The Use of Texture Analysis on Transvaginal Ultrasound Images in Diagnosing Ovarian Masses: A Prospective Study
aldahlawi, rana hussain . 2017
Objective: To assess the diagnostic performance of texture analysis indiscriminating between benign and malignant adnexal masses Design: prospective observational cross-sectional study Population: Women aged eighteen and above with known pelvic masses Methods: ultrasound images were collected from participants and transferred into PC for off-line analysis. MaZda software was used to perform the texture analysis. Two texture features applied: Grey-level co-occurrence matrix (GLCM) and wavelet. Risk of malignancy index,Pelvic mass index and ADNEX scoring systems were applied to the data, and then results were compared to GLCM and wavelet. Main outcome measures: The GLCM showed a higher sensitivity (72%) compared to two of the scoring systems applied (32% RMI and 62% ADNEX). When combining GLCM and wavelet features using logistic regression model resulted in high performance with AUC=0.8 and a good predictive capacity when tested using the Hosmer and Lemeshow test (p=.502). Results: 169 masses were collected, 116 benign, 29 malignant and 24 simple cyst. Data were divided into 95 premenopausal and 68 postmenopausal. There was a significance difference between benign and malignant p=.004, p=.027 for GLCM and wavelet respectively. GLCM had a higher sensitivity (72%) and NPV (90%) for the entire cohort in comparison to RMI (32%; 80%) and ADNEX (62%; 80%) respectively, Conclusions: Two features of texture analysis have a potential in differentiating benign from malignant pelvic masses which are (GLCM) and the wavelet. Keywords: Ovarian masses; Texture analysis; GLCM; Pelvic ultrasound; RMI; ADNEX; PMI(pelvic mass index)
Objective: To assess the diagnostic performance of texture analysis indiscriminating between benign and malignant adnexal masses Design: prospective observational cross-sectional study Population…