Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers.
Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers.
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DOI：
10.1007/s00330-020-06991-7
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发表时间：
2020-12
影响因子：
5.9
通讯作者：
Pinker K
中科院分区：
文献类型：
--
作者：
Lo Gullo R;Daimiel I;Rossi Saccarelli C;Bitencourt A;Gibbs P;Fox MJ;Thakur SB;Martinez DF;Jochelson MS;Morris EA;Pinker K
研究从BRCA阳性亚厘米级乳腺肿块患者的MRI中提取的放射组学特征是否可以与机器学习相结合，以使用无模型参数图区分良性和恶性病变。在这项回顾性研究中，纳入了2013年11月至2019年2月接受MRI检查的BRCA阳性患者，这些患者导致亚厘米病变的活检（BI-RADS 4）或成像随访（BI-RADS 3）。两名放射科医生根据BI-RADS独立评估所有病变，并达成共识。使用开源CERR软件计算放射组学特征。进行单变量分析和多变量建模，以识别要包括在机器学习模型中的显著放射组学特征和临床因素，以区分恶性病变和良性病变。纳入96例BRCA突变携带者（活检时平均年龄= 45.5 ± 13.5岁）。共识BI-RADS分类评估的诊断准确性为53.4%，敏感性为75%（30/40），特异性为42.1%（32/76），PPV为40.5%（30/74），NPV为76.2%（32/42）。结合五个参数的机器学习模型（年龄、病变位置、造影前阶段基于GLCM的相关性、造影后第1阶段的一阶变异系数和造影后第1阶段基于SZM的灰度方差）的诊断准确率为81.5%，灵敏度为63.2%（24/38），特异性为91.4%（64/70），PPV为80.0%（24/30），NPV为82.1%（64/78）。放射组学分析结合机器学习提高了MRI在BRCA突变携带者中将亚厘米级乳腺肿块定性为良性或恶性的诊断准确性，与单独使用BI-RADS分类的定性形态学评估相比。放射组学和机器学习可以帮助区分良性和恶性乳腺肿块，即使肿块很小，形态特征是良性的。放射组学和机器学习分析显示，与单独的定性形态学评估相比，诊断准确性、特异性、PPV和NPV有所提高。本文的在线版本（10.1007/s 00330 -020-06991-7）包含补充材料，可供授权用户使用。
To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone. The online version of this article (10.1007/s00330-020-06991-7) contains supplementary material, which is available to authorized users.
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DOI：
10.1200/jco.2014.56.8626
发表时间：
2015-04-01
期刊：
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
影响因子：
--
作者：
Riedl CC;Luft N;Bernhart C;Weber M;Bernathova M;Tea MK;Rudas M;Singer CF;Helbich TH
通讯作者：
Helbich TH
影响因子：
19.7
作者：
Schrading, Simone;Kuhl, Christiane K.
通讯作者：
Kuhl, Christiane K.
影响因子：
2.6
作者：
Meissnitzer, Matthias;Dershaw, D. David;Morris, Elizabeth A.
通讯作者：
Morris, Elizabeth A.
影响因子：
3.3
作者：
Marino, Maria Adele;Riedl, Christopher C.;Pinker, Katja
通讯作者：
Pinker, Katja
影响因子：
158.5
作者：
Kriege, M;Brekelmans, CTM;Klijn, JGM
通讯作者：
Klijn, JGM