Texture analysis of perimenopausal and post-menopausal endometrial tissue in grayscale transvaginal ultrasonography.
The aim of this study was to investigate the feasibility of texture analysis in characterizing endometrial tissue as depicted in two-dimensional (2D) grayscale transvaginal ultrasonography. Digital transvaginal ultrasound endometrial images were acquired from 65 perimenopausal and post-menopausal women prior to gynaecological operations; histology revealed 15 malignant and 50 benign cases. Images were processed with a wavelet-based contrast enhancement technique. Three regions of interest (ROIs) were identified (endometrium, endometrium plus adjacent myometrium, layer containing endometrial-myometrial interface) on each original and processed image. 32 textural features were extracted from each ROI employing first and second order statistics texture analysis algorithms. Textural features-based models were generated for differentiating benign from malignant endometrial tissue using stepwise logistic regression analysis. Models' performance was evaluated by means of receiver operating characteristic (ROC) analysis. The best logistic regression model comprised seven textural features extracted from the ROIs determined on the processed images; three features were extracted from the endometrium, while four features were extracted from the layer containing the endometrial-myometrial interface. The area under the ROC curve (A(z)) was 0.956+/-0.038, providing 86.0% specificity at 93.3% sensitivity using the cut-off level of 0.5 for probability of malignancy. Texture analysis of 2D grayscale transvaginal ultrasound images can effectively differentiate malignant from benign endometrial tissue and may contribute to computer-aided diagnosis of endometrial cancer.
Michail
G
G
Department of Obstetrics and Gynecology, School of Medicine, University of Patras, 265 00 Patras, Greece.
Karahaliou
A
A
Skiadopoulos
S
S
Kalogeropoulou
C
C
Terzis
G
G
Boniatis
I
I
Costaridou
L
L
Kourounis
G
G
Panayiotakis
G
G
D000328:Adult; D000368:Aged; D000465:Algorithms; D000568:Amenorrhea / Q000209:etiology; D016889:Endometrial Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D004717:Endometrium / Q000000981:diagnostic imaging*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007889:Leiomyoma / Q000000981:diagnostic imaging* / Q000473:pathology; D008593:Menopause / Q000502:physiology*; D008875:Middle Aged; D015588:Observer Variation; D012372:ROC Curve; D014463:Ultrasonography; D014592:Uterine Hemorrhage / Q000209:etiology; D014594:Uterine Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology