Laboratory of Ultrasound, National Institute of Metrology, Standardization, and Industrial Quality (Inmetro), Av. N. Sra. das Gracas, 50 - Xerem, 25250-020 Duque de Caxias, Rio de Janeiro, Brazil.
Med Eng Phys. 2010 Jan;32(1):49-56. doi: 10.1016/j.medengphy.2009.10.007. Epub 2009 Nov 17.
This work aims at investigating seven morphological parameters in distinguishing malignant and benign breast tumors on ultrasound images. Linear discriminant analysis was applied to sets of up to five parameters and then the performances were assessed using the area Az (+/- standard error) under the ROC curve, accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value and negative predictive value. The most relevant individual parameters were the normalized residual value (nrv) and overlap ratio (RS), both calculated from the convex polygon technique, and the circularity (C). When nrv and C were taken together with roughness (R), calculated from normalized radial length (NRL), a performance slightly over 83% in distinguishing malignant and benign breast tumors was achieved.
本研究旨在探讨七种形态学参数在超声图像上区分良恶性乳腺肿瘤的能力。采用线性判别分析对多达五个参数的组合进行分析，然后使用 ROC 曲线下的 Az 面积（+/-标准误差）、准确性（Ac）、敏感性（Se）、特异性（Sp）、阳性预测值和阴性预测值来评估性能。最相关的个体参数是凸多边形技术计算的归一化残差（nrv）和重叠比（RS），以及圆形度（C）。当 nrv 和 C 与粗糙度（R）一起使用时，粗糙度是从归一化径向长度（NRL）计算得出的，在区分良恶性乳腺肿瘤方面的性能略超过 83%。
