Deep Learning-Based Predictive Model for Revascularization of Chronic Total Occlusions on Angiographic Imaging.

Revascularization of chronic total occlusions (CTO) is currently one of the most complex procedures in percutaneous coronary intervention (PCI), requiring the use of specific devices and a high level of experience to obtain good results. Once the clinical indication for extensive ischemia or angina uncontrolled with medical treatment has been established, the decision to perform coronary intervention is not simple, since this procedure has a higher rate of complications than non-PCI percutaneous intervention, higher ionizing radiation doses and a lower success rate. However, CTO revascularization has been shown to be helpful in symptomatic improvement of angina, reduction of ischemic burden, or improvement of ejection fraction. The aim of this work is to determine whether a model developed using deep learning techniques, and trained with angiography images, can better predict the likelihood of a successful revascularization procedure for a patient with a chronic total occlusion (CTO) lesion in their coronary artery (measured as procedure success and the duration of time during which X-ray imaging technology is used to perform a medical procedure) than the scales traditionally used. As a preliminary approach, patients with right coronary artery CTO will be included since they present standard angiographic projections that are performed in all patients and present less technical variability (duration, projection angle, image similarity) among them.The ultimate objective is to develop a predictive model to help the clinician in the decision to intervene and to analyze the performance in terms of predicting the success of the technique for the revascularization of chronic occlusions.Clinical Relevance- The development of a deep learning model based on the angiography images could potentially overcome the gold standard and help interventional cardiologists in the treatment decision for percutaneous coronary intervention, maximizing the success rate of coronary intervention.

Breast Cancer Diagnosis Using Texture and Shape Features in MRI.

Contrast-enhanced magnetic resonance (MR) breast imaging represents a tool with great potential for the detection, evaluation and diagnosis of breast cancer (BC). Due to its high sensitivity and in combination with medical imaging biomarkers, it can overcome setbacks and limitations manifested in other diagnostic modalities such as mammography or ultrasound. In order to aid and assist clinicians in the diagnosis of BC, a methodology based on the extraction of 2D texture and 3D shape features in MR images is proposed. To categorize breast tumor malignancy, we considered its location in the coronal plane, divided into 4 quadrants (UOQ, UIQ, LOQ and LOQ), and the tumor type according to its genetic information (positive HER2 and Luminal B with negative HER2). In this regard, six different studies were conducted: one per feature type (texture and shape), as well as the combination of both features (texture + shape) for each of the two covariables (tumor type and location in the coronal plane). A dataset of 43 BC patients were considered. A radiomics approach was implemented extracting 43 texture and 17 shape features and using to train 5 different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naïve Bayes). The highest precision result for the tumor type study (74.04% in terms of AUC) was obtained with 43 texture features. Whereas for the quadrant localization study, the highest precision result (67.99% AUC) was obtained as a combination of 3 textures and shape features. Both results were achieved with the SVM with Linear Kernel classification model.Clinical Relevance- This work emphasizes the use of quantitative biomarkers as texture and shape features in combination with machine learning techniques to aid in breast tumor malignancy diagnosis on MR imaging. Moreover, considering the location of the tumor in the coronal plane and its type according to its genetic information may improve the selection of appropriate treatments, survival rate, and quality of life for breast cancer patients.

Multiclass Classification of Prostate Tumors Following an MR Image Analysis-Based Radiomics Approach.

Prostate cancer is one of the most common cancers in men, with symptoms that may be confused with those caused by benign prostatic hyperplasia. One of the key aspects of treating prostate cancer is its early detection, increasing life expectancy and improving the quality of life of those patients. However, the tests performed are often invasive, resulting in a biopsy. A non-invasive alternative is the magnetic resonance imaging (MRI)-based PI-RADS v2 classification. The aim of this work was to find objective biomarkers that allow the PI-RADS classification of prostate lesions using a radiomics approach on Multiparametric MRI. A total of 90 subjects were analyzed. From each segmented lesion, 609 different texture features were extracted using five different statistical methods. Two feature selection methods and eight multiclass predictive models were evaluated. This was a multiclass study in which the best AUC result was 0.7442 ± 0.0880, achieved with the Naïve Bayes model using a subset of 120 features. Valuable results were also obtained using the Random Forests model, obtaining an AUC of 0.7394 ± 0.0965 with a lower number of features (52). Clinical Relevance- The current study establishes a methodology for classifying prostate cancer and supporting clinical decision-making in a fast and efficient manner and avoiding additional invasive procedures using MRI.

Development of an Image-Based Methodology for the Evaluation of Histopathological Features in Human Meningioma.

