Dynamic Contrast-enhanced Imaging Research Articles

Pseudoangiomatous stromal hyperplasia of the breast: multimodality imaging findings

Background/Aim: Pseudoangiomatous stromal hyperplasia (PASH) is a rare benign mesenchymal proliferative breast lesion. The literature contains little information on the radiological results of this uncommon tumor. In this study, we aim to define the radiologic findings of PASH through our institutional experience.
 Materials and Methods: Patients with PASH of the breast reported in the surgical database of our institution from 2020 to 2023 were retrospectively reviewed. PASH was detected in 11 female patients among the patients who underwent a total of 2172 breast tru-cut biopsies. Nine patients whose imaging studies could be recalled from the picture archiving systems (PACS) were included in the study. BI-RADS, 5th edition, was used to analyze and classify radiologic findings.
 Results: The median age of cases was 41 (range 22–53). Our single-center incidence was found to be 0.5%. Considering the sonographic findings, all of the lesions had an oval shape. On mammography, they were defined as focal asymmetry or circumscribed masses. An MRI was available in three cases. All three cases were hypointense on T1-weighted sequences and hyperintense on T2-weighted sequences. They displayed type 1 or type 2 enhancement curves in the dynamic contrast-enhanced images. No diffusion restriction was detected.
 Conclusion: In this study, tumor-forming PASH were generally circumscribed oval hypoechoic solid masses with minimal vascularity and no posterior acoustic features on ultrasound. On mammography, calcification, architectural distortion, or spiculation were not present in any of the cases. MRI findings were t2 hyperintensity, type 1–2 enhancement kinetics, and no diffusion restriction. In all imaging modalities, the imaging characteristics point to a benign lesion.

Assessment of Dynamic Contrast Enhanced (DCE) MRI for Detection of Radiotherapy Induced Alteration in Mandibular Vasculature

We aim to determine the kinetics of DCE-MRI changes in various mandibular risk volumes based on radiation (RT) dose received. Eighty-eight head and neck cancer (HNC) patients (Pts) who underwent definitive RT were enrolled in this prospective study after IRB approval and informed consent. Images were acquired at pre-RT (Baseline), 3 weeks after RT start date (Mid-RT), 3 mos post-RT (PostRT1), and 6 mos post-RT (PostRT2). Manually segmented mandibular volumes on T2-weighted images were propagated to co-registered DCE-MRIs. Planning CTs and dose grids were also co-registered to corresponding baseline T2 images to create 3-D dose subvolumes. These were used to create 3 risk subvolumes; <30 Gy, 30-50 Gy, and >50 Gy ROIs. DCE images of different timepoints (TPs) were deformably co-registered and the dose subvolumes were propagated to each TP. We used the extended-Tofts model to generate the vascular quantitative maps (Ktrans and Ve). Each subvolume histogram parameters were extracted at each TP. Wilcoxon Signed Rank test was used to compare the changes at different TPs compared to baseline. We classified Pts' delta parameters at different TPs -based on our prior extensive QA assessment- into Pts with stable vascular profile (±25% change), Pts with significant increase (>25% change) and Pts with significant decrease (<-25%). Chi-square test was used to assess the change at different TPs. For <30 Gy subvolumes, there were no significant changes (p > 0.05) in the studied DCE parameters at all TPs except a significant decrease (p < 0.001) in median Ktrans at PostRT2. For 30-50 Gy subvolumes, there was a significant increase in median Ktrans that started at MidRT (p = 0.006) and continued at PostRT1 (p = 0.04) but recovered to baseline values at PostRT2. Median Ve on the other hand only showed significant increase at PostRT1 (p = 0.001), but other TPs were not significantly different compared to baseline. Similarly, subvolumes >50 Gy showed same kinetics as in 30-50 Gy with significant increase of Ktrans at MidRT and PostRT1 and significant increase in Ve in only PostRT1 (P <0.05). For <30 Gy, there was significant increase in the number of Pts with stable or decrease in Ktrans at PostRT2 compared to earlier TPs (70% vs. 60% at PostRT1 and 54% at MidRT p = 0.003). 30-50 Gy subvolumes showed similar profile like <30 Gy with significant increase in the percentage of Pts with recovery at PostRT2. However, for >50 Gy, there was no significant increase in the number of Pts who recovered at PostRT2 (p = 0.3). Ve showed no significant increase in the percentage of Pts with recovery at different TPs (p > 0.05). Results showed that for all dose mandibular subvolumes, there is an acute vascular insult that tends to recover at +6 months post-RT except for a selective group of patients who continue to have persistence of the vascular insult at high dose subvolumes. These findings are of importance for future selection of high risk population for prophylactic intervention against osteoradionecrosis.

