A protein corona sensor array detects breast and prostate cancers.

Following exposure to human plasma (HP), nanoparticles (NPs) are coated with a biomolecular layer referred to as a protein corona. We recently revealed that characterizing the protein coronas of various NPs may provide a unique opportunity for cancer identification and discrimination. In other words, protein corona profiles of several NPs, when being analyzed using classifiers, would provide a unique "fingerprint" for each type of disease. Here, we probed the capacity of the protein corona for the identification and discrimination of breast and prostate cancer patients from healthy individuals. Using three lipid NP formulations with distinct physical-chemical properties as a cross-reactive sensor array and a supervised random forest classifier, we identified a set of proteins that showed a significant difference in cancer patients and control subjects. Our data show that many of the corona proteins with the highest discrimination ability between oncological patients and healthy individuals are related to cellular and molecular aspects of breast and prostate cancers.

Super-Resolution Whole-Brain 3D MR Spectroscopic Imaging for Mapping D-2-Hydroxyglutarate and Tumor Metabolism in Isocitrate Dehydrogenase 1-mutated Human Gliomas.

Background Isocitrate dehydrogenase (IDH) mutations are highly frequent in glioma, producing high levels of the oncometabolite D-2-hydroxyglutarate (D-2HG). Hence, D-2HG represents a valuable imaging marker for IDH-mutated human glioma. Purpose To develop and evaluate a super-resolution three-dimensional (3D) MR spectroscopic imaging strategy to map D-2HG and tumor metabolism in IDH-mutated human glioma. Materials and Methods Between March and September 2018, participants with IDH1-mutated gliomas and healthy participants were prospectively scanned with a 3-T whole-brain 3D MR spectroscopic imaging protocol optimized for D-2HG. The acquired D-2HG maps with a voxel size of 5.2 × 5.2 × 12 mm were upsampled to a voxel size of 1.7 × 1.7 × 3 mm using a super-resolution method that combined weighted total variation, feature-based nonlocal means, and high-spatial-resolution anatomic imaging priors. Validation with simulated healthy and patient data and phantom measurements was also performed. The Mann-Whitney U test was used to check that the proposed super-resolution technique yields the highest peak signal-to-noise ratio and structural similarity index. Results Three participants with IDH1-mutated gliomas (mean age, 50 years ± 21 [standard deviation]; two men) and three healthy participants (mean age, 32 years ± 3; two men) were scanned. Twenty healthy participants (mean age, 33 years ± 5; 16 men) underwent a simulation of upsampled MR spectroscopic imaging. Super-resolution upsampling improved peak signal-to-noise ratio and structural similarity index by 62% (P < .05) and 7.3% (P < .05), respectively, for simulated data when compared with spline interpolation. Correspondingly, the proposed method significantly improved tissue contrast and structural information for the acquired 3D MR spectroscopic imaging data. Conclusion High-spatial-resolution whole-brain D-2-hydroxyglutarate imaging is possible in isocitrate dehydrogenase 1-mutated human glioma by using a super-resolution framework to upsample three-dimensional MR spectroscopic images acquired at lower resolution. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Huang and Lin in this issue.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Contribution of Quantitative Amygdalar MR FLAIR Signal Analysis for Lateralization of Mesial Temporal Lobe Epilepsy.

BACKGROUND AND PURPOSE: This study evaluates the contribution of an automated amygdalar fluid-attenuated inversion recovery (FLAIR) signal analysis for the lateralization of mesial temporal lobe epilepsy (mTLE). METHODS: Sixty-nine patients (27 M, 42 F) who had undergone surgery and achieved an Engel class Ia postoperative outcome were identified as a pure cohort of mTLE cases. Forty-six nonepileptic subjects comprised the control group. The amygdala was segmented in T1-weighted images using an atlas-based segmentation. The right/left ratios of amygdalar FLAIR mean and standard deviation were calculated for each subject. A linear classifier (ie, discriminator line) was designed for lateralization using the FLAIR features and a boundary domain, within which lateralization was assumed to be less definitive, was established using the same features from control subjects. Hippocampal FLAIR and volume analysis was performed for comparison. RESULTS: With the boundary domain in place, lateralization accuracy was found to be 70% with hippocampal FLAIR and 67% with hippocampal volume. Taking amygdalar analysis into account, 22% of cases that were found to have uncertain lateralization by hippocampal FLAIR analysis were confidently lateralized by amygdalar FLAIR. No misclassified case was found outside the amygdalar FLAIR boundary domain. CONCLUSIONS: Amygdalar FLAIR analysis provides an additional metric by which to establish mTLE in those cases where hippocampal FLAIR and volume analysis have failed to provide lateralizing information.

Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients.

BACKGROUND: Gliomas with mutant isocitrate dehydrogenase (IDH) produce high levels of 2-hydroxyglutarate (2HG) that can be quantitatively measured by 3D magnetic resonance spectroscopic imaging (MRSI). Current glioma MRI primarily relies upon fluid-attenuated inversion recovery (FLAIR) hyperintensity for treatment planning, although this lacks specificity for tumor cells. Here, we investigated the relationship between 2HG and FLAIR in mutant IDH glioma patients to determine whether 2HG mapping is valuable for radiotherapy planning. METHODS: Seventeen patients with mutant IDH1 gliomas were imaged by 3 T MRI. A 3D MRSI sequence was employed to specifically image 2HG. FLAIR imaging was performed using standard clinical protocol. Regions of interest (ROIs) were determined for FLAIR and optimally thresholded 2HG hyperintensities. The overlap, displacement, and volumes of 2HG and FLAIR ROIs were calculated. RESULTS: In 8 of 17 (47%) patients, the 2HG volume was larger than FLAIR volume. Across the entire cohort, the mean volume of 2HG was 35.3 cc (range, 5.3-92.7 cc), while the mean volume of FLAIR was 35.8 cc (range, 6.3-140.8 cc). FLAIR and 2HG ROIs had mean overlap of 0.28 (Dice coefficients range, 0.03-0.57) and mean displacement of 12.2 mm (range, 3.2-23.5 mm) between their centers of mass. CONCLUSIONS: Our results indicate that for a substantial number of patients, the 2HG volumetric assessment of tumor burden is more extensive than FLAIR volume. In addition, there is only partial overlap and asymmetric displacement between the centers of FLAIR and 2HG ROIs. These results may have important implications for radiotherapy planning of IDH mutant glioma.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge.

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as Ktrans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T1 were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 for kepvs. 0.74 for Ktrans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Standard chemoradiation for glioblastoma results in progressive brain volume loss.

OBJECTIVE: To investigate the effects of chemotherapy and cranial irradiation on normal brain tissue using in vivo neuroimaging in patients with glioblastoma. METHODS: We used longitudinal MRI to monitor structural brain changes during standard treatment in patients newly diagnosed with glioblastoma. We assessed volumetric and diffusion tensor imaging measures in 14 patients receiving 6 weeks of chemoradiation, followed by up to 6 months of temozolomide chemotherapy alone. We examined changes in whole brain, gray matter (GM), white matter (WM), anterior lateral ventricle, and hippocampal volumes. Normal-appearing GM, WM, and hippocampal analyses were conducted within the hemisphere of lowest/absent tumor burden. We examined diffusion tensor imaging measures within the subventricular zone. RESULTS: Whole brain (F = 2.41; p = 0.016) and GM (F = 2.13; p = 0.036) volume decreased during treatment, without significant WM volume change. Anterior lateral ventricle volume increased significantly (F = 65.51; p < 0.001). In participants analyzed beyond 23 weeks, mean ventricular volume increased by 42.2% (SE: 8.8%; t = 4.94; p < 0.005). Apparent diffusion coefficient increased within the subventricular zone (F = 7.028; p < 0.001). No significant changes were identified in hippocampal volume. CONCLUSIONS: We present evidence of significant and progressive treatment-associated structural brain changes in patients with glioblastoma treated with standard chemoradiation. Future studies using longitudinal neuropsychological evaluation are needed to characterize the functional consequences of these structural changes.

Repeatability of Cerebral Perfusion Using Dynamic Susceptibility Contrast MRI in Glioblastoma Patients.

