Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
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使用MRI特征预测乳腺癌新辅助治疗的病理反应的多元机器学习模型：使用独立验证集的研究。
DOI：
10.1007/s10549-018-4990-9
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发表时间：
2019-01
影响因子：
3.8
通讯作者：
Mazurowski MA
中科院分区：
文献类型：
--
作者：
Cain EH;Saha A;Harowicz MR;Marks JR;Marcom PK;Mazurowski MA
确定使用治疗前动态对比增强磁共振成像 (DCE-MRI) 计算机提取特征的基于多变量机器学习的模型是否可以预测乳腺癌患者对新辅助治疗 (NAT) 的病理完全缓解 (pCR)。这项对我们机构的 288 名乳腺癌患者进行的回顾性研究获得了机构审查委员会的批准，这些患者接受了 NAT 并进行了治疗前乳腺 MRI。从每位患者的治疗前 MRI 中提取了一套全面的 529 个放射组学特征。将患者分为相等的组，形成训练集和独立测试集。训练两个基于成像特征的多变量机器学习模型（逻辑回归和支持向量机）来预测（a）所有 NAT 患者、（b）新辅助化疗（NACT）患者和（c）三阴性或人表皮生长因子受体 2 阳性（TN/HER2+）NAT 患者的 pCR。使用独立测试集测试多变量模型，并计算受试者工作特征（ROC）曲线下面积（AUC）。在 288 名患者中，64 名达到了 pCR。预测接受 NAT 的 TN/HER+ 患者的 pCR 的 AUC 值显着（.707，95% CI：0.582–0.833，p < 0.002）。基于治疗前 MRI 特征的多变量模型能够预测 TN/HER2+ 患者的 pCR。
To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients. Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient’s pretreatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated. Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (.707, 95%CI: 0.582–0.833, p < 0.002). The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.
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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：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
3.8
作者：
Goorts B;van Nijnatten TJ;de Munck L;Moossdorff M;Heuts EM;de Boer M;Lobbes MB;Smidt ML
通讯作者：
Smidt ML
影响因子：
45.3
作者：
Kaufmann, M;von Minckwitz, G;Senn, HJ
通讯作者：
Senn, HJ
影响因子：
168.9
作者：
Gianni, Luca;Eiermann, Wolfgang;Baselga, Jose
通讯作者：
Baselga, Jose