Dynamic Contrast Enhanced MRI as a Biomarker of Tumor Response and Oncologic Outcomes in Head and Neck Cancer: Results of a Single Institution Prospective Imaging Study

We aim to determine the correlation between vascular parameters of Dynamic contrast enhanced (DCE) MRIs and tumor response and outcomes in head and neck (HNC) patients treated with definitive radiation therapy (RT). Eighty-two HNC patients are included in this prospective study in one institute. All patients had malignant head and neck neoplasm indicative of curative- intent treatment. Patients were imaged using MRIs pre-, mid-, and post-RT completion at 8-12 weeks. T2-weighted sequences were used for tumor contouring then it was co-registered to respective DCE images. The response to treatment was checked at mid-radiotherapy (mid-RT) and at the end of RT. Mid-RT MRI was co-registered to baseline images and the manually segmented baseline primary tumor regions of interest were propagated to mid-RT images. Quantitative maps (Ktrans, Kep, Ve and Vp) were generated with the extended Tofts pharmacokinetic models and were used for analysis. These vascular parameters were presented as a mean value and percentile using histogram analysis and the following parameters were extracted using an in-house programming environment script: mean, 5th, 10th, 20th, 30th, 40th, 50th (i.e., median), 60th, 70th, 80th, 90th, 95th percentile. The non-parametric Wilcoxon signed-rank test was used to assess the changes of mid-RT DCE parameters compared to baseline. Recursive partitioning analysis (RPA) was used to identify the delta DCE threshold associated with relapse. We assessed the identified thresholds' correlation with oncological and survival endpoints using Cox regression with and without standard clinical variables. The median age for patients is 61 years old (33-78 range). Never smokers are 39 (47%), 35 (43%) are former smoker and 8 (10%) are current smoker with a mean value of 14 pack per year and 26 standard deviations. Using AJCC 8th edition, 39 (47%) are stage I and 19 (23%) are stage II and stage III and IV are 15 (18%) and 9 (10%) respectively. HPV positive are 72 (88%). For patients with GTV-P at baseline (n = 60), 11 (18%) had mid-RT CR at the primary site which increased to 50 (83%) post-RT. The LC and RFS for the entire cohort were 91.4%, and 79.2% respectively. In GTV-P, none of the pre-radiotherapy DCE parameters were correlated with LC or RFS. Wilcoxon signed rank test was statistically significant in 80, 90 and 95 percentiles with (p<0.05). RPA analysis identified different thresholds for each DCE parameter, and its inclusions to the multivariate model improved its performance. In GTV-P, RPA analysis identified ΔKtrans 40 percentiles >15.6% at mid-RT as the most significant point. When this value of ΔKtrans added to the multivariate analysis it was associated with a significantly better model performance in RFS (p = 0.00001). DCE parameters are a very promising tool to correlate with response and outcomes in H&N cancer patients. Future work is warranted for external validation of our findings.

Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer. Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC). Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively). A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.