Meningioma is the most common intracranial tumor in adulthood. With a clear female predominance and a recurrence rate that reaches 20%, it is, despite being considered a benign tumor, a pathology that greatly compromises post-diagnosis quality of life. Its prone to recur or progress to a higher degree is difficult to predict in the absence of obvious histological criteria. This project aims to develop an automatic methodology to aid in the diagnosis of meningiomas that is objective and easily reproducible. The methodology is based on histopathological image analysis using artificial intelligence and machine learning algorithms. It includes a semi-automatic process of identification and cleaning of the scanned samples, an automatic detection of the nuclei of each image and, finally, the parameterization of the samples. The obtained data together with the clinical information will be analyzed using statistical methods in order to provide a methodology to support clinical diagnosis and decision-making in patient management. The result is the development of an effective methodology that generates a set of data associated with morphological parameters with different trends according to the pathological groups studied. A tool has been developed that allows an effective semiautomatic analysis of the images to evaluate these parameters in an objective and reproducible way, helping in clinical decision-making and facilitating to undertake projects with large sample series. Clinical Relevance- The main contribution of this project is in the field of neuropathology, for the diagnosis of meningiomas, the most common brain tumor. The present project provides an objective and quantifiable prognosis methodology for the meningiomas, offering a more precise monitoring of the treatment applied to the patient, resulting in a better prognosis and better quality of life.

Short-Term Changes in Left and Right Ventricular Cardiac Magnetic Resonance Feature Tracking Strain Following Ferric Carboxymaltose in Patients With Heart Failure: A Substudy of the Myocardial-IRON Trial.

Background The mechanisms explaining the clinical benefits of ferric carboximaltose (FCM) in patients with heart failure, reduced or intermediate left ventricular ejection fraction, and iron deficiency remain not fully clarified. The Myocardial-IRON trial showed short-term cardiac magnetic resonance (CMR) changes suggesting myocardial iron repletion following administration of FCM but failed to find a significant increase in left ventricular ejection fraction in the whole sample. Conversely, the strain assessment could evaluate more specifically subtle changes in contractility. In this subanalysis, we aimed to evaluate the effect of FCM on the short-term left and right ventricular CMR feature tracking derived strain. Methods and Results This is a post hoc subanalysis of the double-blind, placebo-controlled, randomized clinical trial that enrolled 53 ambulatory patients with heart failure and left ventricular ejection fraction <50%, and iron deficiency [Myocardial-IRON trial (NCT03398681)]. Three-dimensional left and 2-dimensional right ventricular CMR tracking strain (longitudinal, circumferential, and radial) changes were evaluated before, 7 and 30 days after randomization using linear mixed-effect analysis. The median (interquartile range) age of the sample was 73 years (65-78), and 40 (75.5%) were men. At baseline, there were no significant differences in CMR feature tracking strain parameters across both treatment arms. At 7 days, the only global 3-dimensional left ventricular circumferential strain was significantly higher in the FCM treatment-arm (difference: -1.6%, P=0.001). At 30 days, and compared with placebo, global 3-dimensional left ventricular strain parameters significantly improved in those allocated to FCM treatment-arm [longitudinal (difference: -2.3%, P<0.001), circumferential (difference: -2.5%, P<0.001), and radial (difference: 4.2%, P=0.002)]. Likewise, significant improvements in global right ventricular strain parameters were found in the active arm at 30 days (longitudinal [difference: -3.3%, P=0.010], circumferential [difference: -4.5%, P<0.001], and radial [difference: 4.5%, P=0.027]). Conclusions In patients with stable heart failure, left ventricular ejection fraction <50%, and iron deficiency, treatment with FCM was associated with short-term improvements in left and right ventricular function assessed by CMR feature tracking derived strain parameters. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03398681.

Skin Temperature Assessment During Lumbar Sympathetic Blocks by Infrared Thermography.

Complex Regional Pain Syndrome (CRPS) is a pain disorder that can be triggered by injuries or surgery affecting most often limbs. Its multifaceted pathophysiology makes its diagnosis and treatment a challenging work. To reduce pain, patients diagnosed with CRPS commonly undergo sympathetic blocks which involves the injection of a local anesthetic drug around the nerves. Currently, this procedure is guided by fluoroscopy which occasionally is considered as little accurate. For this reason, the use of infrared thermography as a technique of support has been considered.In this work, thermal images of feet soles in patients with lower limbs CRPS undergoing lumbar sympathetic blocks were recorded and evaluated. The images were analyzed by means of a computer-aided intuitive software tool developed using MATLAB. This tool provides the possibility of editing regions of interest, extracting the most important information of these regions and exporting the results data to an Excel file.Clinical Relevance- The final purpose of this work is to value the potential of infrared thermography and the analysis of its images as an intraoperatory technique of support in lumbar sympathetic blocks in patients with lower limbs CRPS.

