Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study
Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study
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DOI：
10.1007/s00330-023-09513-3
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发表时间：
2023-04
影响因子：
5.9
通讯作者：
S. Zhang;Huafei Shao;Wenjuan Li;Haicheng Zhang;Fan Lin;Qianqian Zhang;Han Zhang;Zhongyi Wang-Zhongyi-W
中科院分区：
文献类型：
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作者：
S. Zhang;Huafei Shao;Wenjuan Li;Haicheng Zhang;Fan Lin;Qianqian Zhang;Han Zhang;Zhongyi Wang-Zhongyi-W
目的构建和测试一个诺模图的基础上，肿瘤内和周围的放射组学和临床因素预测恶性BiRADS 4病变的对比增强光谱mammography.MethodsA共884例BiRADS 4病变从两个中心。对于每个病变，使用肿瘤内区域（ITR）、肿瘤周围5 mm和10 mm的瘤周区域（PTR）以及ITR +5 mm和10 mm的PTR定义5个ROI。在选择特征之后，通过LASSO建立了五个放射组学特征。通过多变量逻辑回归分析，使用选定的签名和临床因素建立列线图。通过AUC、决策曲线分析和校准曲线评估诺模图的性能，并与放射组学模型、临床模型和放射科医生进行比较。（由ITR，5 mm PTR，和ITR + 10 mm PTR）和两个临床因素（年龄和BiRADS类别）在内部和外部测试集中显示出强大的预测能力，AUC分别为0.907和0.904。校正曲线，决策曲线分析，显示出良好的诺模图的预测性能。结论通过瘤内和瘤周放射组学特征及临床危险因素建立的诺模图在区分良恶性BiRADS 4病变方面具有最佳性能，这可以帮助放射科医生提高诊断能力。关键点·对比肿瘤周围区域的放射组学特征-增强的光谱乳腺X射线摄影图像可为良性和恶性乳腺成像报告和数据系统4类乳腺病变的诊断提供有价值的信息。结合瘤内和瘤周放射组学特征和临床变量的诺模图在辅助临床决策方面具有良好的应用前景。
ObjectiveTo construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography.MethodsA total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists.ResultsThe nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram.ConclusionThe nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities.Key Points•Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions.•The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.