Analysis of Magnetic Resonance Imaging Perfusion Parameters for the Identification of Spinal Metastatic Tumors with Rich Blood Supply

To determine the reliability of dynamic magnetic resonance imaging (MRI) perfusion parameters for the evaluation of blood supply to spinal metastatic tumors. A total of 36 patients with spinal metastasis who underwent dynamic contrast-enhanced magnetic resonance spinal perfusion imaging at Tianjin Hospital from December 2018 to December 2020 were reviewed. Subsequently, the patients underwent corresponding preoperative examination using digital subtraction angiography of the spine at the hospital and were divided into 2 groups accordingly. Differences in dynamic MRI perfusion parameters between the 2 groups were analyzed. There were statistically significant differences between the 2 groups in the quantitative dynamic contrast-enhanced MRI perfusion parameters vascular permeability and plasma volume, as well as semi-quantitative peak enhancement and blood flow ratio parameters. Dynamic MRI perfusion may distinguish spinal metastatic lesions with rich blood supply from those with poor blood supply and may help clinicians identify patients that can benefit from invasive spinal angiography and preoperative embolization. This technique may also provide guidance on decision taking for surgery basing on dynamic MRI perfusion parameters.

Three-Dimensional Arterial Spin Labeling for the Evaluation of the Cerebral Hemodynamics in Ischemic and Hemorrhagic Moyamoya Disease

Introduction: Moyamoya disease (MMD) is a vascular disease with significant risk of mortality due to ischemia or hemorrhage in the brain. The goal of the study was to explore three-dimensional arterial spin labeling (3D-ASL) to improve evaluation of cerebral hemodynamics in patients with MMD. Methods: Our study included 54 cases of ischemic MMD and 42 cases of hemorrhagic MMD. Dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) and 3D-ASL were performed at 3.0 T. Based on these scans, cerebral blood flow (CBF), mean transit time (MTT), and time to peak (TTP) were calculated and compared between patients with different disease subtypes. Receiver operating characteristics analysis was used to assess the diagnostic sensitivity and specificity of different imaging procedures and parameters. Results: Our data suggested that CBF in the lesion area was more severely reduced in patients with hemorrhagic MMD than in those with ischemic MMD. The CBF parameter in 3D-ASL diagnosed hemorrhagic and ischemic MMD with a significant sensitivity and specificity of 80.59% and 57.41%, respectively, with an area under the curve (AUC) of 0.75. We also found that the relative CBF of 3D-ASL was more pronounced decreased and the relative MTT and TTP of DSC-PWI were significantly increased in patients with hemorrhagic MMD than those with ischemic MMD. Specificity and sensitivity and AUC of 3D-ASL were better than the comparison of absolute values from DSC-PWI scans. Conclusion: Our study indicated that 3D-ASL is powerful in differentiating patients with cerebral ischemic or hemorrhagic MMD, providing another diagnostic tool that could potentially improve precision medicine to monitoring MMD patients.

Feasibility and Implementation of a 4D Free-Breathing Variable Density Stack-of-Stars Functional Magnetic Resonance Urography in Young Children Without Sedation.

Functional magnetic resonance urography (MRU) is well established in the diagnostic workup of urinary tract anomalies in children, providing comprehensive morphological and functional information. However, dynamic contrast-enhanced images acquired in the standard Cartesian k-space manner are prone to motion artifacts. A newly introduced 4D high spatiotemporal resolution dynamic contrast-enhanced magnetic resonance imaging based on variable density elliptical centric radial stack-of-stars sharing technique has shown improved image quality regarding motions under free breathing. The aims of this study were to implement this 4D free-breathing sequence for functional MRU and to compare its image quality and analyzability with standard breath-hold Cartesian MRU. We retrospectively evaluated all functional 4D MRU performed without general anesthesia between September 2021 and December 2022 and compared them with matched pairs (age, affected kidney, diagnosis) of standard Cartesian MRU between 2016 and 2022. Image analysis was performed by 2 radiologists independently regarding the following criteria using a 4-point Likert scale, with 4 being the best: overall image quality, diagnostic confidence, respiratory motion artifacts, as well as sharpness and contrast of aorta, kidneys, and ureters. We also measured vertical kidney motion due to respiratory motion and compared the variance for each kidney using F test. Finally, both radiologists calculated the volume, split renal volume (vDRF), split renal Patlak function (pDRF), and split renal function considering the volume and Patlak function (vpDRF) for each kidney. Values were compared using Bland-Altman plots and F test. Forty children (20 for 4D free-breathing and standard breath-hold, respectively) were enrolled. Ten children of each group were examined using feed-and-sleep technique (median age: 4D, 3.3 months; standard, 4.2 months), 10 were awake (median age: 4D, 8.9 years; standard, 8.6 years). Overall image quality, diagnostic confidence, respiratory motion artifacts, as well as sharpness and contrast of the aorta, kidneys, and ureters were rated significantly better for 4D free-breathing compared with standard breath-hold by both readers ( P ranging from <0.0001 to 0.005). Vertical kidney motion was significantly reduced in 4D free-breathing for the right and the left kidney (both P < 0.001). There was a significantly smaller variance concerning the differences between the 2 readers for vpDRF in 4D MRU ( P = 0.0003). In contrast, no significant difference could be demonstrated for volume ( P = 0.05), vDRF ( P = 0.93), and pDRF ( P = 0.14). We demonstrated the feasibility of applying a 4D free-breathing variable density stack-of-stars imaging for functional MRU in young pediatric patients with improved image quality, fewer motion artifacts, and improved functional analyzability.

Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging.

To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared with biparametric imaging. We collected 3227 multiparametric imaging sets from 332 patients, including 218 cancer patients (291 biopsy-proven foci) and 114 noncancer patients. Diagnostic algorithms of T2-weighted, T2-weighted plus dynamic contrast-enhanced, biparametric, and multiparametric imaging were built using 2578 sets, and their performance for clinically significant cancer was evaluated using 649 sets. Biparametric and multiparametric imaging had following region-based performance: sensitivity of 71.9% and 74.8% (p = 0.394) and positive predictive value of 61.3% and 74.8% (p = 0.013), respectively. In side-specific analyses of cancer images, the specificity was 72.6% and 89.5% (p < 0.001) and the negative predictive value was 78.9% and 83.5% (p = 0.364), respectively. False-negative cancer on multiparametric imaging was smaller (p = 0.002) and more dominant with grade group ≤2 (p = 0.028) than true positive foci. In the peripheral zone, false-positive regions on biparametric imaging turned out to be true negative on multiparametric imaging more frequently compared with the transition zone (78.3% vs. 47.2%, p = 0.018). In contrast, T2-weighted plus dynamic contrast-enhanced imaging had lower specificity than T2-weighted imaging (41.1% vs. 51.6%, p = 0.042). When using deep learning, multiparametric imaging provides superior performance to biparametric imaging in the specificity and positive predictive value, especially in the peripheral zone. Dynamic contrast-enhanced imaging helps reduce overdiagnosis in multiparametric imaging.

A Nomogram of MRI Features to Assess Muscle Invasion in VI-RADS 2 Tumors With Stalk.

Vesical Imaging-Reporting and Data System (VI-RADS) is widely used to assess the muscle-invasive status of bladder cancer. However, the current classification efficacy of VI-RASD 2 tumors of stalk is unsatisfactory. To develop a nomogram to assess muscle-invasive bladder cancer (MIBC) in VI-RADS 2 tumors with stalk. Retrospective. A total of 186 patients (age: 67.8 ± 12.7 years) with 15.1% females, divided randomly into a training cohort (N = 130) and validation cohort (N = 56). 3-T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin-echo), and dynamic contrast-enhanced imaging (gradient-echo). Twenty-one MRI features of tumors and stalks were developed from training cohort. The mean apparent diffusion coefficient (ADC) values of the tumor, stalk, and psoas muscles were calculated from the three circular regions of interest. The normalized T . The normalized ST . Three readers assessed the morphology of tumors and stalks. The final features of nomogram were selected by univariable logistic and the least absolute shrinkage and selection operator (LASSO) regression. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis. In VI-RADS 2 tumors with stalk, tumor size over 3 cm, increased stalk width, stalk morphology, decreased normalized T value, and increased normalized ST value were selected as the risk factors for MIBC. The AUC, accuracy, sensitivity, and specificity of the nomogram to assess MIBC were 0.969 (95% CI: 0.941-0.997), 92.3%, 94.1%, and 92.0% in training cohort and 0.940 (95% CI: 0.859-1.000), 89.3%, 75.0%, and 91.7% in validation cohort. This study constructed a nomogram for preoperative assessment of MIBC and modifying the current VI-RADS. 3 TECHNICAL EFFICACY: Stage 2.