Automatic Multi-Atlas Liver Segmentation and Couinaud Classification from CT Volumes.

Primary Live Cancer (PLC) is the sixth most common cancer worldwide and its occurrence predominates in patients with chronic liver diseases and other risk factors like hepatitis B and C. Treatment of PLC and malignant liver tumors depend both in tumor characteristics and the functional status of the organ, thus must be individualized for each patient. Liver segmentation and classification according to Couinaud's classification is essential for computer-aided diagnosis and treatment planning, however, manual segmentation of the liver volume slice by slice can be a time-consuming and challenging task and it is highly dependent on the experience of the user. We propose an alternative automatic segmentation method that allows accuracy and time consumption amelioration. The procedure pursues a multi-atlas based classification for Couinaud segmentation. Our algorithm was implemented on 20 subjects from the IRCAD 3D data base in order to segment and classify the liver volume in its Couinaud segments, obtaining an average DICE coefficient of 0.94.Clinical Relevance- The final purpose of this work is to provide an automatic multi-atlas liver segmentation and Couinaud classification by means of CT image analysis.

Determination of Non-Invasive Biomarkers for the Assessment of Fibrosis, Steatosis and Hepatic Iron Overload by MR Image Analysis. A Pilot Study.

The reference diagnostic test of fibrosis, steatosis, and hepatic iron overload is liver biopsy, a clear invasive procedure. The main objective of this work was to propose HSA, or human serum albumin, as a biomarker for the assessment of fibrosis and to study non-invasive biomarkers for the assessment of steatosis and hepatic iron overload by means of an MR image acquisition protocol. It was performed on a set of eight subjects to determine fibrosis, steatosis, and hepatic iron overload with four different MRI sequences. We calibrated longitudinal relaxation times (T1 [ms]) with seven human serum albumin (HSA [%]) phantoms, and we studied the relationship between them as this protein is synthesized by the liver, and its concentration decreases in advanced fibrosis. Steatosis was calculated by means of the fat fraction (FF [%]) between fat and water liver signals in "fat-only images" (the subtraction of in-phase [IP] images and out-of-phase [OOP] images) and in "water-only images" (the addition of IP and OOP images). Liver iron concentration (LIC [µmol/g]) was obtained by the transverse relaxation time (T2* [ms]) using Gandon's method with multiple echo times (TE) in T2-weighted IP and OOP images. The preliminary results showed that there is an inverse relationship (r = -0.9662) between the T1 relaxation times (ms) and HSA concentrations (%). Steatosis was determined with FF > 6.4% and when the liver signal was greater than the paravertebral muscles signal, and thus, the liver appeared hyperintense in fat-only images. Hepatic iron overload was detected with LIC > 36 µmol/g, and in these cases, the liver signal was smaller than the paravertebral muscles signal, and thus, the liver behaved as hypointense in IP images.

Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach.

PURPOSE: To evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach. METHODS: This retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results. RESULTS: Highest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations. CONCLUSION: The proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.

A Ciliary Motility Index for Activity Measurement in Cell Cultures With Respiratory Syncytial Virus.

BACKGROUND: The respiratory epithelium is frequently infected by the respiratory syncytial virus, resulting in inflammation, a reduction in cilia activity and an increase in the production of mucus. METHODS: In this study, an automatic method has been proposed to characterize the ciliary motility from cell cultures by means of a motility index using a dense optical flow algorithm. This method allows us to determine the ciliary beat frequency (CBF) together with a ciliary motility index of the cells in the cultures. The object of this analysis is to automatically distinguish between normal and infected cells in a culture. RESULTS: The method was applied in 2 stages. It was concluded from the first stage that the CBF is not a good enough indicator to discriminate between the control and infected cultures. However, the ciliary motility index does succeed in discriminating between the control and infected cultures using the t test with a value t = 6.46 and P < .001. In the second stage, it has been shown that the ciliary motility index did not differ significantly between patients, and the analysis of variance test gives α = 0.05, F = 1.61, P = .20. A threshold for this index has been determined using a receiver operating characteristics analysis that gives an area under the curve of 0.93. CONCLUSIONS: We have obtained a ciliary motility index that is able to discriminate between control and infected cultures after the eighth postinfection day. After infection, there is a rapid cilia loss of the cells and the measured CBF corresponds to the remaining noninfected cells. This is why the CBF does not discriminate between the control and the infected cells.

