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MRI Radiomic Features: Association With Disease-Free Survival In Patients With Triple-Negative Breast Cancer

Sungwon Kim, M. J. Kim, E. Kim, J. Yoon, V. Park
Published 2020 · Medicine

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Radiomic features hold potential to improve prediction of disease-free survival (DFS) in triple-negative breast cancer (TNBC) and may show better performance if developed from TNBC patients. We aimed to develop a radiomics score based on MRI features to estimate DFS in patients with TNBC. A total of 228 TNBC patients who underwent preoperative MRI and surgery between April 2012 and December 2016 were included. Patients were temporally divided into the training (n = 169) and validation (n = 59) set. Radiomic features of the tumor were extracted from T2-weighted and contrast-enhanced T1- weighted MRI. Then a radiomics score was constructed with the least absolute shrinkage and selection operator regression in the training set. Univariate and multivariate Cox proportional hazards models were used to determine what associations the radiomics score and clinicopathologic variables had with DFS. A combined clinicopathologic-radiomic (CCR) model was constructed based on multivariate Cox analysis. The incremental values of the radiomics score were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC) and bootstrapping (n = 1000). The radiomics score, which consisted of 5 selected MRI features, was significantly associated with worse DFS in both the training and validation sets ( p = 0.002, p = 0.033, respectively). In both the training and validation set, the radiomics score showed comparable performance with the clinicopathologic model. The CCR model demonstrated better performance than the clinicopathologic model in the training set (iAUC, 0.844; difference in iAUC, p  < 0.001) and validation set (iAUC, 0.765, difference in iAUC, p  < 0.001). In conclusion, MRI-based radiomic features can improve the prediction of DFS when integrated with clinicopathologic data in patients with TNBC.
This paper references
A multicenter study
Lola Falcón-Neyra (2016)
10.1158/1078-0432.CCR-18-3190
Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study
Z. Liu (2019)
10.1007/s00330-017-5005-7
Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI
Y. Dong (2017)
10.1158/1078-0432.CCR-17-3783
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Hyunjin Park (2018)
10.1007/s10549-013-2461-x
The prognostic impact of age in patients with triple-negative breast cancer
C. Liedtke (2013)
MR Imaging Texture Analysis and Survival Outcomes
Kim (2020)
impact of subtype
A. M. Schmitz (2014)
10.1007/s10549-013-2560-8
Ki-67 is a prognostic parameter in breast cancer patients: results of a large population-based cohort of a cancer registry
E. Inwald (2013)
10.1148/RADIOL.2016152331
Pretreatment MR Imaging Features of Triple-Negative Breast Cancer: Association with Response to Neoadjuvant Chemotherapy and Recurrence-Free Survival.
M. Bae (2016)
10.1088/0031-9155/61/13/R150
Applications and limitations of radiomics.
S. Yip (2016)
10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
The lasso method for variable selection in the Cox model.
R. Tibshirani (1997)
10.1002/jmri.26701
Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences
Ruimei Chai (2019)
Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Park (2018)
10.1002/jmri.26732
Characterization of Sub‐1 cm Breast Lesions Using Radiomics Analysis
P. Gibbs (2019)
10.1038/35021093
Molecular portraits of human breast tumours
C. Perou (2000)
10.2214/AJR.16.16476
Perfusion Parameters on Breast Dynamic Contrast-Enhanced MRI Are Associated With Disease-Specific Survival in Patients With Triple-Negative Breast Cancer.
Vivian Youngjean Park (2017)
CURRENT STATUS AND FUTURE PERSPECTIVES
María Blanca Fernández-Viñéa (2018)
10.1148/radiol.2016160261
Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes.
J. Kim (2017)
10.1002/jmri.26224
Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast‐enhanced MRI
C. Liu (2019)
10.1200/JCO.2008.18.1370
Supervised risk predictor of breast cancer based on intrinsic subtypes.
J. Parker (2009)
10.5858/arpa.2013-0953-SA
Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update.
A. Wolff (2014)
Treatment of HER 2-positive Breast Cancer
Maria Cristina Figueroa-Magalhães (2015)
initial results
Leithner (2019)
10.1200/JCO.18.01010
Tumor-Infiltrating Lymphocytes and Prognosis: A Pooled Individual Patient Analysis of Early-Stage Triple-Negative Breast Cancers.
S. Loi (2019)
10.1371/journal.pone.0143308
Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study
J. Wang (2015)
10.1016/S0167-9473(98)00096-6
An application of changepoint methods in studying the effect of age on survival in breast cancer
C. Contal (1999)
10.1093/annonc/mdv221
Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015
A. Coates (2015)
10.1634/theoncologist.2017-0095
Targeting the Molecular Subtypes of Triple Negative Breast Cancer: Understanding the Diversity to Progress the Field.
C. Yam (2017)
10.1038/nrclinonc.2011.177
Treatment of HER2-positive breast cancer: current status and future perspectives
C. Arteaga (2012)
10.2307/2529310
The measurement of observer agreement for categorical data.
J. Landis (1977)
10.1016/J.YPAT.2012.10.013
Comprehensive molecular portraits of human breast tumours
A. McCullough (2013)
10.1200/JCO.2009.25.6529
American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer.
M. Hammond (2010)
10.1056/NEJMra1001389
Triple-negative breast cancer.
W. Foulkes (2010)
10.1158/0008-5472.CAN-17-0339
Computational Radiomics System to Decode the Radiographic Phenotype.
J. van Griethuysen (2017)
10.1007/s10549-014-3170-9
Association between rim enhancement of breast cancer on dynamic contrast-enhanced MRI and patient outcome: impact of subtype
A. Schmitz (2014)
10.1158/1078-0432.CCR-06-3045
Triple-Negative Breast Cancer: Clinical Features and Patterns of Recurrence
R. Dent (2007)
10.1007/s00330-012-2425-2
Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes
J. H. Youk (2012)
A Pilot Radiomics Study
Wang (2015)
comparison with other breast cancer subtypes
J. H. Youk (2012)
10.1007/s10549-017-4143-6
MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case–control study
S. Song (2017)
10.1186/s13058-019-1187-z
Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results
D. Leithner (2019)
10.1016/j.acra.2018.01.006
An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer.
Cuishan Liang (2018)
10.1172/JCI45014
Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies.
B. Lehmann (2011)
10.1148/radiol.2018181352
Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.
D. Truhn (2019)
a casecontrol study
Song (2017)
A Pooled Individual Patient Analysis of Early-Stage Triple-Negative Breast Cancers
Loi (2019)
10.1111/tbj.12182
MR Imaging Features of Triple‐Negative Breast Cancers
J. Sung (2013)
10.1038/nature11412
Comprehensive molecular portraits of human breast tumors
D. Koboldt (2012)



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