Determination of prognostic predictors in patients with solitary hepatocellular carcinoma: histogram analysis of multiparametric MRI.

To evaluate the histogram parameters of preoperative multiparametric magnetic resonance imaging (MRI) and clinical-radiological (CR) characteristics as prognostic predictors in patients with solitary hepatocellular carcinoma ≤ 5 cm and to determine the optimal time window for histogram analysis. We retrospectively included 151 patients who underwent preoperative MRI between January 2012 and December 2017. All patients were randomly separated into training and validation cohorts (n = 105 and 46). Eight whole-lesion histogram parameters were extracted from T2-weighted images, apparent diffusion coefficient maps, and dynamic contrast-enhanced images. Univariate and multivariate logistic regression analyses were performed to evaluate these histogram parameters and CR variables related to early recurrence (ER) and recurrence-free survival. A nomogram was derived from the clinical-radiological-histogram (CRH) model that incorporated these risk factors. Kaplan-Meier survival analysis was performed to evaluate the prognostic performance of the CRH model. In total, 151 patients (male: female, 130: 21; median age, 54.46 ± 9.09 years) were evaluated. Multivariate logistic regression analysis revealed that the significant risk factors of ER were Mean Absolute Deviation and Minimum in the histogram analysis of the delayed phase images, as well as three important CR variables: albumin-bilirubin grade, microvascular invasion, and tumor size. The nomogram built by incorporating these risk factors showed satisfactory predictive ability in the training and validation cohorts with AUC values of 0.747 and 0.765, respectively. Furthermore, the prognostic nomogram can effectively classify patients into high- and low-risk groups (p < 0.05). Multiparametric MRI-derived histogram parameters provide additional value in predicting patient prognosis. The CRH model may be a useful and noninvasive method for achieving prognostic stratification and personalized disease management.

Functional assessment in endometrial and cervical cancer: diffusion and perfusion, two captivating tools for radiologists.

Uterine cervical and endometrial cancers are two major gynecological malignancies, affecting women's health worldwide. Magnetic resonance imaging (MRI) is appropriate for evaluating malignant disease, thanks to the excellent soft tissue contrast and multiplanar imaging ability. Recently, functional MR techniques, namely diffusion-weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE), have proved to be a precious support not only in cancer diagnosis but also in disease staging, in the therapy planning, in monitoring response to treatment and during long-term recurrence surveillance. In the field of gynecologic oncology, the European Society of Urogenital Radiology (ESUR) recommends DWI and dynamic contrast-enhanced imaging (DCE-MRI) for local staging of endometrial and cervical cancer, but the potential application of functional imaging in all different aspects of patient management seems very promising. The aim of this article is to summarize the existing literature, providing a comprehensive update on the role of functional MRI in endometrial and cervical cancer.

Multiparametric MRI and Radiomics for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers.

Background Half of breast cancers exhibit low expression levels of human epidermal growth factor receptor 2 (HER2) and can be targeted by new antibody-drug conjugates. The imaging differences between HER2-zero (immunohistochemistry [IHC] score of 0), HER2-low (IHC score of 1+ or 2+ with negative findings at fluorescence in situ hybridization [FISH]), and HER2-positive (IHC score of 2+ with positive findings at FISH or IHC score of 3+) breast cancers were unknown. Purpose To assess whether multiparametric dynamic contrast-enhanced MRI-based radiomic features can help distinguish HER2 expressions in breast cancer. Materials and Methods This study included women with breast cancer who underwent MRI at two different centers between December 2020 and December 2022. Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted images. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were used for the selection of features to build a radiomics signature. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the radiomic signature. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions in both the training and prospectively acquired external data set. Results The training set included 208 patients from center 1 (mean age, 53 years ± 14 [SD]), and the external test set included 131 patients from center 2 (mean age, 54 years ± 13). In the external test data set, the radiomic signature achieved an AUC of 0.80 (95% CI: 0.71, 0.89) for distinguishing HER2-low and -positive tumors versus HER2-zero tumors and was a significant predictive factor for distinguishing these two groups (odds ratio = 7.6; 95% CI: 2.9, 19.8; P < .001). Among HER2-low or -positive breast cancers, histology type, associated nonmass enhancement, and multiple lesions at MRI had an AUC of 0.77 (95% CI: 0.68, 0.86) in the external test set for the prediction of HER2-positive versus HER2-low cancers. Conclusion The radiomic signature and tumor descriptors from multiparametric breast MRI may predict distinct HER2 expressions of breast cancers with therapeutic implications. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kataoka and Honda in this issue.

