Ștefan Paul-Andrei, Lupean Roxana-Adelina, Mihu Carmen Mihaela, Lebovici Andrei, Oancea Mihaela Daniela, Hîțu Liviu, Duma Daniel, Csutak Csaba
Anatomy and Embryology, Morphological Sciences Department, "Iuliu Hațieganu" University of Medicine and Pharmacy, Victor Babes Street 8, 400012 Cluj-Napoca, Romania.
Radiology and Imaging Department, County Emergency Hospital, Clinicilor Street 5, 400006 Cluj-Napoca, Romania.
Diagnostics (Basel). 2021 Apr 29;11(5):812. doi: 10.3390/diagnostics11050812.
The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA's capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. A total of 123 adnexal lesions (benign, 88; malignant, 35) were retrospectively included. The USTA was performed on dedicated software. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The features' ability to identify ovarian malignancies was evaluated through univariate, multivariate, and receiver operating characteristics analyses, and also by the use of the k-nearest neighbor (KNN) classifier. Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). Benign and malignant lesions were differentiated with 90.48% sensitivity and 93.1% specificity by the prediction model (which included the three independent predictors), and with 71.43-80% sensitivity and 87.5-89.77% specificity by the KNN classifier. The USTA shows statistically significant differences between the textures of the two groups, but it is unclear whether the parameters can reflect the true histopathological characteristics of adnexal lesions.
经典超声对附件区良恶性肿块的鉴别存在若干局限性。基于超声的纹理分析（USTA）提供了一个新视角，但其作用尚未得到充分评估。本研究旨在进一步探究USTA区分附件区良恶性肿瘤的能力，并将其流程和结果与先前发表的研究进行比较。本研究回顾性纳入了123例附件区病变（良性88例，恶性35例）。USTA在专用软件上进行。通过应用三种降维技术，选择了23个具有最高鉴别潜力的特征。通过单变量、多变量和受试者工作特征分析，以及使用k近邻（KNN）分类器，评估这些特征识别卵巢恶性肿瘤的能力。三个参数是卵巢肿瘤的独立预测因子（方差总和以及平方和的两种变体）。预测模型（包括三个独立预测因子）对良性和恶性病变的区分敏感度为90.48%，特异度为93.1%，KNN分类器的敏感度为71.43 - 80%，特异度为87.5 - 89.77%。USTA显示两组纹理之间存在统计学显著差异，但尚不清楚这些参数是否能反映附件区病变的真实组织病理学特征。
