Affiliation:
1. Departamento de Clínicas Médicas, Instituto de Patología Infecciosa y Experimental, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara; Guadalajara 44340, Mexico
2. Subdirección de Desarrollo Institucional, Instituto Jalisciense de Cancerología, Guadalajara 44280, Mexico
3. Departamento de Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, San Andrés Cholula 72840, Mexico
4. Laboratorio de Biomédica-Mecatrónica, Subdirección de Investigación y Extensión, Centro de Enseñanza Técnica Industrial Plantel Colomos, Guadalajara 44638, Mexico
Abstract
Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (−PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% −PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman’s r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.