Histogram Analysis Approach Research Articles

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional—and even some advanced MRI techniques—are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.

Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques.

Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.

Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach.

According to the revised World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) of 2016, oligodendrogliomas are now defined primarily by a specific molecular signature (presence of IDH mutation and 1p19q codeletion). The purpose of our study was to assess the value of dynamic susceptibility contrast MR imaging (DSC-MRI) and diffusion-weighted imaging (DWI) to characterize oligodendrogliomas and to distinguish them from astrocytomas. Seventy-one adult patients with untreated WHO grade II and grade III diffuse infiltrating gliomas and known 1p/19q codeletion status were retrospectively identified and analyzed using relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) maps based on whole-tumor volume histograms. The Mann-Whitney U test and logistic regression were used to assess the ability of rCBV and ADC to differentiate between oligodendrogliomas and astrocytomas both independently, but also related to the WHO grade. Prediction performance was evaluated in leave-one-out cross-validation (LOOCV). Oligodendrogliomas showed significantly higher microvascularity (higher rCBVMean ≥ 0.80, p = 0.013) and higher vascular heterogeneity (lower rCBVPeak ≤ 0.044, p = 0.015) than astrocytomas. Diffuse gliomas with higher cellular density (lower ADCMean ≤ 1094 × 10-6mm2/s, p = 0.009) were more likely to be oligodendrogliomas than astrocytomas. Histogram analysis of rCBV and ADC was able to differentiate between diffuse astrocytomas (WHO grade II) and anaplastic astrocytomas (WHO grade III). Histogram-derived rCBV and ADC parameter may be used as biomarkers for identification of oligodendrogliomas and may help characterize diffuse gliomas based upon their genetic characteristics.

Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer

ObjectiveTo investigate diagnostic performance of diffusion kurtosis imaging with histogram analysis for stratifying pathologic Gleason grade of prostate cancer (PCa). Materials and methodsThis retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 110 patients pathologically confirmed as having PCa (diameter>0.5cm) underwent preoperative diffusion-weighted magnetic resonance imaging (b value of 0–2,100s/mm2) at 3T. Data were postprocessed by monoexponential and diffusion kurtosis models for quantitation of apparent diffusion coefficients (ADCs), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). The measurement was based on an entire-tumor histogram analysis approach. The ability of imaging indices for differentiating low-grade (LG) PCa (Gleason score [GS]≤6) from intermediate-/high-grade (HG: GS>6) PCa was analyzed by receiver operating characteristic regression. ResultsThere were 49 LG tumors and 77 HG tumors at pathologic findings. HG-PCa had significantly lower ADCs, lower diffusion kurtosis diffusivity (Dapp), and higher kurtosis coefficient (Kapp) in mean, median, 10th, and 90th percentile, with higher Dapp in skewness and kurtosis than LG-PCa (P< 0.05). The 10th ADCs, the 10th Dapp, and the 90th Kapp showed relatively higher area under receiver operating characteristic curve (Az), Youden index, and positive likelihood ratio in stratifying aggressiveness of PCa against other indices. The 90th Kapp showed relatively higher correlation (ρ>0.6) with ordinal GS of PCa; significantly higher Az, sensitivity, and specificity (0.889, 74.1%, and 93.9%, respectively) than the 10th Dapp did (0.765, 61.0%, and 79.6%, respectively; P<0.05); and higher Az and specificity than the 10th ADCs did (0.738 and 71.4%, respectively; P<0.05) in differentiating LG-PCa from HG-PCa. ConclusionsIt demonstrated a good reliability of histogram diffusion kurtosis imaging for stratifying pathologic GS of PCa. The 90th Kapp had better diagnostic performance in differentiating LG-PCa from HG-PCa.

SU-E-J-117: Assessing Response to Radiochemotherapy Treatment On 18F-FDG PET in Non-Small Cell Lung Cancer Using Approaches of Histogram and Gray Level Co-Occurrence Matrix

Purpose: The aim of this study was to propose and investigate gray level histogram and texture features information provided by 18F-FDG PET to assess patients imaging response to radiochemotherapy in non-small cell lung cancer (NSCLC). Methods: Twelve patients with newly diagnosed NSCLC treated with combined radiochemotherapy were involved in this study. Patients were categorized under three headings (non-responders, partial responders and complete responders) by experienced radiologists on the basis of RECIST according PET scans changes between pretreatment and 1 month after treatment. We analyzed the percentage variation of PET density using histogram analysis approach which characterizes global change of tumor region on PET. Texture parameters variation between pretreatment and 1 month after treatment completion which describe local voxel spatial distribution were extracted from Gray Level Co-occurrence Matrix (GLCM). Correlation between characteristics variation and three type response status were analyzed. Results: The uniformity of gray level histogram on the whole and the maximum percentage decrease in histogram was well associated with tumor shrinkage and response status. The above indices derived from histogram were capable to differentiate three groups tumor response to radiochemotherapy. Texture parameters variation (ASM, ENT and IDM) were able to differentiate the 3 response groups considering a high correlation with response status. Conclusion: We demonstrated that histogram and texture analysis methods on baseline 18F-FDG PET scans provided robust, discriminative stratification in assessing response to combined radiochemotherapy and may have a good application prospect in clinical practice.

