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基于多参数磁共振成像的列线图可提高诊断为BI-RADS 4类乳腺病变的诊断性能:与凯泽评分的比较研究

A nomogram based on multiparametric magnetic resonance imaging improves the diagnostic performance of breast lesions diagnosed as BI-RADS category 4: A comparative study with the Kaiser score.

作者信息

Yang Xiao, Lu Zhou, Tan Xiaoying, Shao Lin, Shi Jie, Dou Weiqiang, Sun Zongqiong

机构信息

Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province 214062, China.

GE Healthcare, MR Research China, Beijing 100176, China.

出版信息

Eur J Radiol. 2025 Feb;183:111920. doi: 10.1016/j.ejrad.2025.111920. Epub 2025 Jan 3.

Abstract

PURPOSE

To construct a nomogram combining Kaiser score (KS), synthetic MRI (syMRI) parameters, apparent diffusion coefficient (ADC), and clinical features to distinguish benign and malignant breast lesions better.

METHODS

From December 2022 to February 2024, a retrospective cohort of 168 patients with breast lesions diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 by ultrasound and/or mammography was included. The research population was divided into the training set (n = 117) and the validation set (n = 51) by random sampling with a ratio of 7:3. Breast lesions' KS, ADC, relaxation time of syMRI, and clinical and imaging features were statistically analyzed and compared between malignant and benign groups. Two experienced radiologists independently assigned KS, and measured quantitative values of ADC and parameters of syMRI, and the intraclass correlation coefficient (ICC) was calculated. Independent predictors were identified by univariable and multivariable logistic regression analysis. Then, a nomogram was established, and its performance was evaluated by the area under the curve (AUC), calibration curve, and decision curve.

RESULTS

There were 168 lesions (118 malignant and 50 benign) in 168 female patients confirmed by histopathology. The interobserver agreement for each quantitative parameter was excellent. Older patient (OR = 1.091, 95 % confidence interval [CI]: 1.017-1.170, P = 0.014), higher lesions' KS (OR = 288.431, 95 % CI: 34.930-2381.654, P < 0.001), lower ADC (OR = 0.077, 95 % CI: 0.011-0.558, P = 0.011), and lower T2 relaxation time (OR = 0.918, 95 % CI: 0.868-0.972, P = 0.003) were independent predictors of breast malignancies and utilized to establish the nomogram. The accuracy of KS, ADC, T2, and patient age in predicting malignant breast lesions was 88.89 %, 79.48 %, 82.05 %, and 58.97 %, respectively. No significant differences in AUCs of KS, ADC and T2 were observed in distinguishing benign from malignant breast lesions. The nomogram yielded higher AUCs of 0.968 (0.934-0.996) and 0.959 (0.863-0.995) in training and validation sets than KS, ADC, T2, and patient age (p < 0.05).

CONCLUSION

Although there were no significant differences among the AUCs of KS, ADC, and T2, the constructed nomogram incorporating these parameters significantly improves diagnostic performance for distinguishing benign and malignant BI-RADS 4 breast lesions. Future external validation is needed in practical applications.

摘要

目的

构建一种列线图,结合凯泽评分(KS)、合成磁共振成像(syMRI)参数、表观扩散系数(ADC)和临床特征,以更好地区分乳腺良恶性病变。

方法

纳入2022年12月至2024年2月期间经超声和/或乳腺X线摄影诊断为乳腺影像报告和数据系统(BI-RADS)4类的168例乳腺病变患者的回顾性队列。通过7:3的随机抽样将研究人群分为训练集(n = 117)和验证集(n = 51)。对乳腺病变的KS、ADC、syMRI的弛豫时间以及临床和影像特征在恶性和良性组之间进行统计分析和比较。两名经验丰富的放射科医生独立分配KS,并测量ADC的定量值和syMRI的参数,并计算组内相关系数(ICC)。通过单变量和多变量逻辑回归分析确定独立预测因素。然后,建立列线图,并通过曲线下面积(AUC)、校准曲线和决策曲线评估其性能。

结果

168例女性患者的168个病变经组织病理学证实,其中118个为恶性,50个为良性。各定量参数的观察者间一致性良好。年龄较大的患者(OR = 1.091,95%置信区间[CI]:1.017 - 1.170,P = 0.014)、病变KS较高(OR = 288.431,95% CI:34.930 - 2381.654,P < 0.001)、ADC较低(OR = 0.077,95% CI:0.011 - 0.558,P = 0.011)以及T2弛豫时间较低(OR = 0.918,95% CI:0.868 - 0.972,P = 0.003)是乳腺恶性肿瘤的独立预测因素,并用于建立列线图。KS、ADC、T2和患者年龄预测乳腺恶性病变的准确率分别为88.89%、79.48%、82.05%和58.97%。在区分乳腺良恶性病变方面,KS、ADC和T2的AUCs无显著差异。列线图在训练集和验证集中的AUCs分别为0.968(0.934 - 0.996)和0.959(0.863 - 0.995),高于KS、ADC、T2和患者年龄(P < 0.05)。

结论

虽然KS、ADC和T2的AUCs之间无显著差异,但纳入这些参数构建的列线图显著提高了区分BI-RADS 4类乳腺良恶性病变的诊断性能。实际应用中需要未来进行外部验证。

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