Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer.
Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer.
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DOI：
10.1016/j.breast.2021.09.005
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Yao H
中科院分区：
文献类型：
--
作者：
Yang Y;Li J;Liu Y;Zhong Y;Ren W;Tan Y;He Z;Li C;Ouyang J;Hu Q;Yu Y;Yao H
三分之一的激素受体（HR）阳性乳腺癌患者对激素治疗没有反应，一些患者甚至在辅助内分泌治疗（ET）的两年内进展为原发性内分泌抵抗。然而，没有有效的方法来预测内分泌抵抗。建立一个模型，将治疗前磁共振成像（MRI）的放射组学特征与临床信息相结合，以预测内分泌抵抗。回顾性收集了中国三家医院2015年5月1日至2018年12月31日期间确诊的非转移性乳腺癌患者的临床数据和术前动态增强磁共振成像（DCE-MRI）。将显著的临床病理特征和放射组学特征纳入多变量logistic回归，以建立组合模型来预测训练集中的内分泌抵抗，并验证内部和外部验证集。共纳入来自中国三家医院的744例女性非转移性乳腺癌患者。在训练队列中，放射组学-临床联合模型预测内分泌抵抗的AUC为0.975，高于临床模型（0.849）和IHC 4模型（0.682），与放射组学模型（0.941）相似。此外，内部（0.921）和外部验证队列（0.955）中组合模型的AUC高于临床模型和IHC 4模型。联合模型的灵敏度高于单用放射组学模型，并得到了AUC的最佳阈值。本研究开发并验证了一个基于MRI的预处理多参数放射学-临床联合模型，并在预测内分泌抵抗方面表现出良好的性能。本研究首次建立了基于放射组学的非转移性乳腺癌内分泌抵抗预测模型。该模型是包含多参数MRI放射组学特征和临床特征的组合模型。联合模型预测内分泌抵抗的AUC为0.975，具有较大的临床应用潜力。
One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance. To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. Clinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set. A total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC. This study developed and validated a pretreatment multiparametric MRI-based radiomic-clinical combined model and showed good performance in predicting endocrine resistance. This study first established a model to predict endocrine resistance in non-metastatic breast cancer based on radiomic. This model was a combined model that contain multiparametric MRI radiomics features and clinical features. The AUC of the combined model to predict endocrine resistance was 0.975 , with great potential in clinical applications.
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DOI：
10.1093/jnci/djn309
发表时间：
2008-10-01
期刊：
Journal of the National Cancer Institute
影响因子：
--
作者：
Ellis MJ;Tao Y;Luo J;A'Hern R;Evans DB;Bhatnagar AS;Chaudri Ross HA;von Kameke A;Miller WR;Smith I;Eiermann W;Dowsett M
通讯作者：
Dowsett M
影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
DOI：
10.1200/jco.20.02514
发表时间：
2020-12-01
期刊：
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
影响因子：
--
作者：
Johnston SRD;Harbeck N;Hegg R;Toi M;Martin M;Shao ZM;Zhang QY;Martinez Rodriguez JL;Campone M;Hamilton E;Sohn J;Guarneri V;Okada M;Boyle F;Neven P;Cortés J;Huober J;Wardley A;Tolaney SM;Cicin I;Smith IC;Frenzel M;Headley D;Wei R;San Antonio B;Hulstijn M;Cox J;O'Shaughnessy J;Rastogi P;monarchE Committee Members and Investigators
通讯作者：
monarchE Committee Members and Investigators
影响因子：
3.8
作者：
Nielsen, Kirsten Vang;Ejlertsen, Bent;Mouridsen, Henning T.
通讯作者：
Mouridsen, Henning T.
DOI：
10.1007/bf02967635
发表时间：
2003-01-01
期刊：
Breast cancer (Tokyo, Japan)
影响因子：
--
作者：
Kurebayashi, Junichi
通讯作者：
Kurebayashi, Junichi