Department of Ultrasound, South Medical University Affiliated Maternal & Child Health Hospital of Foshan, Foshan 528000, China.
Department of Pathology, South Medical University Affiliated Maternal & Child Health Hospital of Foshan, Foshan 528000, China.
Biomed Res Int. 2022 Oct 25;2022:7187334. doi: 10.1155/2022/7187334. eCollection 2022.
To use the logistic regression model to evaluate the value of ultrasound characteristics in the Ovarian-Adnexal Reporting and Data System ultrasound lexicon in determining ovarian solid component-containing mass benignancy/malignancy.
We retrospectively analyzed the data of 172 patients with adnexal masses discovered by ultrasound, and diagnosis was confirmed by postoperative pathological tests from January 2019 to December 2021. Thirteen ovarian tumor-related parameters in the benign and malignant ovarian tumor groups were selected for univariate analyses. Statistically significant parameters were included in multivariate logistic regression analyses to construct a logistic regression diagnosis model, and the diagnostic performance of the model in predicting ovarian malignancies was calculated.
Of the 172 adnexal tumors, 104 were benign, and 68 were malignant. There were differences in cancer antigen 125, maximum mass diameter, maximum solid component diameter, multilocular cyst with solid component, external contour, whether acoustic shadows were present in the solid component, number of papillae, vascularity, presence/absence of ascites, and presence/absence of peritoneal thickening or nodules between the benign ovarian tumor and malignancy groups ( < 0.05). Logistic regression analyses showed that maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites were included in the logistic regression model, and the area under the receiver operating characteristic curve for this regression model in predicting ovarian malignancy was 0.962 (95% confidence interval: 0.933~0.990; < 0.001). Logit () ≥ -0.02 was used as the cutoff value, and the prediction accuracy, sensitivity, specificity, positive predictive value, and negative predictive values were 93.6%, 86.8%, 98.1%, 96.7%, and 91.9%, respectively.
The logistic regression model containing the maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites can help in determining the benignancy/malignancy of solid component-containing masses.
利用 logistic 回归模型评估卵巢-附件报告和数据系统超声术语中的超声特征在确定含有卵巢实性成分的肿块良性/恶性中的价值。
我们回顾性分析了 2019 年 1 月至 2021 年 12 月期间经超声发现附件肿块的 172 例患者的数据，诊断结果经术后病理检查证实。对良性和恶性卵巢肿瘤组中 13 个与卵巢肿瘤相关的参数进行单因素分析。将有统计学意义的参数纳入多变量 logistic 回归分析，构建 logistic 回归诊断模型，并计算模型预测卵巢恶性肿瘤的诊断性能。
172 个附件肿瘤中，良性 104 例，恶性 68 例。良性卵巢肿瘤组与恶性卵巢肿瘤组在癌抗原 125、最大肿块直径、最大实性成分直径、多房性伴实性成分的囊肿、外形、实性成分中是否存在声影、乳头数量、血流、腹水、腹腔增厚或结节的存在/缺失方面存在差异（<0.05）。logistic 回归分析显示，最大实性成分直径、实性成分中是否存在声影、乳头数量和腹水的存在被纳入 logistic 回归模型，该回归模型预测卵巢恶性肿瘤的受试者工作特征曲线下面积为 0.962（95%置信区间：0.933~0.990；<0.001）。以 logit（）≥-0.02 为截断值，该回归模型的预测准确率、敏感度、特异度、阳性预测值和阴性预测值分别为 93.6%、86.8%、98.1%、96.7%和 91.9%。
包含最大实性成分直径、实性成分中是否存在声影、乳头数量和腹水存在的 logistic 回归模型有助于确定含有实性成分的肿块的良恶性。
