Early Antiangiogenic Activity of SU11248 Evaluated <i>In vivo</i> by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in an Experimental Model of Colon Carcinoma

Abstract Purpose: To compare two dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) techniques in terms of their ability in assessing the early antiangiogenic effect of SU11248, a novel selective multitargeted tyrosine kinase inhibitor, that exhibits direct antitumor and antiangiogenic activity via inhibition of the receptor tyrosine kinases platelet-derived growth factor receptor, vascular endothelial growth factor receptor, KIT, and FLT3. Experimental Design: A s.c. tumor model of HT29 human colon carcinoma in athymic mice was used. Two DCE-MRI techniques were used based, respectively, on macromolecular [Gd-diethylenetriaminepentaacetic acid (DTPA)-albumin] and low molecular weight (Gd-DTPA) contrast agents. The first technique provided a quantitative measurement of transendothelial permeability and fractional plasma volume, accepted surrogate markers of tumor angiogenesis. With the second technique, we quantified the initial area under the concentration-time curve, which gives information related to tumor perfusion and vascular permeability. Experiments were done before and 24 hours after a single dose administration of SU11248. Results: The early antiangiogenic effect of SU11248 was detected by DCE-MRI with macromolecular contrast agent as a 42% decrease in vascular permeability measured in the tumor rim. The effect was also detected by DCE-MRI done with Gd-DTPA as a 31% decrease in the initial area under the concentration-time curve. Histologic slices showed a statistically significant difference in mean vessel density between the treated and control groups. Conclusions: The early antiangiogenic activity of SU11248 was detected in vivo by DCE-MRI techniques using either macromolecular or low molecular weight contrast agents. Because DCE-MRI techniques with low molecular weight contrast agents can be used in clinical studies, these results could be relevant for the design of clinical trials based on new paradigms

The impact of tumor edema on T2-weighted 3T-MRI invasive breast cancer histological characterization: a pilot radiomics study

Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

Magnetic Resonance imaging Assessment of Tumor Microvessels and Response to Antiangiogenesis Therapy

Magnetic resonance Imaging (MRI) is a diagnostic modality with high inherent contrast resolution and multiplanar imaging capability. Advances in MR technology and image processing have increased the utility and availability of this technique in the past two decades. MRI has become one of the leading modalities in current diagnostic imaging, combining soft tissue contrast with high anatomic and temporal resolution. MRI is now a widely employed diagnostic method for the clinical evaluation of tumors. One of the most recent applications of MRI is the investigation of angiogenesis using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI represents the acquisition of serial MR images before, during, and after the administration of an intravenous contrast agent. The use of contrast enhancement in conjunction with magnetic resonance imaging provides a means to evaluate tissue function, as well as morphology. Tissue blood volume, blood flow, perfusion and capillary permeability represent indicators of the status of the vasculature that can be investigated with DCE-MRI. Use of such quantitation potentially allows tumors to be characterized in terms of pathophysiology and to be monitored over time, during the course of therapeutic interventions. The understanding of the angiogenesis process and the evaluation of new drugs that inhibit or stimulate angiogenesis are directly related to the development of an imaging assay of angiogenic activity. This method should provide functionally relevant and quantitative images, should be high in spatial resolution, should sample the entire tumor and could be repeated at frequent intervals. DCE-MRI has grown in importance with the development of antiangiogenic and neoadjuvant strategies for tumor therapy. Dedicated software makes it possible to interpret imaging pharmacokinetics and aid the assessment of physiological parameters such as capillary permeability and tissue perfusion. For instance, the permeability of functional tumor microvessels can be assessed noninvasively by dynamic MRI of contrast agent uptake in the tumor tissue (1-4). The analysis of contrast kinetics can be applied to differentiate between a malignant and a benign lesion and to determine whether a tumor is responding to treatment (5). It has been demonstrated that the permeability of blood vessels correlates with the ability of the tumor to metastasize, and with its response to treatment (6, 7). Thus, information concerning the status of vascular permeability might help assessing the metastatic potential of tumors and predict the sensitivity to chemotherapy or to antiangiogenic treatment