OBJECTIVES: This study evaluates the repeatability of brain perfusion using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) with a variety of post-processing methods. METHODS: Thirty-two patients with newly diagnosed glioblastoma were recruited. On a 3-T MRI using a dual-echo, gradient-echo spin-echo DSC-MRI protocol, the patients were scanned twice 1 to 5 days apart. Perfusion maps including cerebral blood volume (CBV) and cerebral blood flow (CBF) were generated using two contrast agent leakage correction methods, along with testing normalization to reference tissue, and application of arterial input function (AIF). Repeatability of CBV and CBF within tumor regions and healthy tissues, identified by structural images, was assessed with intra-class correlation coefficients (ICCs) and repeatability coefficients (RCs). Coefficients of variation (CVs) were reported for selected methods. RESULTS: CBV and CBF were highly repeatable within tumor with ICC values up to 0.97. However, both CBV and CBF showed lower ICCs for healthy cortical tissues (up to 0.83), healthy gray matter (up to 0.95), and healthy white matter (WM; up to 0.93). The values of CV ranged from 6% to 10% in tumor and 3% to 11% in healthy tissues. The values of RC relative to the mean value of measurement within healthy WM ranged from 22% to 42% in tumor and 7% to 43% in healthy tissues. These percentages show how much variation in perfusion parameter, relative to that in healthy WM, we expect to observe to consider it statistically significant. We also found that normalization improved repeatability, but AIF deconvolution did not. CONCLUSIONS: DSC-MRI is highly repeatable in high-grade glioma patients.

MRI upsampling using feature-based nonlocal means approach.

In magnetic resonance imaging (MRI), spatial resolution is limited by several factors such as acquisition time, short physiological phenomena, and organ motion. The acquired image usually has higher resolution in two dimensions (the acquisition plane) in comparison with the third dimension, resulting in highly anisotropic voxel size. Interpolation of these low resolution (LR) images using standard techniques, such as linear or spline interpolation, results in distorted edges in the planes perpendicular to the acquisition plane. This poses limitation on conducting quantitative analyses of LR images, particularly on their voxel-wise analysis and registration. We have proposed a new non-local means feature-based technique that uses structural information of a high resolution (HR) image with a different contrast and interpolates the LR image. In this approach, the similarity between voxels is estimated using a feature vector that characterizes the laminar pattern of the brain structures, resulting in a more accurate similarity measure in comparison with conventional patch-based approach. This technique can be applied to LR images with both anisotropic and isotropic voxel sizes. Experimental results conducted on brain MRI scans of patients with brain tumors, multiple sclerosis, epilepsy, as well as schizophrenic patients and normal controls show that the proposed method is more accurate, requires fewer computations, and thus is significantly faster than a previous state-of-the-art patch-based technique. We also show how the proposed method may be used to upsample regions of interest drawn on LR images.

Slice accelerated gradient-echo spin-echo dynamic susceptibility contrast imaging with blipped CAIPI for increased slice coverage.

PURPOSE: To improve slice coverage of gradient echo spin echo (GESE) sequences for dynamic susceptibility contrast (DSC) MRI using a simultaneous-multiple-slice (SMS) method. METHODS: Data were acquired on 3 Tesla (T) MR scanners with a 32-channel head coil. To evaluate use of SMS for DSC, an SMS GESE sequence with two-fold slice coverage and same temporal sampling was compared with a standard GESE sequence, both with 2× in-plane acceleration. A signal to noise ratio (SNR) comparison was performed on one healthy subject. Additionally, data with Gadolinium injection were collected on three patients with glioblastoma using both sequences, and perfusion analysis was performed on healthy tissues as well as on tumor. RESULTS: Retained SNR of SMS DSC is 90% for a gradient echo (GE) and 99% for a spin echo (SE) acquisition, compared with a standard acquisition without slice acceleration. Comparing cerebral blood volume maps, it was observed that the results of standard and SMS acquisitions are comparable for both GE and SE images. CONCLUSION: Two-fold slice accelerated DSC MRI achieves similar SNR and perfusion metrics as a standard acquisition, while allowing a significant increase in slice coverage of the brain. The results also point to a possibility to improve temporal sampling rate, while retaining the same slice coverage.

MRI tracking of FePro labeled fresh and cryopreserved long term in vitro expanded human cord blood AC133+ endothelial progenitor cells in rat glioma.

