Application of logistic regression model derived from IOTA simple rules in differentiation of benign and malignant ovarian tumors

ZHU Zhengtao; ZHENG Li; JIANG Xinhui; HUANG Shaojie; CAI Yingyu

doi:10.19732/j.cnki.2096-6210.2021.06.010

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Application of logistic regression model derived from IOTA simple rules in differentiation of benign and malignant ovarian tumors

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- Application of logistic regression model derived from IOTA simple rules in differentiation of benign and malignant ovarian tumors
- Vol. 30, Issue 6, Pages: 489-493(2021)
- 作者机构：
  
  1. 上海市宝山区中西医结合医院超声科,上海,201999
  2. 上海交通大学附属第一人民医院超声科,上海,201620
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2021.06.010
  CLC：
- Published Online：28 December 2021，
  
  Published：28 December 2021
- 稿件说明：
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朱征涛,郑丽,江鑫辉,等. 基于IOTA简易原则的logistic回归模型在卵巢肿瘤鉴别诊断中的应用[J]. 肿瘤影像学, 2021, 30(6): 489-493 https://doi.

org/10.19732/j.cnki.2096-6210.2021.06.010
朱征涛,郑丽,江鑫辉,等. 基于IOTA简易原则的logistic回归模型在卵巢肿瘤鉴别诊断中的应用[J]. 肿瘤影像学, 2021, 30(6): 489-493 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.06.010.

org/10.19732/j.cnki.2096-6210.2021.06.010 DOI：

摘要

目的：

探讨基于国际卵巢肿瘤分析（International Ovarian Tumor Analysis，IOTA）组织简易原则的logistic回归模型在卵巢肿瘤诊断中的临床价值。

方法：

回顾并分析经超声检查并手术后病理学检查证实的221个卵巢肿瘤，由2名对病理学检查结果不知情的超声科医师分析并记录超声形态学特征，应用logistic模型计算每个肿瘤的恶性概率，对照病理学检查结果，计算诊断效能。

结果：

221个卵巢肿瘤包括168个良性肿瘤、37个恶性肿瘤以及16个交界性肿瘤。若将交界性肿瘤与恶性肿瘤归为非良性组，预测概率30%为阈值，则logistic回归模型判断肿瘤良恶性的灵敏度、特异度、阳性预测值、阴性预测值及准确度分别为73.6%、100%、100%、92.3%和93.7%。

结论：

基于IOTA简易原则的logistic回归模型是诊断卵巢肿瘤的可靠工具，但对交界性肿瘤的判断尚有不足，需结合医师的经验或其他评估手段。

Abstract

Objective:

To explore the value of logistic regression model derived from International Ovarian Tumor Analysis (IOTA) group simple rules in differentiation of benign and malignant ovarian tumors.

Methods:

A total of 221 ovarian tumors with pathologic results were retrospectively analyzed. Two ultrasound physicians who blinded to the pathologic results summarized the ultrasound characteristics of each tumor. The probability of malignancy was calculated using the logistic regression model. The diagnostic efficacy was obtained

by the comparison of malignancy probability to the pathologic results.

Results:

A total of 221 ovarian tumors included 168 benign tumors

37 malignant tumors and 16 tumors of borderline malignancy. When borderline tumors were classified as malignant ones and malignant probability of 30% was set as cutoff point

the sensitivity

specificity

positive predictive value

negative predictive value and diagnostic accuracy were 73.6%

100%

92.3% and 93.7%

respectively.

Conclusion:

The logistic regression model derived from IOTA simple rules is a reliable tool for diagnosis of ovarian tumors

but the diagnostic efficacy for borderline tumors remains to be improved

and assistance from experienced radiologists may be needed.

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