Dynamic Contrast-enhanced MRI Imaging Research Articles

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical.

Perfusion magnetic resonance imaging in Asherman syndrome.

Microvascular scarring compromises the functionality of the endometrium, and vascular flow at the junctional zone (JZ) may be the key to understanding poor reproductive outcomes in women with Asherman syndrome (AS). To investigate whether vascular perfusion of the uterus, measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is impaired in women with intrauterine adhesions (IUA) and AS. A prospective observational cohort pilot study of 23 women with IUA treated with hysteroscopic synecholysis and a control group of two patients with cervix cancer were subject to DCE-MRI with gadolinium to assess uterine vascularity. Twelve regions of interest (ROIs) were allocated on the DCE-MRI image incorporating the JZ, with control ROI placed at the psoas muscle. Individual ROIs were compared to the mean total perfusion (TP) in the same uterus. Pre- and post-operative perfusion analyses were performed on five women. Receiver operator curves (ROC) were used to analyse MRI as a predictor of IUA. There was no significant difference in perfusion; a trend toward reduced perfusion was observed in women with IUA compared to the controls. The ROC was predictive of higher-grade and inoperable IUA. Reduced perfusion on DCE-MRI as assessed by ROC predicted higher-stage AS. The results of this study support further investigation of DCE-MRI as a prognostic tool for AS prior to surgical intervention to assist in providing prognostic guidance for women suffering from AS.

Retracted: Predictive Value of Preoperative Dynamic Contrast-Enhanced MRI Imaging Features in Breast Cancer Patients with Postoperative Recurrence Time.

[This retracts the article DOI: 10.1155/2022/9556880.].

Radiation source personalization for nanoparticle-enhanced radiotherapy using dynamic contrast-enhanced MRI in the treatment planning process

Nanoparticle-enhanced radiotherapy offers the potential to selectively increase the radiation dose imparted to the tumor while at the same time sparing the healthy structures around it. Among the recommendations of an interdisciplinary group for the clinical translation of this treatment modality, is the developing of methods to quantify the effects that the nanoparticle concentration has on the radiation dosimetry and incorporate these effects into the treatment planning process. In this work, using Monte Carlo simulations and dynamic contrast-enhanced MRI images, treatment plans for nanoparticle-enhanced radiotherapy are calculated in order to evaluate the effects that realistic distributions of the nanoparticles have on the resultant plans and to devise treatment strategies to account for these effects, including the selection of the proper x-ray source configuration in terms of energy and collimation. Dynamic contrast-enhanced MRI studies were obtained for two treatment sites, namely brain and breast. A model to convert the MRI signal to contrast agent concentration was applied to each set of images. Two treatment modalities, 3D conformal radiotherapy and Stereotactic Body Radiation Therapy, were evaluated at three different x-ray spectra, namely 6 MV from a linear accelerator, 110 kVp and 220 kVp from a tungsten target. For the breast patient, as the nanoparticle distribution varies markedly with time, the treatment plans were obtained at two different times after administration. It was determined that maximum doses to the healthy structures around the tumor are mostly determined by the minimum nanoparticle concentration in the tumor. The presence of highly hypoxic or necrotic tissue, which fails to accumulate the nanoparticles, or leakage of the contrast agent into the surrounding healthy tissue, make irradiation with conventional conformal radiotherapy unfeasible for kilovoltage beam energies, as the uniform beam apertures lack the ability to compensate for the non-uniform distribution of the nanoparticles. Therefore, proper quantification of the nanoparticle distribution not only in the target volume but also in the surrounding tissues and structures is crucial for the proper planning of nanoparticle-enhanced radiotherapy and a treatment delivery with a high-degree of freedom, such as small-field stereotactic body radiotherapy, should be the method of choice for this treatment modality.

