Ovarian Tissue Characterization in Ultrasound

Abstract

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computeraided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given.

Histopathological Pattern and Age Distribution, of Malignant Ovarian Tumor among Sudanese Ladies

“…Peritoneal cavity, lymph nodes, lungs, and liver are the most common site for metastasis [ 4 ]. The risk of ovarian cancer increases with ovulation induction treatment, nulliparity, women on hormonal replacement therapy, and those begin ovulation at a younger age or reach menopause at an older age at a higher risk of ovarian cancer [ 5 ][ 6 ]. Factors that decrease the risk of ovarian cancer include the use of OCP, tubal ligation, and breastfeeding [ 6 ].…”

“…The risk of ovarian cancer increases with ovulation induction treatment, nulliparity, women on hormonal replacement therapy, and those begin ovulation at a younger age or reach menopause at an older age at a higher risk of ovarian cancer [ 5 ][ 6 ]. Factors that decrease the risk of ovarian cancer include the use of OCP, tubal ligation, and breastfeeding [ 6 ]. Genetic inheritances are responsible for 10% of cases; the estimated risk for women with BRCA1 or BRCA2 is 50% [ 6 ].…”

“…Factors that decrease the risk of ovarian cancer include the use of OCP, tubal ligation, and breastfeeding [ 6 ]. Genetic inheritances are responsible for 10% of cases; the estimated risk for women with BRCA1 or BRCA2 is 50% [ 6 ]. The most common type of ovarian malignancies is epithelial cell carcinoma which accounts for 95% of cases.…”

First review on psoriasis severity risk stratification: An engineering perspective

“…A note on strengths and weaknesses of CAD systems: CAD systems are popularly adapted in literature for several applications such as liver disease classification [88], EEG signals classification [89], and tissue characterization [90]. It offers the following advantages: (i) risk assessment is near real-time since the learning parameters are computed apriori based on the off-line training data sets; (ii) such systems are easily adaptable by different set of classifiers such as: Support Vector Machine (SVM), Neural Networks (NN) and Fuzzy Classifiers; (iii) CADx systems provide the flexibility of increasing or decreasing the input number of linear or non-linear image-based features; (iv) it provides the ability to select the best features which help in psoriasis disease classification and risk stratification; (v) CADx systems allow to numerically compute the reliability and stability dynamically.…”

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

“…The online system also consists of two main steps: (a) online feature extraction and (b) class prediction (Control or COVID). Such a system has been developed by our group before for tissue characterization for different applications such as liver cancer [39][40][41], thyroid cancer [42][43][44], ovarian cancer [45][46][47], atherosclerotic plaque characterization [48][49][50][51][52][53], and lung disease classification [37]. The classification of lungs into COVID and Control was implemented using three ML methods namely ANN, DT, and RF.…”