MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.
MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.
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DOI：
10.1016/j.ebiom.2020.103042
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发表时间：
2020-11
期刊：
影响因子：
11.1
通讯作者：
Jochelson MS
中科院分区：
文献类型：
--
作者：
Bitencourt AGV;Gibbs P;Rossi Saccarelli C;Daimiel I;Lo Gullo R;Fox MJ;Thakur S;Pinker K;Morris EA;Morrow M;Jochelson MS
使用临床和MRI放射组学特征结合机器学习评估接受新辅助化疗（NAC）的HER 2过表达乳腺癌患者的HER 2表达水平并预测病理学缓解（pCR）。这项回顾性研究包括311例患者。pCR定义为乳腺或腋窝淋巴结中无残留浸润性癌（ypT 0/isN 0）。使用MATLAB和CERR软件进行放射组学/统计分析。在ROC和相关性分析后，将选定的放射组学参数与基于MRI的临床参数（病变类型、多灶性、大小、淋巴结状态）一起进行机器学习建模。为了预测pCR，将数据分成训练集和测试集（80：20）。总pCR率为60.5%（188/311）。预测HER 2异质性的最终模型使用了三个MRI参数（两个临床参数，一个放射组学参数），灵敏度为99.3%（277/279），特异性为81.3%（26/32），诊断准确性为97.4%（303/311）。预测pCR的最终模型包括六个MRI参数两种诊断方法（两种临床方法，四种放射学方法）的敏感性为86.5%（32/37），特异性为80.0%（20/25），诊断准确性为83.9%（52/62）（test set）;这些结果与年龄和ER状态无关，并且优于仅使用临床参数开发的最佳模型（p=0.029，比例卡方检验比较）。机器学习模型，包括临床和放射组学MRI特征，可用于评估HER 2表达水平，并可预测HER 2过表达乳腺癌患者NAC后的pCR。NIH/NCI（P30CA008748），Susan G.科门基金会，乳腺癌研究基金会，西班牙基金会阿方索马丁埃斯库德罗，欧洲放射学院。
To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.
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DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
6.6
作者：
Rye IH;Trinh A;Saetersdal AB;Nebdal D;Lingjaerde OC;Almendro V;Polyak K;Børresen-Dale AL;Helland Å;Markowetz F;Russnes HG
通讯作者：
Russnes HG
影响因子：
5
作者：
Ha, Richard;Jin, Brian;Wynn, Ralph
通讯作者：
Wynn, Ralph
影响因子：
3.1
作者：
Leithner, Doris;Bernard-Davila, Blanca;Pinker, Katja
通讯作者：
Pinker, Katja
影响因子：
4.6
作者：
Wolff, Antonio C.;Hammond, M. Elizabeth Hale;Dowsett, Mitchell
通讯作者：
Dowsett, Mitchell