BACKGROUND: Endothelial progenitors cells (EPCs) are important for the development of cell therapies for various diseases. However, the major obstacles in developing such therapies are low quantities of EPCs that can be generated from the patient and the lack of adequate non-invasive imaging approach for in vivo monitoring of transplanted cells. The objective of this project was to determine the ability of cord blood (CB) AC133+ EPCs to differentiate, in vitro and in vivo, toward mature endothelial cells (ECs) after long term in vitro expansion and cryopreservation and to use magnetic resonance imaging (MRI) to assess the in vivo migratory potential of ex vivo expanded and cryopreserved CB AC133+ EPCs in an orthotopic glioma rat model. MATERIALS, METHODS AND RESULTS: The primary CB AC133+ EPC culture contained mainly EPCs and long term in vitro conditions facilitated the maintenance of these cells in a state of commitment toward endothelial lineage. At days 15-20 and 25-30 of the primary culture, the cells were labeled with FePro and cryopreserved for a few weeks. Cryopreserved cells were thawed and in vitro differentiated or i.v. administered to glioma bearing rats. Different groups of rats also received long-term cultured, magnetically labeled fresh EPCs and both groups of animals underwent MRI 7 days after i.v. administration of EPCs. Fluorescent microscopy showed that in vitro differentiation of EPCs was not affected by FePro labeling and cryopreservation. MRI analysis demonstrated that in vivo accumulation of previously cryopreserved transplanted cells resulted in significantly higher R2 and R2* values indicating a higher rate of migration and incorporation into tumor neovascularization of previously cryopreserved CB AC133+ EPCs to glioma sites, compared to non-cryopreserved cells. CONCLUSION: Magnetically labeled CB EPCs can be in vitro expanded and cryopreserved for future use as MRI probes for monitoring the migration and incorporation to the sites of neovascularization.

Prediction of glioblastoma multiform response to bevacizumab treatment using multi-parametric MRI.

Glioblastoma multiform (GBM) is a highly malignant brain tumor. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). This paper presents a non-invasive approach based on image analysis of multi-parametric magnetic resonance images (MRI) to predict response of GBM to this treatment. The resulting prediction system has potential to be used by physicians to optimize treatment plans of the GBM patients. The proposed method applies signal decomposition and histogram analysis methods to extract statistical features from Gd-enhanced regions of tumor that quantify its microstructural characteristics. MRI studies of 12 patients at multiple time points before and up to four months after treatment are used in this work. Changes in the Gd-enhancement as well as necrosis and edema after treatment are used to evaluate the response. Leave-one-out cross validation method is applied to evaluate prediction quality of the models. Predictive models developed in this work have large regression coefficients (maximum R²â€Š= 0.95) indicating their capability to predict response to therapy.

Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy.

This study assesses the utility of compartmental analysis of SPECT data in lateralizing ictal onset in cases of a putative mesial temporal lobe epilepsy (mTLE). An institutional archival review provided 46 patients (18M, 28F) operated for a putative mTLE who achieved an Engel class Ia postoperative outcome. This established the standard to assure a true ictal origin. Ictal and interictal SPECT images were separately coregistered to T1-weighted (T1W) magnetic resonance (MR) image using a rigid transformation and the intensities matched with an l(1) norm minimization technique. The T1W MR image was segmented into separate structures using an atlas-based automatic segmentation technique with the hippocampi manually segmented to improve accuracy. Mean ictal-interictal intensity difference values were calculated for select subcortical structures and the accuracy of lateralization evaluated using a linear classifier. Hippocampal SPECT analysis yielded the highest lateralization accuracy (91%) followed by the amygdala (87%), putamen (67%) and thalamus (61%). Comparative FLAIR and volumetric analyses yielded 89% and 78% accuracies, respectively. A multi-modality analysis did not generate a higher accuracy (89%). A quantitative anatomically compartmented approach to SPECT analysis yields a particularly high lateralization accuracy in the case of mTLE comparable to that of quantitative FLAIR MR imaging. Hippocampal segmentation in this regard correlates well with ictal origin and shows good reliability in the preoperative analysis.

Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles.

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extraoperative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases.

Differentiation of glioma and radiation injury in rats using in vitro produce magnetically labeled cytotoxic T-cells and MRI.

