高级检索
当前位置: 首页 > 详情页

Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE

机构: [1]Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China. [2]Department of Radiology, Sichuan Provincial Maternity and Child Health Care Hospital, Chengdu, China. [3]Department of Radiology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
出处:

关键词: Magnetic resonance imaging Radiomics Breast cancer

摘要:
Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.© 2025. The Author(s).

基金:
语种:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2025]版:
大类 | 3 区 综合性期刊
小类 | 3 区 综合性期刊
最新[2025]版:
大类 | 3 区 综合性期刊
小类 | 3 区 综合性期刊
JCR分区:
出版当年[2025]版:
最新[2023]版:
Q1 MULTIDISCIPLINARY SCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2024版] 出版当年五年平均 出版前一年[2024版]

第一作者:
第一作者机构: [1]Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
共同第一作者:
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

相关文献

[1]Magnetic Resonance Imaging-Based Radiomics Models to Predict Early Extrapancreatic Necrosis in Acute Pancreatitis. [2]Quantifying the reproducibility and longitudinal repeatability of radiomics features in magnetic resonance Image-Guide accelerator Imaging: A phantom study [3]Mammography combined with breast dynamic contrast-enhanced-magnetic resonance imaging for the diagnosis of early breast cancer [4]Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma [5]Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer. [6]Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma. [7]A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging. [8]Comparison of dosimetry with magnetic resonance and computed tomography imaging delineation of surgical bed volume in breast cancer irradiation [9]MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study [10]MRI-based radiomics to compare the survival benefit of induction chemotherapy plus concurrent chemoradiotherapy versus concurrent chemoradiotherapy plus adjuvant chemotherapy in locoregionally advanced nasopharyngeal carcinoma: A multicenter study.

资源点击量:59059 今日访问量:0 总访问量:4849 更新日期:2025-06-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持:重庆聚合科技有限公司 地址:成都市人民南路四段55号