Liver Cancer Vascularity Driven by Extracellular Matrix Stiffness: Implications for Imaging Research.

Extracellular matrix stiffness represents a barrier to effective local and systemic drug delivery. Increasing stiffness disrupts newly formed vessel architecture and integrity, leading to tumor-like vasculature. The resulting vascular phenotypes are manifested through different cross-sectional imaging features. Contrast-enhanced studies can help elucidate the interplay between liver tumor stiffness and different vascular phenotypes. This study aims to correlate extracellular matrix stiffness, dynamic contrast-enhanced computed tomography, and dynamic contrast-enhancement ultrasound imaging features of 2 rat hepatocellular carcinoma tumor models. Buffalo-McA-RH7777 and Sprague Dawley (SD)-N1S1 tumor models were used to evaluate tumor stiffness by 2-dimensional shear wave elastography, along with tumor perfusion by dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography. Atomic force microscopy was used to calculate tumor stiffness at a submicron scale. Computer-aided image analyses were performed to evaluate tumor necrosis, as well as the percentage, distribution, and thickness of CD34+ blood vessels. Distinct tissue signatures between models were observed according to the distribution of the stiffness values by 2-dimensional shear wave elastography and atomic force microscopy ( P < 0.05). Higher stiffness values were attributed to SD-N1S1 tumors, also associated with a scant microvascular network ( P ≤ 0.001). Opposite results were observed in the Buffalo-McA-RH7777 model, exhibiting lower stiffness values and richer tumor vasculature with predominantly peripheral distribution ( P = 0.03). Consistent with these findings, tumor enhancement was significantly greater in the Buffalo-McA-RH7777 tumor model than in the SD-N1S1 on both dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography ( P < 0.005). A statistically significant positive correlation was observed between tumor perfusion on dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography in terms of the total area under the curve and % microvessel tumor coverage ( P < 0.05). The stiffness signatures translated into different tumor vascular phenotypes. Two-dimensional shear wave elastography and dynamic contrast-enhanced ultrasonography adequately depicted different stromal patterns, which resulted in unique imaging perfusion parameters with significantly greater contrast enhancement observed in softer tumors.

A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data.

Breast cancer is a major cause of mortality among women worldwide. Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) is a good imaging technique that can show temporal information about the kinetics of the contrast agent in suspicious breast lesions as well as acceptable spatial resolution. Computer-aided detection systems assist in the detection of lesions through medical image processing techniques combined with computerized analysis and calculation, which in turn helps radiologists recognize molecular subtypes of breast lesions that will be beneficial for better treatment plan decisions. In this paper, a computer-aided diagnosis method is proposed to automatically locate breast cancer lesions and identify molecular subtypes of breast cancer with heterogeneity analysis from radiomics data. A fast region-based convolutional network (Faster R-CNN) framework is first applied to images to detect breast cancer lesions. Then, the heterogeneous regions of every breast cancer lesion are extracted. Based on the multiple visual and kinetic radiomics features extracted from the heterogeneous regions, a temporal bag of visual word model is proposed, which takes into account the dynamic characteristics of both lesion and heterogeneous regions in images over time. The recognition task of molecular subtypes of breast lesions is realized based on a stacking classification model. At the genetic level, breast cancer is divided into four molecular subtypes, namely, luminal epithelial type A (Luminal A), luminal epithelial type B (Luminal B), HER-2 overexpression and basal cell type. The experimental results show that the precision of the four subtypes is 93%, 94%, 83%, 86%; the recall is 96%, 80%, 91%, 94%; and the F1-score is 95%, 86%, 87%. The experimental results denote the influence of heterogeneous regions on the recognition task. The DCE-MRI-based approach to identify molecular typing of breast cancer for noninvasive diagnosis will contribute to the development of breast cancer treatment, improved outcomes and reduced mortality.

