The value of IOTA Logistic regression model LR2 in predicting benign and malignant ovarian tumors

WANG Runli; LI Hezhou; ZHANG Hongbin

doi:10.19401/j.cnki.1007-3639.2018.03.015

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The value of IOTA Logistic regression model LR2 in predicting benign and malignant ovarian tumors

更新时间：2025-12-15

- The value of IOTA Logistic regression model LR2 in predicting benign and malignant ovarian tumors
- Vol. 27, Issue 3, Pages: 207-210(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2018.03.015
  CLC：
- Published Online：26 July 2018，
  
  Published：26 July 2018
- 稿件说明：
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王润丽,栗河舟,张红彬. IOTA Logistic回归模型LR2预测卵巢良恶性肿瘤的价值[J]. 肿瘤影像学, 2018, 27(3): 207-210 https://doi.

org/10.19401/j.cnki.1007-3639.2018.03.015
王润丽,栗河舟,张红彬. IOTA Logistic回归模型LR2预测卵巢良恶性肿瘤的价值[J]. 肿瘤影像学, 2018, 27(3): 207-210 https://doi. DOI： 10.19401/j.cnki.1007-3639.2018.03.015.

org/10.19401/j.cnki.1007-3639.2018.03.015 DOI：

摘要

目的：

探讨国际卵巢肿瘤研究组（International ovarian of tumor analysis，IOTA） Logistic回归模型LR2预测卵巢良恶性肿瘤的价值。

方法：

选取2016年1月2017年3月因附件包块在郑州大学第三附属医院住院并行手术治疗的215例患者，所有患者术前均接受超声检查，观察、总结声像图特点，用IOTA后处理软件计算风险值，随访术后病理结果，计算IOTA Logistic回归模型LR2的灵敏度、特异度、阳性预测值、阴性预测值、阳性似然比、阴性似然比及受试者工作特征(receiver operating characteristic，ROC)曲线的曲线下面积（area under curve，AUC）。

结果：

病例组共215例，其中良性肿瘤126例（58.6%），恶性肿瘤89例（41.4%），LR2的灵敏度为95.5%（95%CI：90.4%~98.3%），特异度为76.2%（95%CI：62.7%~87.7%），AUC为0.89(SE=0.024，95%CI：0.87~0.91)。

结论：

IOTA Logistic回归模型LR2在预测卵巢肿瘤方面有较高的应用价值，可作为非妇产科或者低年资超声科医师诊断卵巢肿瘤的辅助方法。

Abstract

Objective:

Evaluating the clinical value of IOTA Logistic regression model LR2 in predicting benign and malignant ovarian tumors by ultrasound.

Methods:

This study selected the patients who had been hospitalized surgical treatment in the Third Affiliated Hospital of Zhengzhou University due to the adnexal tumors

all the patients underwent ultrasound

observed and summarized the image

then used IOTA post-processing software calculating the

risk index

follow-up postoperative pathological results

analyzed the sensitivity

specificity

positive predictive value

negative predictive value

positive likelihood ratio

negative likelihood ratio and the area under curve (AUC) of the receiver operating characteristic (ROC).

Results:

A total of 215 patients were calculated

including 126 benign tumors (58.6%) and 89 malignant tumors(41.4%)

the sensitivity was 95.5% (95%CI: 90.4%-98.3%)

the specificity was 76.2% (95%CI: 62.7%-87.7%)

the AUC was 0.89 (SE=0.024

95%CI: 0.87-0.91).

Conclusion:

IOTA Logistic regression model LR2 has highly diagnosis value in evaluating the ovarian tumors

which can be used as an auxiliary diagnosis method for non-gynecological ultrasound or non-expert operators.

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