MRI Texture Analysis Reveals Bulbar Abnormalities in Friedreich Ataxia

Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods

Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.

Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury

Acute kidney injury (AKI) relates to an abrupt reduction in renal function resulting from numerous conditions. Morbidity, mortality, and treatment costs related to AKI are relatively high. This condition is strongly associated with damage to proximal tubule cells (PTCs), generating distinct patterns of transcriptional and epigenetic alterations that result in structural changes in the nuclei of this epithelium. To this date, AKI-related nuclear chromatin redistribution in PTCs is poorly understood, and it is unclear whether changes in PTC chromatin patterns can be detected using conventional microscopy during mild AKI, which can progress to more debilitating forms of injury. In recent years, gray level co-occurrence matrix (GLCM) analysis and discrete wavelet transform (DWT) have emerged as potentially valuable methods for identifying discrete structural changes in nuclear chromatin architecture that are not visible during the conventional histopathological exam. Here we present findings indicating that GLCM and DWT methods can be successfully used in nephrology to detect subtle nuclear morphological alterations associated with mild tissue injury demonstrated in rodents by inducing a mild form of AKI through ischemia-reperfusion injury. Our results show that mild ischemic AKI is associated with the reduction of local textural homogeneity of PTC nuclei quantified by GLCM and the increase of nuclear structural heterogeneity indirectly assessed with DWT energy coefficients. This rodent model allowed us to show that mild ischemic AKI is associated with the significant reduction of textural homogeneity of PTC nuclei, indirectly assessed by GLCM indicators and DWT energy coefficients.

Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst

The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann-Whitney test. Mann-Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

Background

Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.

Methods

Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).

Results

The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.

Conclusions

CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

Analysis of Vascular Architecture and Parenchymal Damage Generated by Reduced Blood Perfusion in Decellularized Porcine Kidneys Using a Gray Level Co-occurrence Matrix

There is no cure for kidney failure, but a bioartificial kidney may help address this global problem. Decellularization provides a promising platform to generate transplantable organs. However, maintaining a viable vasculature is a significant challenge to this technology. Even though angiography offers a valuable way to assess scaffold structure/function, subtle changes are overlooked by specialists. In recent years, various image analysis methods in radiology have been suggested to detect and identify subtle changes in tissue architecture. The aim of our research was to apply one of these methods based on a gray level co-occurrence matrix (Topalovic et al.) computational algorithm in the analysis of vascular architecture and parenchymal damage generated by hypoperfusion in decellularized porcine. Perfusion decellularization of the whole porcine kidneys was performed using previously established protocols. We analyzed and compared angiograms of kidneys subjected to pathophysiological arterial perfusion of whole blood. For regions of interest Santos et al. covering kidney medulla and the main elements of the vascular network, five major GLCM features were calculated: angular second moment as an indicator of textural uniformity, inverse difference moment as an indicator of textural homogeneity, GLCM contrast, GLCM correlation, and sum variance of the co-occurrence matrix. In addition to GLCM, we also performed discrete wavelet transform analysis of angiogram ROIs by calculating the respective wavelet coefficient energies using high and low-pass filtering. We report statistically significant changes in GLCM and wavelet features, including the reduction of the angular second moment and inverse difference moment, indicating a substantial rise in angiogram textural heterogeneity. Our findings suggest that the GLCM method can be successfully used as an addition to conventional fluoroscopic angiography analyses of micro/macrovascular integrity following in vitro blood perfusion to investigate scaffold integrity. This approach is the first step toward developing an automated network that can detect changes in the decellularized vasculature.

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab

Background

Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [¹⁸F]FDG PET radiomics signature (iRADIOMICS) predicts response of metastatic non-small-cell lung cancer (NSCLC) patients to pembrolizumab better than the current clinical standards.

Patients and methods

Thirty patients receiving pembrolizumab were scanned with [¹⁸F]FDG PET/CT at baseline, month 1 and 4. Associations of six robust primary tumour radiomics features with overall survival were analysed with Mann-Whitney U-test (MWU), Cox proportional hazards regression analysis, and ROC curve analysis. iRADIOMICS was constructed using univariate and multivariate logistic models of the most promising feature(s). Its predictive power was compared to PD-L1 tumour proportion score (TPS) and iRECIST using ROC curve analysis. Prediction accuracies were assessed with 5-fold cross validation.

Results

The most predictive were baseline radiomics features, e.g. Small Run Emphasis (MWU, p = 0.001; hazard ratio = 0.46, p = 0.007; AUC = 0.85 (95% CI 0.69–1.00)). Multivariate iRADIOMICS was found superior to the current standards in terms of predictive power and timewise with the following AUC (95% CI) and accuracy (standard deviation): iRADIOMICS (baseline), 0.90 (0.78–1.00), 78% (18%); PD-L1 TPS (baseline), 0.60 (0.37–0.83), 53% (18%); iRECIST (month 1), 0.79 (0.62–0.95), 76% (16%); iRECIST (month 4), 0.86 (0.72–1.00), 76% (17%).

Conclusions

Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab. The predicted non-responders could be offered other treatment options to improve their overall survival.