An improved semantic segmentation for breast lesion from dynamic contrast enhanced MRI images using deep learning

AbstractThe World Health Organization (WHO) reports that approximately 2.3 million breast cancer cases are diagnosed each year. Early detection is key to tackling this issue, and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE‐MRI) is a preferred method for detecting tumors. Convolutional Neural Networks (CNNs) can accurately segment images without human assistance. The objective of this study is to develop a computer‐aided diagnosis system that can segment breast lesions from DCE‐MRI images. A 92‐layer deep CNN, called DCNN‐92, and a 94‐layer deep CNN, called DCNN‐94, have been designed to identify lesions. The proposed methods have been validated using images from The Cancer Image Archive (TCIA) database. The proposed DCNN‐92 model segments the tumor pixels effectively, but it exhibits some misclassifications where certain background pixels are incorrectly labeled as tumor and tumor pixels are identified as background. To segment the tumor pixels more accurately, two grouped convolution layers are added to the DCNN‐92 model. The model with 94 layers, that is, DCNN‐94, segments most of the tumor pixels correctly, thereby enhancing the segmentation performance. When compared to DCNN‐92, the DCNN‐94 model exhibits enhanced performance across standard metrics such as sensitivity, dice coefficient, Jaccard coefficient, and area under the curve (AUC). It was found that the training time for DCNN‐94 is shorter. The DCNN‐94 model with dilation factor and group convolution is concluded to be an effective method for lesion segmentation from breast DCE‐MRI images compared to existing methods.

Coarse-to-fine registration and time-intensity curves constraint for liver DCE-MRI synthesis

Image registration plays a crucial role in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), used as a fundamental step for the subsequent diagnosis of benign and malignant tumors. However, the registration process encounters significant challenges due to the substantial intensity changes observed among different time points, resulting from the injection of contrast agents. Furthermore, previous studies have often overlooked the alignment of small structures, such as tumors and vessels. In this work, we propose a novel DCE-MRI registration framework that can effectively align the DCE-MRI time series. Specifically, our DCE-MRI registration framework consists of two steps, i.e., a de-enhancement synthesis step and a coarse-to-fine registration step. In the de-enhancement synthesis step, a disentanglement network separates DCE-MRI images into a content component representing the anatomical structures and a style component indicating the presence or absence of contrast agents. This step generates synthetic images where the contrast agents are removed from the original images, alleviating the negative effects of intensity changes on the subsequent registration process. In the registration step, we utilize a coarse registration network followed by a refined registration network. These two networks facilitate the estimation of both the coarse and refined displacement vector fields (DVFs) in a pairwise and groupwise registration manner, respectively. In addition, to enhance the alignment accuracy for small structures, a voxel-wise constraint is further conducted by assessing the smoothness of the time-intensity curves (TICs). Experimental results on liver DCE-MRI demonstrate that our proposed method outperforms state-of-the-art approaches, offering more robust and accurate alignment results.

Pathologic complete response prediction in breast cancer lesion segmentation and neoadjuvant therapy.

Predicting whether axillary lymph nodes could achieve pathologic Complete Response (pCR) after breast cancer patients receive neoadjuvant chemotherapy helps make a quick follow-up treatment plan. This paper presents a novel method to achieve this prediction with the most effective medical imaging method, Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI). In order to get an accurate prediction, we first proposed a two-step lesion segmentation method to extract the breast cancer lesion region from DCE-MRI images. With the segmented breast cancer lesion region, we then used a multi-modal fusion model to predict the probability of axillary lymph nodes achieving pCR. We collected 361 breast cancer samples from two hospitals to train and test the proposed segmentation model and the multi-modal fusion model. Both segmentation and prediction models obtained high accuracy. The results show that our method is effective in both the segmentation task and the pCR prediction task. It suggests that the presented methods, especially the multi-modal fusion model, can be used for the prediction of treatment response in breast cancer, given data from noninvasive methods only.

