Marco C. Pinho

Improved tumor oxygenation and survival in glioblastoma patients who show increased blood perfusion after cediranib and chemoradiation

Significance This study demonstrates that antiangiogenic therapy increases tumor blood perfusion in a subset of newly diagnosed glioblastoma patients, and that it is these patients who survive longer when this expensive and potentially toxic therapy is combined with standard radiation and chemotherapy. This study provides fresh insights into the selection of glioblastoma patients most likely to benefit from antiangiogenic treatments. Antiangiogenic therapy has shown clear activity and improved survival benefit for certain tumor types. However, an incomplete understanding of the mechanisms of action of antiangiogenic agents has hindered optimization and broader application of this new therapeutic modality. In particular, the impact of antiangiogenic therapy on tumor blood flow and oxygenation status (i.e., the role of vessel pruning versus normalization) remains controversial. This controversy has become critical as multiple phase III trials of anti-VEGF agents combined with cytotoxics failed to show overall survival benefit in newly diagnosed glioblastoma (nGBM) patients and several other cancers. Here, we shed light on mechanisms of nGBM response to cediranib, a pan-VEGF receptor tyrosine kinase inhibitor, using MRI techniques and blood biomarkers in prospective phase II clinical trials of cediranib with chemoradiation vs. chemoradiation alone in nGBM patients. We demonstrate that improved perfusion occurs only in a subset of patients in cediranib-containing regimens, and is associated with improved overall survival in these nGBM patients. Moreover, an increase in perfusion is associated with improved tumor oxygenation status as well as with pharmacodynamic biomarkers, such as changes in plasma placenta growth factor and sVEGFR2. Finally, treatment resistance was associated with elevated plasma IL-8 and sVEGFR1 posttherapy. In conclusion, tumor perfusion changes after antiangiogenic therapy may distinguish responders vs. nonresponders early in the course of this expensive and potentially toxic form of therapy, and these results may provide new insight into the selection of glioblastoma patients most likely to benefit from anti-VEGF treatments.

Basic MR relaxation mechanisms and contrast agent design

The diagnostic capabilities of magnetic resonance imaging (MRI) have undergone continuous and substantial evolution by virtue of hardware and software innovations and the development and implementation of exogenous contrast media. Thirty years since the first MRI contrast agent was approved for clinical use, a reliance on MR contrast media persists, largely to improve image quality with higher contrast resolution and to provide additional functional characterization of normal and abnormal tissues. Further development of MR contrast media is an important component in the quest for continued augmentation of diagnostic capabilities. In this review we detail the many important considerations when pursuing the design and use of MR contrast media. We offer a perspective on the importance of chemical stability, particularly kinetic stability, and how this influences ones thinking about the safety of metal–ligand‐based contrast agents. We discuss the mechanisms involved in MR relaxation in the context of probe design strategies. A brief description of currently available contrast agents is accompanied by an in‐depth discussion that highlights promising MRI contrast agents in the development of future clinical and research applications. Our intention is to give a diverse audience an improved understanding of the factors involved in developing new types of safe and highly efficient MR contrast agents and, at the same time, provide an appreciation of the insights into physiology and disease that newer types of responsive agents can provide. J. Magn. Reson. Imaging 2015;42:545–565.

Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study

BACKGROUND AND PURPOSE: Despite availability of advanced imaging, distinguishing radiation necrosis from recurrent brain tumors noninvasively is a big challenge in neuro-oncology. Our aim was to determine the feasibility of radiomic (computer-extracted texture) features in differentiating radiation necrosis from recurrent brain tumors on routine MR imaging (gadolinium T1WI, T2WI, FLAIR). MATERIALS AND METHODS: A retrospective study of brain tumor MR imaging performed 9 months (or later) post-radiochemotherapy was performed from 2 institutions. Fifty-eight patient studies were analyzed, consisting of a training (n = 43) cohort from one institution and an independent test (n = 15) cohort from another, with surgical histologic findings confirmed by an experienced neuropathologist at the respective institutions. Brain lesions on MR imaging were manually annotated by an expert neuroradiologist. A set of radiomic features was extracted for every lesion on each MR imaging sequence: gadolinium T1WI, T2WI, and FLAIR. Feature selection was used to identify the top 5 most discriminating features for every MR imaging sequence on the training cohort. These features were then evaluated on the test cohort by a support vector machine classifier. The classification performance was compared against diagnostic reads by 2 expert neuroradiologists who had access to the same MR imaging sequences (gadolinium T1WI, T2WI, and FLAIR) as the classifier. RESULTS: On the training cohort, the area under the receiver operating characteristic curve was highest for FLAIR with 0.79; 95% CI, 0.77–0.81 for primary (n = 22); and 0.79, 95% CI, 0.75–0.83 for metastatic subgroups (n = 21). Of the 15 studies in the holdout cohort, the support vector machine classifier identified 12 of 15 studies correctly, while neuroradiologist 1 diagnosed 7 of 15 and neuroradiologist 2 diagnosed 8 of 15 studies correctly, respectively. CONCLUSIONS: Our preliminary results suggest that radiomic features may provide complementary diagnostic information on routine MR imaging sequences that may improve the distinction of radiation necrosis from recurrence for both primary and metastatic brain tumors.