Quantification Of Vascular Parametric Indices Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-invasive method used to evaluate the biological activity in early clinical trials of novel drugs targeting the tumor vasculature using gadolinium-DTPA (Gd) as a contrast agent. However, it has some limitations, such as reproducibility, data acquisition times, the presence of noise, extracting contrast concentration, estimating T1 relaxation and estimating pharmacokinetic parameters. In this work, a new approach to used fixed T1(0) which provides more reproducible DCE results has been introduced. Using this new algorithm to quantify the vascular changes in DCE-MRI, a pre-clinical renal cell carcinoma (RCC) tumor model was used to demonstrate the ability of DCE-MRI to quantify the vascular changes induced by various doses of sunitinib in tumor-bearing kidneys and normal contralateral kidneys. Usually, only the first minute of data are used for processing to calculate the initial area under the curve (IAUC) and/or the median value of cumulative initial area under the curve (CIAUC) in order to monitor changes between pre and post drug treatment. However, in this work, the first two minutes was used to include the effect of the washout process of the kidneys. Moreover, DCE-MRI was used to investigate the vascular changes induced by pre-treatment with sunitinib in KCL-18 kidney tumors to schedule the initiation of chemotherapy. DCE results were confirmed with the histologic studies. In this thesis, several new measures of vascular properties have been introduced, including: the fraction of active pixels (FAP); contrast agent uptake to the peak (AUCtp); time to peak concentration (TTP); washout slope (Nslope); as well as full width half maximum (FWHM) of IAUC. The results from the pre-clinical RCC tumor model demonstrate that DCE parametric maps have the potential to assess the effect of antiangiogenic drugs on blood flow and physiological vascular changes in tumors as well as normal tissues. These new parametric maps provided further guidance as to what could be considered normal versus abnormal tissue response to antiangiogenic therapy. The results of this research should lead to a clear improvement in the ability of DCE-MRI as a quantitative method to evaluate tumor vasculature and other hemodynamic properties

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI

Functional Magnetic Resonance Imaging of Breast Cancer

This thesis examines the use of magnetic resonance imaging (MRI) techniques in the detection of breast cancer and the prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NACT). This thesis compares the diagnostic performance of diffusion-weighted imaging (DWI) models in the breast using a systematic review and meta-analysis. Advanced diffusion models have been proposed that may improve the performance of standard DWI using the apparent diffusion coefficient (ADC) to discriminate between malignant and benign breast lesions. Pooling the results from 73 studies, comparable diagnostic accuracy is shown using the ADC and parameters from the intra-voxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) models. This work highlights a lack of standardisation in DWI protocols and methodology. Conventional acquisition techniques used in DWI often suffer from image artefacts and low spatial resolution. A multi-shot DWI technique, multiplexed sensitivity encoding (MUSE), can improve the image quality of DWI. A MUSE protocol has been optimised through a series of phantom experiments and validated in 20 patients. Comparing MUSE to conventional DWI, statistically significant improvements are shown in distortion and blurring metrics and qualitative image quality metrics such as lesion conspicuity and diagnostic confidence, increasing the clinical utility of DWI. This thesis investigates the use of dynamic contrast-enhanced MRI (DCE-MRI) in the detection of breast cancer and the prediction of pCR. Abbreviated MRI (ABB-MRI) protocols have gained increasing attention for the detection of breast cancer, acquiring a shortened version of a full diagnostic protocol (FDP-MRI) in a fraction of the time, reducing the cost of the examination. The diagnostic performance of abbreviated and full diagnostic protocols is systematically compared using a meta-analysis. Pooling 13 studies, equivalent diagnostic accuracy is shown for ABB-MRI in cohorts enriched with cancers, and lower but not significantly different diagnostic performance is shown in screening cohorts. Higher order imaging features derived from pre-treatment DCE-MRI could be used to predict pCR and inform decisions regarding targeted treatment, avoiding unnecessary toxicity. Using data from 152 patients undergoing NACT, radiomics features are extracted from baseline DCE-MRI and machine learning models trained to predict pCR with moderate accuracy. The stability of feature selection using logistic regression classification is demonstrated and a comparison of models trained using features from different time points in the dynamic series demonstrates that a full dynamic series enables the most accurate prediction of pCR.GE Healthcare funded PhD Studentshi

Hyperpolarized carbon 13 MRI: clinical applications and future directions in oncology