Radiomic Nomogram for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer

The detection of axillary lymph node metastasis (ALNM) in patients with breast cancer is a crucial determinant in the decision-making process for axillary surgery and potential therapies. The objective of this study was to develop and validate a radiomics nomogram that integrates radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical factors to predict ALNM in patients with breast cancer. A total of 177 patients with breast cancer were randomly divided into a training set (n=123) and a validation set (n=54) using a 7:3 ratio. From the DCE-MRI images, 2818 radiomics features were extracted from the primary tumor and axillary lymph node (ALN). Subsequently, optimal features were selected through the least absolute shrinkage and selection operator algorithmto construct the Radscore. Clinical factors were identified using univariate logistic regression analysis and included in a multivariate logistic regression analysis. Using the Radscore and clinical factors, a radiomics nomogram was developed using the Support Vector Machinemethod. The predicting efficacy of our model was visually appraised utilizing a receiver operator characteristic (ROC) curve, while its clinical application and predictive accuracy were assessed through decision curve analysis (DCA) and calibration curves, respectively. The results revealed Ki67, multifocality, and MRI-reported ALN status as independent risk factors for ALNM. The radiomics nomogram demonstrated good calibration and discrimination with areas under the ROC curve of 0.92 (95% confidence interval [CI], 0.88-0.97) in the training set and 0.90 (95% CI, 0.72-0.90) in the validation set. DCA revealed the clinical usefulness of the radiomics nomogram. The DCE-MRI-based radiomics nomogram is a reliable tool for assessing ALNM in patients with breast cancer.

Blood-brain barrier disruption imaging in postoperative cerebral hyperperfusion syndrome using DCE-MRI

Little has been reported about the association between cerebral hyperperfusion syndrome (CHS) and blood-brain barrier (BBB) disruption in human. We aimed to investigate the changes in permeability after bypass surgery in cerebrovascular steno-occlusive diseases using dynamic contrast-enhanced MRI (DCE-MRI) and to demonstrate the association between CHS and BBB disruption. This retrospective study included 36 patients (21 hemispheres in 18 CHS patients and 20 hemispheres in 18 controls) who underwent combined bypass surgery for moyamoya and atherosclerotic steno-occlusive diseases. DCE-MRI and arterial spin labeling perfusion-weighted imaging (ASL-PWI) were obtained at the baseline, postoperative state, and discharge. Perfusion and permeability parameters were calculated at the MCA territory (CBF(territorial), Ktrans(territorial), Vp(territorial)) and focal perianastomotic area (CBF(focal), Ktrans(focal), Vp(focal)) of operated hemispheres. As compared with the baseline, both CBF(territorial) and CBF(focal) increased in the postoperative period and decreased at discharge, corresponding well to symptoms in the CHS group. Vp(focal) was lower in the postoperative period and at discharge, as compared with the baseline. In the control group, no parameters significantly differed among the three points. In conclusion, Vp at the focal perianastomotic area significantly decreased in patients with CHS during the postoperative period. BBB disruption may be implicated in the development of CHS after bypass surgery.

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

Molecular subtypes classification of breast cancer in DCE-MRI using deep features

Breast cancer is a major cause of concern on a global scale due to its high incidence rate. It is one of the leading causes of death for women, if left untreated. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly being used in the evaluation of breast cancer. Prior studies neglected to take into account breast cancer characteristics and features that might be helpful for distinguishing the four molecular subtypes of breast cancer. The use of breast DCE-MRI to identify the molecular subtypes is now the focus of research in breast cancer analysis. It offers breast cancer patients a better chance for an early and effective treatment plan. A manually annotated dataset of 1359 DCE-MRI images was used in this study, with 70% used for training and the remaining for testing. Twelve deep features were extracted from this dataset. The dataset was initially preprocessed through placing the ROIs by a radiologist experienced in breast MRI interpretation, then deep features are extracted using the proposed convolutional neural network (CNN). Finally, the deep features extracted are classified into molecular subtypes of breast cancer using the support vector machine (SVM). The effectiveness of the predictive model was assessed using accuracy and area under curve (AUC) measures. The test was performed on unseen held-out data. The maximum achieved accuracy and AUC were 99.78% and 100% respectively, with substantially a low complexity rate.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n=519), internal validation set (n=129), external test set 1 (n=296), and external test set 2 (n=44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

An imaging‑based diagnostic approach to vascular anomalies of the oral and maxillofacial region.