3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson's Disease Using Artificial Neural Networks

Parkinson's disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson's disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson's disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson's disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson's disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Neuroimaging Applications in Chronic Ataxias

Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT) and positron emission tomography (PET) are the main instruments for neuroimaging investigation of patients with chronic ataxia. MRI has a predominant diagnostic role in the single patient, based on the visual detection of three patterns of atrophy, namely, spinal atrophy, cortical cerebellar atrophy and olivopontocerebellar atrophy, which correlate with the aetiologies of inherited or sporadic ataxia. In fact spinal atrophy is observed in Friedreich ataxia, cortical cerebellar atrophy in Ataxia Telangectasia, gluten ataxia and Sporadic Adult Onset Ataxia and olivopontocerebellar atrophy in Multiple System Atrophy cerebellar type. The 39 types of dominantly inherited spinocerebellar ataxias show either cortical cerebellar atrophy or olivopontocerebellar atrophy. T2 or T2* weighted MR images can contribute to the diagnosis by revealing abnormally increased or decreased signal with a characteristic distribution. These include symmetric T2 hyperintensity of the posterior and lateral columns of the cervical spinal cord in Friedreich ataxia, diffuse and symmetric hyperintensity of the cerebellar cortex in Infantile Neuro-Axonal Dystrophy, symmetric hyperintensity of the peridentate white matter in Cerebrotendineous Xanthomatosis, and symmetric hyperintensity of the middle cerebellar peduncles and peridentate white matter, cerebral white matter and corpus callosum in Fragile X Tremor Ataxia Syndrome. Abnormally decreased T2 or T2* signal can be observed with a multifocal distribution in Ataxia Telangectasia and with a symmetric distribution in the basal ganglia in Multiple System Atrophy. T2 signal hypointensity lining diffusely the outer surfaces of the brainstem, cerebellum and cerebrum enables diagnosis of superficial siderosis of the central nervous system. The diagnostic role of nuclear medicine techniques is smaller. SPECT and PET show decreased uptake of radiotracers investigating the nigrostriatal system in Multiple System Atrophy and in patients with Fragile X Tremor Ataxia Syndrome. Semiquantitative or quantitative MRI, SPECT and PET data describing structural, microstructural and functional changes of the cerebellum, brainstem, and spinal cord have been widely applied to investigate physiopathological changes in patients with chronic ataxias. Moreover they can track diseases progression with a greater sensitivity than clinical scales. So far, a few small-size and single center studies employed neuroimaging techniques as surrogate markers of treatment effects in chronic ataxias.

Main inherited neurodegenerative cerebellar ataxias, how to recognize them using magnetic resonance imaging?

Ataxia is a neurodegenerative disease resulting from brainstem, cerebellar, and/or spinocerebellar tracts impairments. Symptoms onset could vary widely from childhood to late-adulthood. Autosomal cerebellar ataxias are considered as one of the most complex group in neurogenetics. In addition to their genetic heterogeneity, there is an important phenotypic variability in the expression of cerebellar impairment, complicating the genetic mutation research. A pattern recognition approach using brain MRI measures of atrophy, hyperintensities and iron-induced hypointensity of the dentate nuclei, could be therefore helpful in guiding genetic research. This review will discuss a pattern recognition approach that, associated with the age at disease onset, and clinical manifestations, may help neuroradiologists differentiate the most frequent profiles of ataxia.

Cognitive and functional connectivity alterations in Friedreich's ataxia

Objective

The aim of this study was to perform the first resting-state functional MRI (RS-fMRI) analysis in Friedreich's ataxia (FRDA) patients to assess possible brain functional connectivity (FC) differences in these patients, and test their correlations with neuropsychological performances.

Methods

In total, 24 FRDA patients (M/F: 15/9, mean age 31.3 ± 15.0) and 24 healthy controls (HC; M/F: 15/9, mean age 30.7 ± 15.5) were enrolled in this cross-sectional study. All patients underwent a thorough neuropsychological battery, investigating different cognitive domains. RS-fMRI data were analyzed using a seed-based approach, probing the FC of cortical areas potentially referable to specific executive and cognitive functions compromised in FRDA.

Results

Compared to HC, FRDA patients showed overall worse neuropsychological scores in several domains, including global cognitive assessment, spatial memory, visuoperception and visuospatial functions, and executive functions. Analysis of RS-fMRI data showed a higher FC in FRDA patients compared to HC between paracingulate gyri and the medial frontal gryrus, between the superior frontal gyrus and bilateral angular gyri, and between the middle temporal gyrus and the cingulate gyrus, with a reduced FC between the medial frontal gryrus and the cerebellum.

Interpretation

We found a reduction in FC between frontal areas and the contralateral cerebellar cortex in FRDA, in line with the known alteration in cerebello-cortical pathway in this condition. On the other hand, a higher FC between different cortical areas was shown, possibly reflecting a compensatory phenomenon. These results, in conjunction with clinical findings, may shed new light on the pattern of supratentorial and infratentorial involvement, and on dynamics of brain plasticity in this disease.