Evaluation of Performance Tradeoffs When Using Mechanically Swept 1-D Linear Arrays for 3-D DCE-US.

Dynamic contrast-enhanced ultrasound imaging (DCE-US) may be used to characterize tumor vascular perfusion using metrics derived from time-amplitude curves (TACs). The 3-D DCE-US enables generation of 3-D parametric maps of TAC metrics that may inform on how perfusion varies across the entire tumor. The aim of this work was to understand the effect of low temporal sampling (i.e., < 1 Hz) typical of 3-D imaging using a swept 1-D array transducer on the evaluation of TAC metrics and the effect of transducer motion in combination with flow on 3-D parametric maps generated using both plane wave imaging (PWI) (seven angles) and focused imaging (FI). Correlation maps were introduced to evaluate the spatial blurring of TAC metrics. A research ultrasound scanner and a pulse-inversion algorithm were used to obtain DCE-US. The 2-D (frame rate 10 Hz) and 3-D (volume rate 0.4 Hz) images were acquired of a simple wall-less vessel phantom (flow phantom) and a cartridge phantom. Volumetric imaging provided similar TACs to that of the higher 2-D sampling rate. Varying sweep speed and acceleration/deceleration had little influence on the 3-D TAC compared to 2-D for both FI and PWI. Sweeping motion and limited temporal sampling (0.4 Hz) did not change the spatial correlation of TAC metrics measured using FI, whereas a small increase in correlation across the cartridge phantom was observed for PWI. This was attributed to grating lobe artifacts, broad beam spatial blurring, and incoherent compounding caused by motion. Increased correlation will reduce the spatial resolution with which inhomogeneity of vascular perfusion can be mapped supporting the choice of FI for DCE-US.

Inter-reader agreement of the prostate imaging quality (PI-QUAL) score for basic readers in prostate MRI: A multi-center study

BackgroundThe Prostate Imaging Quality (PI-QUAL) score is the first step toward image quality assessment in multi-parametric prostate MRI (mpMRI). Previous studies have demonstrated moderate to excellent inter-rater agreement among expert readers; however, there is a need for studies to assess the inter-reader agreement of PI-QUAL scoring in basic prostate readers. ObjectivesTo assess the inter-reader agreement of the PI-QUAL score amongst basic prostate readers on multi-center prostate mpMRI. MethodsFive basic prostate readers from different centers assessed the PI-QUAL scores independently using T2-weighted images, diffusion-weighted imaging (DWI) including apparent diffusion coefficient (ADC) maps, and dynamic-contrast-enhanced (DCE) images on mpMRI data obtained from five different centers following Prostate Imaging-Reporting and Data System Version 2.1. The inter-reader agreements amongst radiologists for PI-QUAL were evaluated using weighted Cohen’s kappa. Further, the absolute agreements in assessing the diagnostic adequacy of each mpMRI sequence were calculated. ResultsA total of 355 men with a median age of 71 years (IQR, 60–78) were enrolled in the study. The pair-wise kappa scores ranged from 0.656 to 0.786 for the PI-QUAL scores, indicating good inter-reader agreements between the readers. The pair-wise absolute agreements ranged from 0.75 to 0.88 for T2W imaging, from 0.74 to 0.83 for the ADC maps, and from 0.77 to 0.86 for DCE images. ConclusionsBasic prostate radiologists from different institutions provided good inter-reader agreements on multi-center data for the PI-QUAL scores.