The accurate diagnosis of vascular anomalies (VAs) is considered a challenging endeavor. Misdiagnosis of VAs can lead clinicians in the wrong direction, such as the performance of an unnecessary biopsy or inappropriate surgical procedures, which can potentially lead to unforeseen consequences and increase the risk of patient injury. The purpose of the present study was to develop an approach for the diagnosis of VAs of the oral and maxillofacial region based on computed tomography (CT), magnetic resonance imaging (MRI) and dynamic contrast-enhanced MRI (DCE-MRI). In the present study, the CT and MR images of 87 VAs were examined, and the following imaging features were evaluated: Detectability of the lesion, the periphery of the lesion, the inner nature of the lesion, the density of the lesion on CT, the signal intensity of the lesion on MRI, the detectability of phleboliths and the shape of the lesion. A total of 29 lesions were further evaluated using the contrast index (CI) curves created from the DCE-MRI images. A diagnostic diagram, which is based on the imaging features of VAs and CI curve patterns, was subsequently extrapolated. The results obtained demonstrated that the VAs were detected more readily by MRI compared with CT, whereas the detectability of phleboliths was superior when using CT compared with MRI. VAs showed a propensity for homogeneous isodensity on CT, whereas, by contrast, they exhibited a propensity for heterogeneous hyperdensity on CE-CT. VAs also showed a propensity for homogeneous intermediate signal intensity when performing T1-weighted imaging (T1WI), heterogeneous high signal intensity when performing short tau inversion recovery MRI, and heterogeneous high signal intensity when performing fat-saturated CE-T1WI. The CI curves of VAs were found to exhibit a specific pattern: Of the 29 CI curves, 23 (79.3%) showed early weak enhancement, followed by a plateau leading up to 400-600 sec. An imaging-based diagnostic diagram was ultimately formulated. This diagram can act as an aid for radiologists when they are expecting to find a VA, and hopefully serve the purpose of simplifying the diagnostic process. Taken together, the findings of the present study indicated that DCE-MRI may be considered a useful tool for the diagnosis of VAs.

DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast‐enhanced magnetic resonance imaging (DCE-MRI)-based radiomics.MethodsThe data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models.ResultsAmong the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit.ConclusionThe DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.Graphical abstract

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer.

The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer. One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness. Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95). The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

Abstract 5569: Quantification of tumor-associated vasculature as an imaging biomarker for monitoring the response of triple-negative breast cancer to neoadjuvant chemotherapy

Abstract Introduction: Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant systemic therapy (NAST). However, the response of TNBC to NAST is varied and a prognostic marker is still lacking. Since angiogenesis plays an important role in cancer progression, the quantification of changes in the tumor vasculature during treatment has the potential to provide useful information for characterizing the treatment response. We have recently developed a method to quantify changes in tumor-associated vasculature through a novel analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we investigated its ability to monitor the response of TNBC to NAST. Methods: Dynamic contrast-enhanced (DCE) MRI was acquired in TNBC patients (N = 50; 25 pathological complete response (pCR), 25 non-pCR) before, after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C), as part of the ARTEMIS trial (NCT02276433). Using DCE-MRI, we identified the tumor-associated vessels via a breast vasculature analysis. Specifically, the difference between pre- and post-contrast DCE-MRI images was enhanced by a histogram-based intensity transfer function. The 3D vasculature in the breast was then segmented by applying a Hessian filter. Given the vasculature segmented by the algorithms and the tumor masks segmented by radiologists, a lowest-cost tracking algorithm was used to automatically detect the vessels that are most likely to contact the tumor. These vessels are defined as tumor-associated vessels (TAV). The number of TAVs per patient was tabulated and the Wilcoxon rank sum test compared the TAV values before (V1), after 2 cycles (V2), and after 4 cycles of NAST (V3). Additionally, we compared the percent change of TAV from V1 to V3 between the pCR patients and non-pCR patients. Statistical significance was defined as P &amp;lt; 0.05. Results: A significant decrease in the number of TAVs was observed during the NAST. In particular, the number of TAVs has a median (interquartile range) of 15 (7 - 24), 7 (3 - 14), and 2 (0 - 8) at V1, V2, V3, respectively, which are (pair-wise) significantly different (P &amp;lt; 0.01). Moreover, pCR patients showed a significantly greater decrease in the number of TAVs as compared to non-pCR patients. The percent changes in the number of TAVs from V1 to V3 have a median (range) of -88.89% (-100.00% - -60.00%) and -66.67% (-87.85% - -40.88%) in the pCR and non-pCR patients, respectively (P &amp;lt; 0.05). Conclusion: These preliminary results demonstrate that tumor-associated vasculature may be a valuable imaging biomarker for monitoring the response of TNBC to NAST. Ongoing efforts include additional investigation of the TAVs beyond their number, as well as applying the analysis to more patients. Citation Format: Chengyue Wu, Casey E. Stowers, Zhan Xu, Ernesto A. B. F. Lima, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Quantification of tumor-associated vasculature as an imaging biomarker for monitoring the response of triple-negative breast cancer to neoadjuvant chemotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5569.