Open Source 3D Printed Lung Tumor Movement Simulator for Radiotherapy Quality Assurance.

In OECD (Organization for Economic Co-operation and Development) countries, cancer is one of the main causes of death, lung cancer being one of the most aggressive. There are several techniques for the treatment of lung cancer, among which radiotherapy is one of the most effective and least invasive for the patient. However, it has associated difficulties due to the moving target tumor. It is possible to reduce the side effects of radiotherapy by effectively tracking a tumor and reducing target irradiation margins. This paper presents a custom electromechanical system that follows the movement of a lung tumor. For this purpose, a hysteresis loop of human lung movement during breathing was studied to obtain its characteristic movement equation. The system is controlled by an Arduino, steppers motors and a customized 3D printed mechanism to follow the characteristic human breathing, obtaining an accurate trajectory. The developed device helps the verification of individualized radiation treatment plans and permits the improvement of radiotherapy quality assurance procedures.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

OBJECTIVE: To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. METHODS: Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. RESULTS: In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). CONCLUSION: Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. KEY POINTS: • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution.

OBJECTIVE: To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA). MATERIALS AND METHODS: Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps. RESULTS: For LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA. CONCLUSION: Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.

A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma.

Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictive models were evaluated using a nested cross-validation scheme. The best classification results were achieved using 3D texture features for all the models, obtaining an average AUC > 0.9 in all cases and an AUC = 0.947 ± 0.067 when using the best model (naïve Bayes).

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression.

PURPOSE: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. METHODS: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine has been developed. In this way, a probability map is generated and deformed to be adapted to each testing case. RESULTS: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC), the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final average of 15.51 ± 2.74 mm. Also, a global value of 91.01 ± 3.18% in terms of DSC and a MSD of 0.66 ± 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic region, as ribs were almost perfectly suppressed. CONCLUSION: The study demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.

Opportunistic screening for osteoporosis by routine CT in Southern Europe.

Feasibility evaluation of early detection of osteoporosis in oncologic patients by bone mineral density (BMD) on abdominal computed tomography (CT) scans performed for other clinical indications, by using dual-energy X-ray absorptiometry (DXA) as reference. Abdominal CT images can identify patients with osteoporosis BMD without additional radiation exposure or cost. INTRODUCTION: The purpose of the study is to evaluate the feasibility of early detection of osteoporosis by bone mineral density (BMD) on abdominal computed tomography (CT) scans performed in oncologic patients, comparing calibrated and uncalibrated measurements by using dual-energy X-ray absorptiometry (DXA) as reference. We also performed an external validation of a threshold of 160 Hounsfield units (HU), proposed as highly sensitive. METHODS: Cohort comprised CT-DXA pairs within a 6-month period performed for any indication on 326 consecutive adults, aged 62.4 ± 12.38 years (mean ± standard deviation). CT attenuation of trabecular bone in HU was measured at the axial cross sections of L1, L2, L3, and L4 vertebrae. Vertebral compression fractures were assessed by sagittal reconstruction view. Diagnostic performance measures and the area under the receiver operator characteristic curve (AUC) for diagnosing osteoporosis were calculated. RESULTS: BMD values were statistical significantly lower at any vertebral level from L1 to L4 for patients with osteoporosis defined by DXA (p < 0.001). Calibrated and uncalibrated BMD values were significantly correlated (R 2 = 0.833, p < 0.01). An uncalibrated L1 CT attenuation threshold of 160 HU was more than 90 % sensitive, and a threshold of 73 HU was more than 90 % specific for distinguishing osteoporosis BMD. Fifty-nine percent of patients with vertebral compression fracture had non-osteoporotic DXA T-scores. CONCLUSIONS: Abdominal CT images obtained for other reasons can identify patients with osteoporosis BMD without additional radiation exposure or cost. Uncalibrated values at L1 can detect more osteoporosis patients with spinal compression fractures than DXA in oncologic patients.

Brain metastases detection on MR by means of three-dimensional tumor-appearance template matching.