Reproducibility of Dynamic Contrast-enhanced MR Imaging. Part II. Comparison of Intra- and Interobserver Variability with Manual Region of Interest Placement versus Semiautomatic Lesion Segmentation and Histogram Analysis

To compare the inter- and intraobserver variability with manual region of interest (ROI) placement versus that with software-assisted semiautomatic lesion segmentation and histogram analysis with respect to quantitative dynamic contrast material-enhanced (DCE) MR imaging determinations of the volume transfer constant (K(trans)). The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Fifteen DCE MR imaging studies of the female pelvis defined the study group. Uterine fibroids were used as a perfusion model. Three varying types of lesion measurements were performed by five readers on each study by using DCE MR imaging perfusion analysis software with manual ROI placement and a semiautomatic lesion segmentation and histogram analysis solution. Intra- and interreader variability of measurements of K(trans) with the different measurement types was calculated. The overall interobserver variability of K(trans) with manual ROI placement (mean, 28.5% ± 9.3) was reduced by 42.5% when the semiautomatic, software-assisted lesion measurement method was used (16.4% ± 6.2). Whole-lesion measurement showed the lowest interobserver variability with both measurement methods (20.1% ± 4.3 with the manual method vs 10.8% ± 2.6 with the semiautomatic method). The overall intrareader variability with the manual ROI method (7.6% ± 10.6) was not significantly different from that with the semiautomatic method (7.3% ± 10.8), but the intraclass correlation coefficient for intrareader reproducibility improved from 0.86 overall with the manual method to 0.99 with the semiautomatic method. A semiautomatic lesion segmentation and histogram analysis approach can provide a significant reduction in interobserver variability for DCE MR imaging measurements of K(trans) when compared with manual ROI methods, whereas intraobserver reproducibility is improved to some extent.

A combined bootstrap/histogram analysis approach for computing a lateralization index from neuroimaging data

Cerebral hemispheric specialization has traditionally been described using a lateralization index (LI). Such an index, however, shows a very severe threshold dependency and is prone to be influenced by statistical outliers. Reliability of this index thus has been inherently weak, and the assessment of this reliability is as yet not possible as methods to detect such outliers are not available. Here, we propose a new approach to calculating a lateralization index on functional magnetic resonance imaging data by combining a bootstrap procedure with a histogram analysis approach. Synthetic and real functional magnetic resonance imaging data was used to assess performance of our approach. Using a bootstrap algorithm, 10,000 indices are iteratively calculated at different thresholds, yielding a robust mean, maximum and minimum LI and thus allowing to attach a confidence interval to a given index. Taking thresholds into account, an overall weighted bootstrapped lateralization index is calculated. Additional histogram analyses of these bootstrapped values allow to judge reliability and the influence of outliers within the data. We conclude that the proposed methods yield a robust and specific lateralization index, sensitively detect outliers and allow to assess the underlying data quality.

A new combination of replica exchange Monte Carlo and histogram analysis for protein folding and thermodynamics

A novel combination of replica exchange Monte Carlo sampling techniques with a histogram analysis approach is developed and applied to study the thermodynamics of the folding transition in a face-centered cubic lattice chain protein model. Sequences of hydrophobic (H) and polar (P) topology residues were designed to fold into various β-barrel type proteins. The interaction scheme includes the short-range propensity to form extended conformations, residue-dependent long-range contact potentials, and orientation-dependent hydrogen bonds. Weakly cooperative folding transitions could be observed for properly designed HP. (Hydrophobic and polar residue sequences with cooperative long-range interaction methods were proposed and tested.) Based on the study of these systems, the computational cost of such an approach is many times less than the cost of other Monte Carlo procedures. This opens up the possibility for efficient studies of the folding thermodynamics of more detailed protein models.

Clinical radiation nephropathy

An analysis of the normal tissue effects of irradiation of the kidney is presented. Various clinical syndromes resulting from treatment are described as well as the potential cellular basis for these findings. Effects of concurrent and/or sequential treatment with irradiation and various chemotherapeutic agents are discussed and the impact of these agents on toxicity presented. Adverse consequences of renal treatment in the child is described and possible radiation effects on so-called compensatory hypertrophy following nephrectomy presented. Renal consequences described to date of bone marrow transplantation programs utilizing irradiation are also presented. The necessity of a dose-volume histogram analysis approach to analyzing renal toxic effects in patients followed for long (> 10 year) periods is essential in developing accurate guidelines of renal tolerance.

Scene segmentation by cluster detection in color spaces

Ohlander [1] has shown that a variety of scenes can be segmented into meaningful parts by histogramming the values of various point or local properties of the scene; extracting the region whose points gave rise to that peak; and repeating the process for the remainder of the scene. A generalization of this histogram analysis approach is to map the points of the scene into a multi-dimensional feature space, and to look for clusters in this space (a histogram is a mapping into a one-dimensional feature space, in which clusters are peaks). This note illustrates how one of Ohlander's scenes, a house, can be reasonably segmented by mapping it into a three-dimensional color space.