Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

An MRI-based Radiomics Classifier For Preoperative Prediction Of Ki-67 Status In Breast Cancer.

Cuishan Liang, Zi-xuan Cheng, Yanqi Huang, L. He, X. Chen, Zelan Ma, X. Huang, Changhong Liang, Zaiyi Liu
Published 2018 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
Share
RATIONALE AND OBJECTIVES This study aims to investigate the value of a magnetic resonance imaging-based radiomics classifier for preoperatively predicting the Ki-67 status in patients with breast cancer. MATERIALS AND METHODS We chronologically divided 318 patients with clinicopathologically confirmed breast cancer into a training dataset (n = 200) and a validation dataset (n = 118). Radiomics features were extracted from T2-weighted (T2W) and contrast-enhanced T1-weighted (T1+C) images of breast cancer. Radiomics feature selection and radiomics classifiers were generated using the least absolute shrinkage and selection operator regression analysis method. The correlation between the radiomics classifiers and the Ki-67 status in patients with breast cancer was explored. The predictive performances of the radiomics classifiers for the Ki-67 status were evaluated with receiver operating characteristic curves in the training dataset and validated in the validation dataset. RESULTS Through the radiomics feature selection, 16 and 14 features based on T2W and T1+C images, respectively, were selected to constitute the radiomics classifiers. The radiomics classifier based on T2W images was significantly correlated with the Ki-67 status in both the training and the validation datasets (both P < .0001). The radiomics classifier based on T1+C images was significantly correlated with the Ki-67 status in the training dataset (P < .0001) but not in the validation dataset (P = .083). The T2W image-based radiomics classifier exhibited good discrimination for Ki-67 status, with areas under the receiver operating characteristic curves of 0.762 (95% confidence interval: 0.685, 0.838) and 0.740 (95% confidence interval: 0.645, 0.836) in the training and validation datasets, respectively. CONCLUSIONS The T2W image-based radiomics classifier was a significant predictor of Ki-67 status in patients with breast cancer. Thus, it may serve as a noninvasive approach to facilitate the preoperative prediction of Ki-67 status in clinical practice.
This paper references
10.1200/JCO.2010.31.2835
Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer.
J. Cuzick (2011)
10.1067/J.CPRADIOL.2006.12.001
Benign breast lesions that simulate malignancy: magnetic resonance imaging with radiologic-pathologic correlation.
A. Iglesias (2007)
10.1109/ICSNS.2015.7292421
A comparative study on the swarm intelligence based feature selection approaches for fake and real fingerprint classification
V. Sasikala (2015)
10.1186/s13000-016-0525-z
Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer
J. Besusparis (2016)
10.1148/RADIOL.2442051620
The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice.
C. Kuhl (2007)
10.1097/PAI.0000000000000315
Intratumoral Heterogeneity for Ki-67 Index in Invasive Breast Carcinoma: A Study on 131 Consecutive Cases
M. Boros (2017)
10.1148/radiol.2015151169
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
10.1259/bjr.20170441
Abbreviated breast MRI for screening women with dense breast: the EA1141 trial
C. Kuhl (2018)
10.1148/RADIOL.2016152110
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
H. Li (2016)
10.1038/ncomms5006
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1007/s00330-014-3150-9
Prostatic ductal adenocarcinoma: an aggressive tumour variant unrecognized on T2 weighted magnetic resonance imaging (MRI)
N. Schieda (2014)
10.1002/1097-0142(19891215)64:12<2501::AID-CNCR2820641216>3.0.CO;2-0
Assessment of cell proliferation in colorectal carcinomas using the monoclonal antibody KI‐67. Correlation with pathohistologic criteria and influence of irradiation
R. Porschen (1989)
10.1002/jmri.25606
Prediction of malignancy by a radiomic signature from contrast agent‐free diffusion MRI in suspicious breast lesions found on screening mammography.
S. Bickelhaupt (2017)
10.1200/JCO.2015.65.2289
Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline.
Lyndsay N. Harris (2016)
10.1148/rg.302095044
Radiologic and pathologic findings in breast tumors with high signal intensity on T2-weighted MR images.
G. Santamaría (2010)
10.1016/j.acra.2014.06.011
Small hepatocellular carcinoma: MRI findings for predicting tumor growth rates.
R. Jha (2014)
10.2214/AJR.184.4.01841274
MRI of metaplastic carcinoma of the breast.
M. Velasco (2005)
10.1200/JCO.2015.65.9128
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Yanqi Huang (2016)
10.1118/1.3651635
Improving suspicious breast lesion characterization using semi-automatic lesion fractional volume washout kinetic analysis.
J. Huang (2011)
10.1200/JCO.2007.14.2364
American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer.
L. Harris (2007)
10.1200/JCO.2008.17.0829
Prognostic and predictive value of centrally reviewed Ki-67 labeling index in postmenopausal women with endocrine-responsive breast cancer: results from Breast International Group Trial 1-98 comparing adjuvant tamoxifen with letrozole.
G. Viale (2008)
10.1148/RADIOL.2016152234
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Yanqi Huang (2016)
10.1200/JCO.2016.69.4406
Ki67 Proliferation Index as a Tool for Chemotherapy Decisions During and After Neoadjuvant Aromatase Inhibitor Treatment of Breast Cancer: Results From the American College of Surgeons Oncology Group Z1031 Trial (Alliance)
M. Ellis (2017)
10.1038/npjbcancer.2016.12
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
H. Li (2016)
10.1016/J.ORALONCOLOGY.2004.10.009
Heterogeneity, histological features and DNA ploidy in oral carcinoma by image-based analysis.
N. Diwakar (2005)
10.1016/S1470-2045(12)70211-5
Global cancer transitions according to the Human Development Index (2008-2030): a population-based study.
F. Bray (2012)
10.1097/RLI.0000000000000057
A Novel Approach to Contrast-Enhanced Breast Magnetic Resonance Imaging for Screening: High-Resolution Ultrafast Dynamic Imaging
R. Mann (2014)
10.3348/kjr.2014.15.5.591
Intratumoral Heterogeneity of Breast Cancer Xenograft Models: Texture Analysis of Diffusion-Weighted MR Imaging
B. Yun (2014)
10.1097/CAD.0000000000000123
The role of Ki-67 in the proliferation and prognosis of breast cancer molecular classification subtypes
G. Stathopoulos (2014)
10.1038/s41598-017-01524-7
Metabolic Radiomics for Pretreatment 18F-FDG PET/CT to Characterize Locally Advanced Breast Cancer: Histopathologic Characteristics, Response to Neoadjuvant Chemotherapy, and Prognosis
Seunggyun Ha (2017)
10.1007/s10549-017-4264-y
Abbreviated breast dynamic contrast-enhanced MR imaging for lesion detection and characterization: the experience of an Italian oncologic center
A. Petrillo (2017)
10.3322/caac.21262
Global cancer statistics, 2012
L. Torre (2015)
10.1093/JNCI/DJK020
Prognostic value of Ki67 expression after short-term presurgical endocrine therapy for primary breast cancer.
M. Dowsett (2007)
10.1016/j.crad.2016.09.013
Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging.
E. Sala (2017)
10.18632/oncotarget.8919
The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer
Cuishan Liang (2016)
10.1148/radiol.12120254
Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival.
F. Ng (2013)
10.1007/s00261-015-0438-4
CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes
M. Lubner (2015)
10.2307/1390657
On the LASSO and its dual
M. R. Osborne (2000)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
10.1093/annonc/mdr304
Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011
A. Goldhirsch (2011)



