Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status

被引:95
作者
Li, Chunli [1 ,2 ]
Song, Lirong [2 ]
Yin, Jiandong [2 ]
机构
[1] China Med Univ, Sch Fundamental Sci, Dept Biomed Engn, Shenyang, Peoples R China
[2] China Med Univ, Shengjing Hosp, Dept Radiol, Shenyang, Peoples R China
关键词
breast cancer; radiomics; magnetic resonance imaging; Ki‐ 67; HER‐ 2; LYMPH-NODE METASTASIS; NEOADJUVANT CHEMOTHERAPY; ADJUVANT CHEMOTHERAPY; CANCER; EXPRESSION; INVASION; SUBTYPES; KI67; PROLIFERATION; TRASTUZUMAB;
D O I
10.1002/jmri.27651
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated. Purpose To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast-enhanced MRI (DCE-MRI) for prediction of HER-2 and Ki-67 status. Study Type Retrospective. Population A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts. Field Strength/Sequence 3.0T, T-1 gradient echo. Assessment Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time-intensity curves of DCE-MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively. Statistical Tests Wilcoxon rank-sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test. Results The intratumoral and peritumoral RSs for prediction of HER-2 and Ki-67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574-0.793) and 0.690 (95% CI, 0.577-0.804), and 0.714 (95% CI, 0.616-0.812) and 0.692 (95% CI, 0.590-0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604-0.823) and 0.749 (95% CI, 0.656-0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8). Data Conclusion Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE-MRI had the potential to identify HER-2 and Ki-67 status. Technical Efficacy Stage: 2
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收藏
页码:703 / 714
页数:12
相关论文
共 47 条
  • [21] DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers
    Fan, Ming
    Cheng, Hu
    Zhang, Peng
    Gao, Xin
    Zhang, Juan
    Shao, Guoliang
    Li, Lihua
    [J]. JOURNAL OF MAGNETIC RESONANCE IMAGING, 2018, 48 (01) : 237 - 247
  • [22] Expression of PCNA, Ki-67 and COX-2 in breast cancer based on DCE-MRI image information
    Qiu, Xiaoming
    Wang, Hong
    Wang, Zhen
    Fu, Yufei
    Yin, Jianjun
    [J]. JOURNAL OF INFECTION AND PUBLIC HEALTH, 2020, 13 (12) : 2032 - 2037
  • [23] An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy
    Wu, Yimin
    Ma, Qianqing
    Fan, Lifang
    Wu, Shujian
    Wang, Junli
    [J]. ACADEMIC RADIOLOGY, 2024, 31 (01) : 93 - 103
  • [24] Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes
    Hao Xu
    Ao Yang
    Min Kang
    Hua Lai
    Xinzhu Zhou
    Zhe Chen
    Libo Lin
    Peng Zhou
    Heping Deng
    [J]. Scientific Reports, 15 (1)
  • [25] Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study
    Tao Jiang
    Jiangdian Song
    Xiaoyu Wang
    Shuxian Niu
    Nannan Zhao
    Yue Dong
    Xingling Wang
    Yahong Luo
    Xiran Jiang
    [J]. Molecular Imaging and Biology, 2022, 24 : 550 - 559
  • [26] Correlation between Ki-67 index and Her-2 Expression Status in Invasive Ductal Carcinoma of Breast
    Malik, Ayesha Imtiaz
    Kamal, Farrukh
    Basharat, Rabia
    Naz, Asima
    Iqbal, Madiha
    [J]. ANNALS OF KING EDWARD MEDICAL UNIVERSITY LAHORE PAKISTAN, 2022, 28 : 360 - 364
  • [27] Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE-MRI in classifying benign from malignant lesions
    Jiang, Zejun
    Yin, Jiandong
    [J]. JOURNAL OF SURGICAL ONCOLOGY, 2020, 121 (08) : 1181 - 1190
  • [28] Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study
    Zhang, Shuhai
    Wang, Xiaolei
    Yang, Zhao
    Zhu, Yun
    Zhao, Nannan
    Li, Yang
    He, Jie
    Sun, Haitao
    Xie, Zongyu
    [J]. FRONTIERS IN ONCOLOGY, 2022, 12
  • [29] Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer
    Zhong, Shiling
    Wang, Fan
    Wang, Zhiying
    Zhou, Minghui
    Li, Chunli
    Yin, Jiandong
    [J]. DIAGNOSTICS, 2022, 12 (10)
  • [30] Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study
    Zhou, Jing
    Yu, Xuan
    Wu, Qingxia
    Wu, Yaping
    Fu, Cong
    Wang, Yunxia
    Hai, Menglu
    Tan, Hongna
    Wang, Meiyun
    [J]. HELIYON, 2024, 10 (07)