Application of Texture Features for Classification of Primary Benign and Primary Malignant Focal Liver Lesions

Manth, Nimisha; Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

doi:10.1007/978-3-319-28854-3_15

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Using texture analysis (TA), radiomics can be utilized to analyze the voxel gray levels, as well as the distribution and relationship of pixels in US, CT, MRI, and PET/CT images to obtain radiomic features, thereby providing an objective and quantitative assessment of tumor heterogeneity [21,22]. is mode of analysis has been applied to a variety of imaging techniques to distinguish between benign and malignant lesions, such as US imaging of the thyroid [23] and liver [24], CT images of the lungs [25] and kidneys [26], and MRI imaging of the breasts [27]. Moreover, it has been used to evaluate the prognosis of esophageal [28], lung [29], and hypopharyngeal [30] cancers.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

Zheng

Zhao

et al. 2020

Contrast Media & Molecular Imaging

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Background. The National Comprehensive Cancer Network guidelines recommend excisional biopsies for the diagnosis of lymphomas. However, resection biopsies in all patients who are suspected of having malignant lymph nodes may cause unnecessary injury and increase medical costs. We investigated the usefulness of 18F-fluorodeoxyglucose positron emission/computed tomography- (18F-FDG-PET/CT-) based radiomics analysis for differentiating between lymphomatous lymph nodes (LLNs) and cancerous lymph nodes (CLNs). Methods. Using texture analysis, radiomic parameters from the 18F-FDG-PET/CT images of 492 lymph nodes (373 lymphomatous lymph nodes and 119 cancerous lymph nodes) were extracted with the LIFEx package. Predictive models were generated from the six parameters with the largest area under the receiver operating characteristics curve (AUC) in PET or CT images in the training set (70% of the data), using binary logistic regression. These models were applied to the test set to calculate predictive variables, including the combination of PET and CT predictive variables (PREcombination). The AUC, sensitivity, specificity, and accuracy were used to compare the differentiating ability of the predictive variables. Results. Compared with the pathological diagnosis of the patient’s primary tumor, the AUC, sensitivity, specificity, and accuracy of PREcombination in differentiating between LLNs and CLNs were 0.95, 91.67%, 94.29%, and 92.96%, respectively. Moreover, PREcombination could effectively distinguish LLNs caused by various lymphoma subtypes (Hodgkin’s lymphoma and non-Hodgkin’s lymphoma) from CLNs, with the AUC, sensitivity, specificity, and accuracy being 0.85 and 0.90, 77.78% and 77.14%, 97.22% and 88.89%, and 90.74% and 83.10%, respectively. Conclusions. Radiomics analysis of 18F-FDG-PET/CT images may provide a noninvasive, effective method to distinguish LLN and CLN and inform the choice between fine-needle aspiration and excision biopsy for sampling suspected lymphomatous lymph nodes.

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Section: Introductionmentioning

confidence: 99%

Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

Zheng

Zhao

et al. 2020

Contrast Media & Molecular Imaging

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“…We used seven models to acquire features from specific ROIs in the proposed work. These features included the First-Order Statistics (FoS) [34], Gray-Level Spatial Co-Occurrence Matrix (SGLCM) [35,36], Gray-Level Difference Statistics (GLDS) [37], Fourier Power Spectrum (FPS) [38], Fractal Features [39], Statistical Feature Matrix (SFM) [40], and Law's Texture Energy Measures (TEM) [41]. We extracted 37 features from the CT images and then normalized each to have the unit variance and zero means.…”

Section: Extraction Of Featuresmentioning

confidence: 99%

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Nisa

Buzdar

Khan³

et al. 2022

Symmetry

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Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.

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A hybrid CAD system design for liver diseases using clinical and radiological data

Bansal

Chhabra

Chandra

et al. 2019

U-Healthcare Monitoring Systems

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Application of Texture Features for Classification of Primary Benign and Primary Malignant Focal Liver Lesions

Cited by 4 publications

References 31 publications

Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

A hybrid CAD system design for liver diseases using clinical and radiological data

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