Hyperpolarized carbon 13 MRI (13C MRI) is a novel imaging approach that can noninvasively probe tissue metabolism in both normal and pathologic tissues. The process of hyperpolarization increases the signal acquired by several orders of magnitude, allowing injected 13C-labeled molecules and their downstream metabolites to be imaged in vivo, thus providing real-time information on kinetics. To date, the most important reaction studied with hyperpolarized 13C MRI is exchange of the hyperpolarized 13C signal from injected [1-13C]pyruvate with the resident tissue lactate pool. Recent preclinical and human studies have shown the role of several biologic factors such as the lactate dehydrogenase enzyme, pyruvate transporter expression, and tissue hypoxia in generating the MRI signal from this reaction. Potential clinical applications of hyperpolarized 13C MRI in oncology include using metabolism to stratify tumors by grade, selecting therapeutic pathways based on tumor metabolic profiles, and detecting early treatment response through the imaging of shifts in metabolism that precede tumor structural changes. This review summarizes the foundations of hyperpolarized 13C MRI, presents key findings from human cancer studies, and explores the future clinical directions of the technique in oncology

Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p \u3c 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine

관내상피암의 유방보존술 후 국소재발암 발생 예측을 위한 종양주변실질의 역동자기공명영상 조영증가율 평가

학위논문 (석사)-- 서울대학교 대학원 : 의학과 영상의학 전공, 2013. 2. 조나리야.Purpose: To retrospectively investigate whether the signal enhancement ratio (SER) of the background parenchyma around the tumor on dynamic contrast enhanced MR imaging was associated with ipsilateral breast tumor recurrence (IBTR) in breast ductal carcinoma in situ (DCIS) patients after breast conserving surgery (BCS). Materials and Methods: Between 2004 and 2009, 215 consecutive women (median age, 47yearsrange, 24-74years) with pure DCIS (mean size, 2.90cm ± 1.99range, 0.2cm – 9.8cm) who underwent preoperative MRI, curative BCS and had at least 2-year follow-up data were identified. Their clinicopathologic features (age, menopausal status, surgery type, adjuvant therapy, ER, PR, HER2 status, nuclear grade, margin width) and MRI features [lesion size, morphology, fibroglandular density, background parenchymal enhancement grade, parenchymal SER defined as (Se-Sp)/(Sd-Sp), where Sp, Se, and Sd represent the signal intensity on the precontrast, early postcontrast, and delayed postcontrast images] were analyzed. Receiver operating characteristic curves were used to determine the best cut-off value of variables for the prediction of IBTR. The reproducibility of the SER measurements was evaluated by using the intraclass correlation coefficient (ICC). RFS was estimated using the Kaplan-Meier method. A multivariate Cox proportional hazards model was used to determine associations between survival outcome and MRI variables, adjusting for clinicopathologic variables. Results: There were 15 (7.0%, 15 of 215) ipsilateral breast tumor recurrences (9 DCIS, 6 invasive recurrences). The median follow-up period for the no recurrence group (n=200) was 48 months (range 27-100 months). The ICC between the two radiologists was 0.852 (95% confidence interval [CI]: 0.811, 0.885P < .001) for measurements of the SER, which indicates excellent agreement. On multivariate analysis, high mean background parenchymal SER around tumor was an independent factor associated with early IBTR : The hazard ratio (HR) for high SER were 17.837 (95% CI: 4.958, 64.472P< .001), and 10.136 (95% CI: 3.392, 30.288P< .001) for reader 1 and reader 2, respectively. Omission of the adjuvant endocrine therapy and larger size of tumor measured at surgical specimen were also found to be independent poor prognostic factors for IBTR on multivariate analysis. Conclusion: High SER in the background parenchyma around the tumor, omission of adjuvant endocrine therapy and larger tumor size at specimens were independent factor associated with IBTR in breast DCIS patients treated with BCS.I. INTRODUCTION 1 II.MATERIALS and METHODS 4 1. Study population 4 2. Prognostic variables 6 3. MR imaging technique 7 4. Image analysis 9 5. Statistical analysis 11 III. RESULTS 14 1. Patients characteristics 14 2. Reproducibility for SER measurements and ROC analysis 14 3. Survival analysis 15 IV. DISCUSSION 18 Reference 38 국문초록 48Maste