This paper is referenced by
10.1007/s11547-019-01100-1
Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps
Y. Zhang (2019)
10.21147/j.issn.1000-9604.2019.05.10
Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: A prospective study
Z. Ye (2019)
10.1007/s00259-019-04372-x
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
M. Sollini (2019)
10.1007/978-3-030-51156-2_131
Classification of Breast DCE-MRI Images via Boosting and Deep Learning Based Stacking Ensemble Approach
Ahmet Haşim Yurttakal (2020)
10.1016/j.acra.2019.05.002
Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging.
Hongbing Luo (2019)
10.1186/s40169-020-0263-4
Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort
Qing-Wei Zhang (2020)
10.3233/XST-180488
A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer.
Hong-xia Zhang (2019)
10.21037/jtd.2019.11.52
Characteristics of contrast-enhanced ultrasonography and strain elastography of locally advanced breast cancer.
Li-Shuang Gu (2019)
10.1177/1533033820916191
The Application of Radiomics in Breast MRI: A Review
Dong-Man Ye (2020)
10.3348/kjr.2019.0855
Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review
S. H. Lee (2020)
10.1016/j.acra.2020.02.012
Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach.
J. Dong (2020)
10.1038/s41598-020-60822-9
MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer
S. Kim (2020)
10.2217/bmm-2020-0248
Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics.
Yao Wang (2020)
10.1007/978-3-319-94878-2
Artificial Intelligence in Medical Imaging
E. Ranschaert (2019)
10.3389/fonc.2020.531476
Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment
W. Hao (2020)
10.1016/j.acra.2020.02.006
Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning.
Weixiao Liu (2020)
10.1259/bjr.20200358
A preoperative radiomics model for the identification of lymph node metastasis in patients with Early-stage cervical squamous cell carcinoma.
L. Yan (2020)
10.1016/j.acra.2020.10.015
Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer.
Jie Ding (2020)
10.1155/2018/6120703
A New Challenge for Radiologists: Radiomics in Breast Cancer
P. Crivelli (2018)
10.1016/j.crad.2020.01.012
Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma.
A. A. Ahmed (2020)
10.1002/jmri.26852
Machine learning in breast MRI
B. Reig (2019)
10.35711/aimi.v1.i1.6
Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art
Alessia Angela Maria Orlando (2020)
10.1016/J.EJRAD.2019.06.025
Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
Qianbiao Gu (2019)
10.1016/j.crad.2019.12.021
Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study.
M. Matsuda (2020)
10.1177/1179299X19851513
Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry
D. Woolf (2019)
10.1002/jmri.26981
Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue
J. Zhou (2019)
Semantic Scholar Logo Some data provided by SemanticScholar