estimations. The logistic local dose-response model coupled with the cumulative functional reserve NTCP model were compared between k1 and perfusion portal venous liver function maps. <h3>Results</h3> In the logistic model, dose that causes 50% reduction of function is much lower for k1 map, D50 of 10.2 Gy (all C-P) /14.8 Gy (pre-RT C-P < = 8) compared to 23.5Gy for perfusion. This may be due to the difference in image biomarkers or the population heterogeneity. As noted, excluding the high pre-RT CP patients, the D50 increases from 10.2 Gy to 14.8 Gy, which may indicate that worse pre-RT liver condition patients will tend to lose more liver function at lower dose. k1 map results showed a steeper response curve for C-P change compared to perfusion results using the NTCP Functional reserve model, with of 2.09/1.52 for k1 and 1.08 for perfusion. The position corresponding to 50% complication probability rate (f₅₀) are similar for both. <h3>Conclusion</h3> Functional maps showed steeper and lower dose for 50% complication compared with previous perfusion results. Considering its superiority to perfusion maps in representing liver "function", this may indicate a lower limit for normal liver dose or better dose distribution to preserve more liver function. The preliminary findings will be validated in a larger and independent cohort.

Toxicity Outcomes of Hypofractionated Image Guided Pencil Beam Scanning Proton Beam Therapy for Spinal Chordomas

To evaluate acute and late toxicities in patients of chordoma treated with hypofractionated, image guided pencil beam scanning proton beam therapy.Consecutive patients diagnosed with spinal chordomas treated at our center with pencil beam scanning proton beam therapy (part of a prospective SCCORE- Sarcoma, Chordoma, Chondrosarcoma registry) were included in this study. Among all patients, high risk CTV (CTV-HR) was treated to a dose of 70.4CGE/32fractions and intermediate risk CTV (CTV-IR) was treated to a dose of 57.6-64CGE/32fractions. Patients underwent clinical, radiological (including metabolic- DCE MRI imaging) and QOL (EORTC QOL QC-30) evaluations at baseline and 6 monthly thereafter. Acute and late toxicities were recorded at each clinical visit as per CTCAE V4.0 and LENT SOMA scales respectively. Clinical and radiological outcomes are being reported in this study.Consecutive 23 patients with a median age of 38 years (range 4-64 years) with histologically proven chordoma (20 classical chordomas, 1 chondroid chordomas and 2 de-differentiated chordomas were analyzed. Among patients with skull base and dorsal chordomas (15 patients) all patients underwent surgery at least once, 21% underwent surgery twice, 14% underwent thrice and 7% underwent more than 3 times. Among patients with sacral chordomas (8 patients), 63% underwent surgery at least once and 50% underwent more than once. 17% patients (2 clival and 2 sacral) underwent prior radiation with photons (IMRT technique) with 13% receiving more than once. Mean volumes of CTV HR and for skull base chordomas was 32.4cc (0.83-132cc) and for sacral chordomas was 855cc (96.1-1740cc). Two patients had acute grade 3 dermatitis, 1 patient had acute grade 3 oropharyngeal mucositis and 1 patient had acute grade 3 gastrointestinal mucositis. 28.5% of patients with Clival and 50% of patients with Sacral lesions had grade 2 acute toxicities. 1 patient had late grade 3 toxicity requiring tracheostomy. None of patients had any late grade 2/3 gastrointestinal or genitourinary toxicities. With a median follow up of 17 months (9-23 months), 21 patients (91%) remained radiologically stable with favorable metabolic response in all the 10 patients (43%) where metabolic imaging was available. Only 1 patient had in-field radiological progression and 1 patient had distant bone metastases.Pencil beam scanning proton beam therapy is feasible in patients with spinal chordomas. Despite high doses, large treatment volumes and large number of patients receiving prior treatments, the acute and late toxicities were relatively low in this cohort.

Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology

In this study, the objective was to characterize the MR signatures of the various benign prostate tissues and to differentiate them from cancer. Data was from seventy prostate cancer patients who underwent multiparametric MRI (mpMRI) and subsequent prostatectomy. The scans included T2-weighted imaging (T2W), diffusion weighted imaging, dynamic contrast-enhanced MRI (DCE MRI), and MR spectroscopic imaging. Histopathology tissue information was translated to MRI images. The mpMRI parameters were characterized separately per zone and by tissue type. The tissues were ordered according to trends in tissue parameter means. The peripheral zone tissue order was cystic atrophy, high grade prostatic intraepithelial neoplasia (HGPIN), normal, atrophy, inflammation, and cancer. Decreasing values for tissue order were exhibited by ADC (1.8 10−3 mm2/s to 1.2 10−3 mm2/s) and T2W intensity (3447 to 2576). Increasing values occurred for DCE MRI peak (143% to 157%), DCE MRI slope (101%/min to 169%/min), fractional anisotropy (FA) (0.16 to 0.19), choline (7.2 to 12.2), and choline / citrate (0.3 to 0.9). The transition zone tissue order was cystic atrophy, mixed benign prostatic hyperplasia (BPH), normal, atrophy, inflammation, stroma, anterior fibromuscular stroma, and cancer. Decreasing values occurred for ADC (1.6 10−3 mm2/s to 1.1 10−3 mm2/s) and T2W intensity (2863 to 2001). Increasing values occurred for DCE MRI peak (143% to 150%), DCE MRI slope (101%/min to 137%/min), FA (0.18 to 0.25), choline (7.9 to 11.7), and choline / citrate (0.3 to 0.7). Logistic regression was used to create parameter model fits to differentiate cancer from benign prostate tissues. The fits achieved AUCs ≥0.91. This study quantified the mpMRI characteristics of benign prostate tissues and demonstrated the capability of mpMRI to discriminate among benign as well as cancer tissues, potentially aiding future discrimination of cancer from benign confounders.

Dynamic contrast enhanced magnetic resonance imaging: A review of its application in the assessment of placental function.

It is important to develop a better understanding of placental insufficiency given its role in common maternofetal complications such as preeclampsia and fetal growth restriction. Functional magnetic resonance imaging offers unprecedented techniques for exploring the placenta under both normal and pathological physiological conditions. Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an established and very robust method to investigate the microcirculatory parameters of an organ and more specifically its perfusion. It is currently a gold standard in the physiological and circulatory evaluation of an organ. Its application to the human placenta could enable to access many microcirculatory parameters relevant to the placental function such as organ blood flow, fractional blood volume, and permeability surface area, by the acquisition of serial images, before, during, and after administration of an intravenous contrast agent. Widely used in animal models with gadolinium-based contrast agents, its application to the human placenta could be possible if the safety of contrast agents in pregnancy is established or they are confirmed to not cross the placenta.