BACKGROUND: A limitation with current imaging strategies of recurrent glioma undergoing radiotherapy is that tumor and radiation injury cannot be differentiated with post contrast CT or MRI, or with PET or other more complex parametric analyses of MRI data. We propose to address the imaging limitation building on emerging evidence indicating that effective therapy for recurrent glioma can be attained by sensitized T-cells following vaccination of primed dendritic cells (DCs). The purpose of this study was to determine whether cord blood T-cells can be sensitized against glioma cells (U-251) and if these sensitized cytotoxic T-cells (CTLs) can be used as cellular magnetic resonance imaging probes to identify and differentiate glioma from radiation necrosis in rodent models. METHODOLOGY/PRINCIPAL FINDINGS: Cord blood T and CD14+ cells were collected. Isolated CD14+ cells were then converted to dendritic cells (DCs), primed with glioma cell lysate and used to sensitize T-cells. Phenotypical expression of the generated DCs were analyzed to determine the expression level of CD14, CD86, CD83 and HLA-DR. Cells positive for CD25, CD4, CD8 were determined in generated CTLs. Specificity of cytotoxicity of the generated CTLs was also determined by lactate dehydrogenase (LDH) release assay. Secondary proliferation capacity of magnetically labeled and unlabeled CTLs was also determined. Generated CTLs were magnetically labeled and intravenously injected into glioma bearing animals that underwent MRI on days 3 and 7 post- injection. CTLs were also administered to animals with focal radiation injury to determine whether these CTLs accumulated non-specifically to the injury sites. Multi-echo T2- and T2*-weighted images were acquired and R2 and R2* maps created. Our method produced functional, sensitized CTLs that specifically induced U251 cell death in vitro. Both labeled and unlabeled CTLs proliferated equally after the secondary stimulation. There were significantly higher CD25 positive cells (p = <0.006) in CTLs. In addition, T2- and T2*-weighted MR images showed increased low signal intensity areas in animals that received labeled CTLs as compared to the images from animals that received control cells. Histological analysis confirmed the presence of iron positive cells in sites corresponding to MRI low signal intensity regions. Significant differences (p = <0.001) in tumor R2 and R2* values were observed among the groups of animals. Animals with radiation injury exhibited neither MRI hypointense areas nor presence of iron positive cells. CONCLUSION: Our results indicate that T-cells can be effectively sensitized by in vitro methods and used as cellular probes to identify and differentiate glioma from radiation necrosis.

Magnetically-labeled sensitized splenocytes to identify glioma by MRI: a preliminary study.

This study investigated the feasibility of imaging the migration and incorporation of magnetically-labeled sensitized splenocytes in an experimental 9L glioma brain tumor model. Splenocytes collected from tumor-bearing (sensitized splenocytes) or control (nonsensitized splenocytes) host rats were analyzed to determine the population of different cells, labeled with ferumoxides-protamine sulfate (FePro) and injected intravenously to recipient rats (N=4, for each group) bearing intracranial 9L tumors. Day 3 postinjection of splenocytes multiecho T2*-weighted and three-dimensional (3D) gradient echo MRI were obtained using a 7 Tesla MR system. R2* (1/T2*) maps were created from the T2*-weighted images. Signal intensities (SIs) and R2* values in the tumors and contralateral brain were determined by hand drawn regions of interest (ROIs). Brain sections were stained for the evidence of administered cells. Both 3D and T2*-weighted MRI showed low signal intensity areas in and around the tumors in rats that received labeled sensitized splenocytes. Prussian blue (PB), CD45- and CD8-positive cells were present in areas at the corresponding sites of low signal intensities seen on MRI. Rats that received labeled nonsensitized splenocytes did not show low signal intensity areas or PB positive cells in or around the implanted tumors. In conclusion, the immunogenic reaction can be exploited to delineate recurrent glioma using MRI following systemically delivered magnetically labeled sensitized splenocytes or T-cells.

An image analysis approach for automatic malignancy determination of prostate pathological images.

BACKGROUND: Determining malignancy of prostate pathological samples is important for treatment planning of prostate cancer. Traditionally, this is performed by expert pathologists who evaluate the structure of prostate glands in the biopsy samples. However, this is a subjective task due to inter- and intra-observer differences among pathologists. Also, it is time-consuming and difficult to some extent. Therefore, automatic determination of malignancy of prostate pathological samples is of interest. METHODS: A texture-based technique is first used to segment the prostate glands in the image. Features related to size and shape of these glands are then extracted and combined to generate an index, which is proportional to malignancy of cancer. A linear classifier is employed to classify the specimens into benign (low potential for malignancy) and malignant. RESULTS: The leave-one-out technique is employed to evaluate the method using two datasets. The first has 91 images with similar magnifications and illuminations while the second has 199 images with different magnifications and illuminations. In the experiments, accuracies of about 98 and 95% have been achieved for these two datasets, respectively. CONCLUSIONS: An image analysis approach is employed to evaluate prostate pathological images. Experimental results show that the proposed method can successfully classify the prostate biopsy samples into benign and malignant. They also show that the proposed method is robust to variations in magnification and illumination.

Multiwavelet grading of pathological images of prostate.

Histological grading of pathological images is used to determine level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by non-invasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.