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions.

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) 'AverageIntensity', (2) 'Autocorrelation', (3) 'Contrast', (4) 'Compactness', (5) 'StandardDeviation', (6) 'MeanAbsoluteDeviation' and (7) 'InterquartileRange'. AUC values of 0.86 (95% C.I. 0.73-0.94) for the training set and 0.73 (95% C.I. 0.54-0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28-0.57) for the training set and 0.4 (0.23-0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.

Fluid mechanics approach to perfusion quantification: vasculature computational fluid dynamics simulation, quantitative transport mapping (QTM) analysis of dynamics contrast enhanced MRI, and application in nonalcoholic fatty liver disease classification.

We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude [Formula: see text] derived from QTM, liver arterial blood flow [Formula: see text], liver venous blood flow [Formula: see text], permeability [Formula: see text], blood volume [Formula: see text] and extravascular space volume [Formula: see text] averaged in liver ROI. In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. We propose to apply QTM method in liver DCE MRI perfusion quantification.

Comparative diagnostic performance of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging for differentiating clear cell and non-clear cell renal cell carcinoma.

To compare the diagnostic efficiency of contrast-enhanced ultrasound (CEUS) with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the differential diagnosis of clear and non-clear cell renal cell carcinoma, as confirmed by subsequent pathology. A total of 181 patients with 184 renal lesions diagnosed by both CEUS and DCE-MRI were enrolled in the study, including 136 clear cell renal cell carcinoma (ccRCC) and 48 non-clear cell renal cell carcinoma (non-ccRCC) tumors. All lesions were confirmed by histopathologic diagnosis after surgical resection. Interobserver agreement was estimated using a weighted kappa statistic. Diagnostic efficiency in evaluating ccRCC and non-ccRCC was compared between CEUS and DCE-MRI. The weighted kappa value for interobserver agreement was 0.746 to 0.884 for CEUS diagnosis and 0.764 to 0.895 for DCE-MRI diagnosis. Good diagnostic performance in differential diagnosis of ccRCC and non-ccRCC was displayed by both CEUS and DCE-MRI: sensitivity was 89.7% and 91.9%, respectively; specificity was 77.1% and 68.8%, respectively; and area under the receiver operating curve was 0.834 and 0.803, respectively. No statistically significant differences were present between the two methods (p = 0.54). Both CEUS and DCE-MRI imaging are effective for the differential diagnosis of ccRCC and non-ccRCC. Thus, CEUS could be an alternative to DCE-MRI as a first test for patients at risk of renal cancer, particularly where DCE-MRI cannot be carried out. • CEUS and DCE-MRI features can help differentiate ccRCC and non-ccRCC. • The differential diagnosis of ccRCC and non-ccRCC by CEUS is comparable to that of DCE-MRI. • Interobserver agreement is generally high using CEUS and DCE-MRI.