DCE-MRI-derived microvasculature mapping and outcome prediction in patients with breast cancer.

e12614 Background: Tumor response to neoadjuvant therapy (NAT) in patients with breast cancer is related to perfusion in the tumor microenvironment and surrounding tissue. Direct and indirect measures of microvascular (MV) permeability could improve the ability to predict short- and long-term patient outcomes. Detection and quantification of circulating tumor DNA (ctDNA) is one such measurement that depends on microvasculature and has been shown to strongly correlate with both response to therapy and overall prognosis in a wide range of cancers. We hypothesized that a combination of ctDNA and numerical computations of MV permeability using pretreatment dynamic contrast-enhanced (DCE) MRI images (MV maps) can predict patient response to NAT and long-term survival outcomes. Here, we show how MV maps — computed from standard-of-care (SOC) imaging — improve upon ctDNA measurements in predicting pathologic complete response (pCR) following NAT as well as distant recurrence free survival (DRFS). Methods: DCE-MRIs were obtained for a sub-sample of I-SPY2 patients (n = 81) whose ctDNA levels were previously reported (Magbanua et al 2021). Tumors were segmented from pretreatment images by a convolutional neural network with manual verification. MV maps were computed using a Tofts-like model to describe the transfer kinetics between microvasculature and extracellular extravascular space. Microvasculature parameters (MVPs), such as median and skewness, were extracted from MV maps to describe the distribution of kinetic parameters in the intra- and peritumoral regions. Logistic regression was used to predict pCR with leave-one-out cross validation. Univariate Cox models were used to stratify DRFS. Results: MVPs exhibited a moderate correlation with pretreatment ctDNA levels (Spearman rho = -0.26; p = 0.018) and with pCR status in this cohort (rho = 0.25; p = 0.026). In a series of logistic regression models, a combination of SOC clinical features and ctDNA levels correctly classified 23% of true pCRs, with an F1 score of 0.58. This predictive power increased when informed by MV maps; 45% of true pCRs were identified after the addition of MVPs, and the F1 score increased to 0.68. In univariate Cox models, 24 MVPs significantly stratified DRFS (p &lt; 0.05) when uncorrected for multiple hypothesis testing bias. The top MVP carried a hazard ratio 3.8 (95%CI 1.6 - 9.0) compared to 11.9 (95%CI 2.7 - 53.6) for ctDNA levels. Conclusions: DCE-MRI-derived kinetic features are calculated from SOC images with no additional clinical intervention. We found that MVPs substantially improve pCR prediction when added to models trained on ctDNA levels and clinical features. ctDNA levels and MVPs were predictive of DRFS to differing degrees of confidence. Although further validation in larger datasets is needed, this exploratory analysis uncovered a promising role for MV maps in predicting response to NAT as well as patient outcomes.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor.

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy

PurposeTo assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs). MethodsSeventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman’s correlation coefficients (ρ) were computed. ResultsHGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67–0.91) and 0.74 (95% CI, 0.59–0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48–0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52–0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57–0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01). ConclusionsThe 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.

Intravesical Contrast-Enhanced MRI: A Potential Tool for Bladder Cancer Surveillance and Staging.

This review article gives an overview of the current state of the art of bladder cancer imaging and then discusses in depth the scientific and technical merit of a novel imaging approach, tracing its evolution from murine cancer models to cancer patients. While the poor resolution of soft tissue obtained by widely available imaging options such as abdominal sonography and radiation-based CT leaves them only suitable for measuring the gross tumor volume and bladder wall thickening, dynamic contrast-enhanced magnetic resolution imaging (DCE MRI) is demonstrably superior in resolving muscle invasion. However, major barriers still exist in its adoption. Instead of injection for DCE-MRI, intravesical contrast-enhanced MRI (ICE-MRI) instills Gadolinium chelate (Gadobutrol) together with trace amounts of superparamagnetic agents for measurement of tumor volume, depth, and aggressiveness. ICE-MRI leverages leaky tight junctions to accelerate passive paracellular diffusion of Gadobutrol (604.71 Daltons) by treading the paracellular ingress pathway of fluorescein sodium and of mitomycin (<400 Daltons) into bladder tumor. The soaring cost of diagnosis and care of bladder cancer could be mitigated by reducing the use of expensive operating room resources with a potential non-surgical imaging option for cancer surveillance, thereby reducing over-diagnosis and over-treatment and increasing organ preservation.