PURPOSE: To develop and evaluate a method for an automatic detection of brain metastases in MR images. MATERIALS AND METHODS: Nineteen patients were scanned using a 1.5 Tesla MR scanner. Two radiologists and a radiation oncologist marked the location of the brain metastases. The training group consisted of eight patients harboring 20 metastases. First, three-dimensional (3D) tumor-appearance templates were cross-correlated with MR brain images to evaluate their similarity, and a correlation threshold was established for metastasis candidates. Afterward, a method to reduce false positive rate (FPR) was applied: each detected object was segmented and its degree of anisotropy (DA) was obtained, removing the elongated structures with a DA above the optimal value from the receiver operating characteristic curve. Finally, the method was statistically validated in two groups: 11 patients with 42 brain metastases and 11 patients without metastases. RESULTS: The method led to a sensitivity of 80% and an FPR per slice of 0.023 and 2.75 per patient in the training group. In the first validation group, a sensitivity of 88.10% and an FPR per slice of 0.05 corresponding to 6.91 false positives per patient were obtained. DA implementation decreased 3.5 times FPR compared with templates alone. It improved the radiologist's performance in metastases less than 10 mm from 89-93% to 100%. In the second validation group the FPR was 0.04 per slice and 5.18 per patient. CONCLUSION: This method demonstrates that 3D template matching applying DA technique has high sensitivity and low FPR for detecting brain metastases in MR images. J. Magn. Reson. Imaging 2016;44:642-652.

Neurosurgery planning in rodents using a magnetic resonance imaging assisted framework to target experimentally defined networks.

BACKGROUND AND OBJECTIVE: Meaningful targeting of brain structures is required in a number of experimental designs in neuroscience. Current technological developments as high density electrode arrays for parallel electrophysiological recordings and optogenetic tools that allow fine control of activity in specific cell populations provide powerful tools to investigate brain physio-pathology. However, to extract the maximum yield from these fine developments, increased precision, reproducibility and cost-efficiency in experimental procedures is also required. METHODS: We introduce here a framework based on magnetic resonance imaging (MRI) and digitized brain atlases to produce customizable 3D-environments for brain navigation. It allows the use of individualized anatomical and/or functional information from multiple MRI modalities to assist experimental neurosurgery planning and in vivo tissue processing. RESULTS: As a proof of concept we show three examples of experimental designs facilitated by the presented framework, with extraordinary applicability in neuroscience. CONCLUSIONS: The obtained results illustrate its feasibility for identifying and selecting functionally and/or anatomically connected neuronal population in vivo and directing electrode implantations to targeted nodes in the intricate system of brain networks.

Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI.

PURPOSE: To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis. METHODS: Texture features were extracted from 115 lesions: 32 of them previously diagnosed as radiation necrosis, 23 as radiation-treated metastasis and 60 untreated metastases; including a total of 179 features derived from six texture analysis methods. A feature selection technique based on support vector machine was used to obtain a subset of features that provide optimal performance. RESULTS: The highest classification accuracy evaluated over test sets was achieved with a subset of ten features when the untreated metastases were not considered; and with a subset of seven features when the classifier was trained with untreated metastases and tested on treated ones. Receiver operating characteristic curves provided area-under-the-curve (mean ± standard deviation) of 0.94 ± 0.07 in the first case, and 0.93 ± 0.02 in the second. CONCLUSION: High classification accuracy (AUC > 0.9) was obtained using texture features and a support vector machine classifier in an approach based on conventional MRI to differentiate between brain metastasis and radiation necrosis.

Functional diffusion map: A biomarker of brain metastases response to treatment based on magnetic resonance image analysis.

Validated biomarkers for treatment response in patients suffering from brain metastases are needed in daily clinical practice as they may improve survival by providing reliable prognostic information and allowing alternative therapies. This work presents a new analysis tool for an early and non-invasive evaluation of treatment response in patients with brain metastases. A set of twenty-five metastases from sixteen patients were examined by T1-weighted and diffusion magnetic resonance imaging before starting radiotherapy and at least once after treatment. Diffusion MRI can show a correlation between water diffusion variation within metastasis area and its clinical evolution. Images were co-registered to pretreatment scans. Diffusion changes, resulting in spatially varying changes in apparent diffusion coefficient values of metastatic lesions, were quantified and presented as a functional diffusion map (fDM). These functional maps were compared to two traditional criteria for assessing oncological response. Of the twenty-five metastases analyzed, seven were classified as partial response (PR), eight as stable disease (SD) and nine as progressive disease (PD). Normalized volume values of the metastases for each response group were obtained, disclosing that apparent diffusion coefficient increase was a good predictor of response. Sensitivity was 88%, specificity 100%, positive predictive value 100% and negative predictive value was 94%. Outcome reveals that the implemented tool, based on functional diffusion mapping as evolution biomarker, provides a reliable prediction of metastases response to treatment.