Proton T2 measurement and quantification of lactate in brain tumors by MRS at 3 Tesla in vivo

To evaluate the T2 relaxation time of lactate (Lac) in brain tumors and the correlation of the T2 and concentration with tumor grades.

Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL

Assessment of brain hemodynamics without exogenous contrast agents is of increasing importance in clinical applications. This study aims to develop an MR perfusion technique that can provide noncontrast and multiparametric estimation of hemodynamic markers.

A generic support vector machine model for preoperative glioma survival associations.

PURPOSE To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) imaging-based blood volume distribution data for preoperative glioma survival associations and to prospectively evaluate the diagnostic effectiveness of this model in autonomous patient data. MATERIALS AND METHODS Institutional and regional medical ethics committees approved the study, and all patients signed a consent form. Two hundred thirty-five preoperative adult patients from two institutions with a subsequent histologically confirmed diagnosis of glioma after surgery were included retrospectively. An SVM learning technique was applied to MR imaging-based whole-tumor relative cerebral blood volume (rCBV) histograms. SVM models with the highest diagnostic accuracy for 6-month and 1-, 2-, and 3-year survival associations were trained on 101 patients from the first institution. With Cox survival analysis, the diagnostic effectiveness of the SVM models was tested on independent data from 134 patients at the second institution. RESULTS were adjusted for known survival predictors, including patient age, tumor size, neurologic status, and postsurgery treatment, and were compared with survival associations from an expert reader. RESULTS Compared with total qualitative assessment by an expert reader, the whole-tumor rCBV-based SVM model was the strongest parameter associated with 6-month and 1-, 2-, and 3-year survival in the independent patient data (area under the receiver operating characteristic curve, 0.794-0.851; hazard ratio, 5.4-21.2). DISCUSSION Machine learning by means of SVM in combination with whole-tumor rCBV histogram analysis can be used to identify early patient survival in aggressive gliomas. The SVM model returned higher diagnostic accuracy values than an expert reader, and the model appears to be insensitive to patient, observer, and institutional variations.

Detection of 2‐hydroxyglutarate in brain tumors by triple‐refocusing MR spectroscopy at 3T in vivo

To test the efficacy of triple‐refocusing MR spectroscopy (MRS) for improved detection of 2‐hydroxyglutarate (2HG) in brain tumors at 3T in vivo.

In vivo detection of 2-hydroxyglutarate in brain tumors by optimized point-resolved spectroscopy (PRESS) at 7T

To test the efficacy of 7T MRS for in vivo detection of 2‐hydroxyglutarate (2HG) in brain tumors.

Cerebrovascular reactivity mapping without gas challenges

Abstract Cerebrovascular reactivity (CVR), the ability of cerebral vessels to dilate or constrict, has been shown to provide valuable information in the diagnosis and treatment evaluation of patients with various cerebrovascular conditions. CVR mapping is typically performed using hypercapnic gas inhalation as a vasoactive challenge while collecting BOLD images, but the inherent need of gas inhalation and the associated apparatus setup present a practical obstacle in applying it in routine clinical use. Therefore, we aimed to develop a new method to map CVR using resting‐state BOLD data without the need of gas inhalation. This approach exploits the natural variation in respiration and measures its influence on BOLD MRI signal. In this work, we first identified a surrogate of the arterial CO2 fluctuation during spontaneous breathing from the global BOLD signal. Second, we tested the feasibility and reproducibility of the proposed approach to use the above‐mentioned surrogate as a regressor to estimate voxel‐wise CVR. Third, we validated the “resting‐state CVR map” with a conventional CVR map obtained with hypercapnic gas inhalation in healthy volunteers. Finally, we tested the utility of this new approach in detecting abnormal CVR in a group of patients with Moyamoya disease, and again validated the results using the conventional gas inhalation method. Our results showed that global BOLD signal fluctuation in the frequency range of 0.02–0.04 Hz contains the most prominent contribution from natural variation in arterial CO2. The CVR map calculated using this signal as a regressor is reproducible across runs (ICC=0.91±0.06), and manifests a strong spatial correlation with results measured with a conventional hypercapnia‐based method in healthy subjects (r=0.88, p<0.001). We also found that resting‐state CVR was able to identify vasodilatory deficit in patients with steno‐occlusive disease, the spatial pattern of which matches that obtained using the conventional gas method (r=0.71±0.18). These results suggest that CVR obtained with resting‐state BOLD may be a useful alternative in detecting vascular deficits in clinical applications when gas challenge is not feasible. HighlightsA new method was proposed to map CVR without gas inhalation or breath‐holding.This method exploits natural variations in respiration and their effect on BOLD signal.The resulted CVR maps were reproducible and consistent with CO2‐inhalation CVR maps.This method can identify CVR deficit in patients with steno‐occlusive disease.This method may be a useful alternative to map CVR when gas challenges is not feasible.

Machine learning in preoperative glioma MRI: Survival associations by perfusion‐based support vector machine outperforms traditional MRI

To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion‐based dynamic susceptibility contrast magnetic resonance imaging (DSC‐MRI) for preoperative survival associations in patients with gliomas and compare our results to traditional MRI.