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利用放射组学鉴别肢体脂肪瘤与非典型脂肪瘤性肿瘤

Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics.

作者信息

Tang Yaozhou, Cui Jingjing, Zhu Jingyi, Fan Guoguang

机构信息

Department of Radiology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China.

Department of Research and Development, Shanghai United Imaging Intelligence, Shanghai, China.

出版信息

J Magn Reson Imaging. 2022 Dec;56(6):1746-1754. doi: 10.1002/jmri.28167. Epub 2022 Mar 29.

Abstract

BACKGROUND

The differentiation of soft tissue lipomas from atypical lipoma tumors (ALTs) of the extremities is important because of the distinction of the cytogenetic profiles and the treatment decisions.

PURPOSE

To investigate a radiomics method to differentiate between lipomas and ALTs of the extremities.

STUDY TYPE

Retrospective.

POPULATION

Imaging data of 122 patients including 90 cases of lipomas and 32 cases of ALTs.

FIELD STRENGTH/SEQUENCE: Axial T1-weighted imaging and fat suppressed T2-weighted imaging at 3.0T MRI.

ASSESSMENT

Analysis of variance and the least absolute shrinkage and selection operator methods were used for feature selection and the random forest method was used to build three radiomics models based on T1WI, FS T2WI, and their combination (T1&T2WI). Three independent radiologists classified the tumors based on the subjective assessments.

STATISTICAL TESTS

The area under the curve (AUC) of the receiver operating characteristic curve, accuracy, F1-score, specificity, and sensitivity were employed. The differences of the classifiers and discriminating ability of the radiologists and the radiomics model were compared by Delong test. A P value <0.05 was considered significant. Kappa test was used to determine the inter-reader agreements between the radiologists.

RESULT

The AUCs were 0.952 (95% confidence interval [CI]: 0.785-0.998), 0.944 (95% CI: 0.774-0.997), and 0.968 (95% CI: 0.809-1) for T1WI, FS T2WI, and T1&T2WI models in testing sets respectively. Delong test showed there were no significant difference between the different radiomics models (P > 0.05). The AUCs of the radiologists were 0.893 (95% CI: 0.824-0.942), 0.831 (95% CI: 0.752-0.893), and 0.893 (95% CI: 0.824-0.94), respectively. There were significant difference between radiomics model and radiologists' model in the training and entire cohorts (P < 0.05) while there were no significant difference in the testing sets (P > 0.05).

DATA CONCLUSION

Radiomics has the potential to distinguish between lipomas and ALTs of the extremities and their discrimination ability is no weaker than the senor radiologists.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

摘要

背景

由于细胞遗传学特征的差异和治疗决策的不同,区分软组织脂肪瘤和四肢非典型脂肪瘤肿瘤(ALT)很重要。

目的

研究一种用于区分四肢脂肪瘤和ALT的放射组学方法。

研究类型

回顾性研究。

研究对象

122例患者的影像数据,包括90例脂肪瘤和32例ALT。

场强/序列:3.0T MRI的轴向T1加权成像和脂肪抑制T2加权成像。

评估

采用方差分析和最小绝对收缩与选择算子方法进行特征选择,并使用随机森林方法基于T1WI、FS T2WI及其组合(T1&T2WI)构建三个放射组学模型。三名独立的放射科医生根据主观评估对肿瘤进行分类。

统计检验

采用受试者操作特征曲线下面积(AUC)、准确性、F1分数、特异性和敏感性。通过德龙检验比较分类器之间的差异以及放射科医生和放射组学模型的鉴别能力。P值<0.05被认为具有统计学意义。使用kappa检验确定放射科医生之间的阅片者间一致性。

结果

测试集中T1WI、FS T2WI和T1&T2WI模型的AUC分别为0.952(95%置信区间[CI]:0.785-0.998)、0.944(95%CI:0.774-0.997)和0.968(95%CI:0.809-1)。德龙检验显示不同放射组学模型之间无显著差异(P>0.05)。放射科医生的AUC分别为0.893(95%CI:0.824-0.942)、0.831(95%CI:0.752-0.893)和0.893(95%CI:0.824-0.94)。在训练集和整个队列中,放射组学模型与放射科医生的模型之间存在显著差异(P<0.05),而在测试集中无显著差异(P>0.05)。

数据结论

放射组学有潜力区分四肢脂肪瘤和ALT,其鉴别能力不弱于资深放射科医生。

证据水平

3 技术效能阶段:2。

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