Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques.

BACKGROUND: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. METHODS: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. RESULTS: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. CONCLUSIONS: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Comparison between the histopathological and immunophenotypical features of hypopigmented and nonhypopigmented mycosis fungoides: A retrospective study.

BACKGROUND: Mycosis fungoides (MF), the commonest primary cutaneous T-cell lymphoma, has classic and variant types which include hypopigmented MF (HMF). Previous studies have identified distinct clinicopathological profiles in HMF. This study aims to objectively compare the clinicopathological features of HMF with non-HMF lesions in order to characterize salient features of HMF. METHODS: This cross-sectional, retrospective study analyzed biopsy specimens of 87 patients with MF. HMF and non-HMF groups were compared using clinical data, immunophenotypic features and scores given for six histopathological features: dermal infiltrate, basilar and superficially extending epidermotropism, Pautrier microabscesses and dermal and epidermotropic lymphocytic atypia. RESULTS: Seventy-six patients had HMF. Presentation in females (59.21%; p = .04) and patch stage (88.16%; p = .01) in HMF were significant, and HMF presented at a younger mean age when compared to non-HMF. Both groups had equal intensity of epidermotropism, with HMF showing milder dermal infiltrates and significantly less dermal atypia. Pautrier microabscesses were significantly commoner in non-HMF (LR 10.76; p < .01). 94.74% of HMF were CD4-/CD8+. CONCLUSION: HMF presents at a lower age and earlier stage with female predominance compared to non-HMF. Because of milder dermal infiltrates, less dermal atypia, and Pautrier microabscesses, the diagnosis of HMF requires correlation with clinical features and careful assessment of epidermotropic cells.

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques.

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients' demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Calcified Leiomyoma of the Distal Forearm in a Child: A Case Report and Review of Literature.

BACKGROUND: Deep somatic leiomyomas arising in skeletal muscle are extremely rare in children, especially in the extremities. Around half of them show calcifications. We present a rare case of a calcified leiomyoma of the distal forearm in a child. Case Summary. A seven-year-old boy presented with right distal forearm and wrist pain with restricted supination for 4 years. X-ray showed ring and arc calcifications in the distal forearm at the interosseous area. MRI also confirmed a well-defined soft tissue lesion with areas of calcifications. A diagnosis of a cartilage-forming lesion or a peripheral nerve sheath tumour was suggested. The lesion was completely excised. Histology showed a lesion composed of intersecting fascicles of spindle cells with stromal calcification having immunohistochemical features of a leiomyoma. CONCLUSION: Although soft tissue calcifications can be seen in a plethora of conditions seen in daily orthopaedic practise, a high index of suspicion should be maintained for rare conditions like deep somatic leiomyoma.