Rationale and Objectives
Preoperative prediction of LVI status can facilitate personalized therapeutic planning.
This study aims to investigate the efficacy of preoperative MRI-based radiomics for
predicting lymphatic vessel invasion (LVI) determined by D2-40 in patients with invasive
breast cancer.
Materials and Methods
A total of 203 patients with pathologically confirmed invasive breast cancer, who
underwent preoperative breast MRI, were retrospectively enrolled and randomly assigned
to the following cohorts: training cohort (n=141) and test cohort (n=62). Then, univariate and multivariate logistic regression were performed to select
independent risk factors and build a clinical model. Afterwards, least absolute shrinkage
and selection operator (LASSO) logistic regression was performed to select predictive
features extracted from the early and delay enhancement dynamic contrast-enhanced
(DCE)-MRI images, and a radiomics signature was established. Subsequently, a nomogram
model was constructed by incorporating the radiomics score and risk factors. Receiver
operating characteristic curves were performed to determine the performance of various
models. The efficacy of the various models was evaluated using calibration and decision
curves.
Results
Fourteen radiomics features were selected to construct the radiomics model. The size
of the lymph node was identified as an independent risk factor of the clinical model.
The nomogram model demonstrated the best calibration and discrimination performance
in both the training and test cohorts, with an area under the curve of 0.873 (95%
confidence interval [CI]: 0.807-0.923) and 0.902 (95% CI: 0.800-0.963), respectively.
The decision curve illustrated that the nomogram model added more net benefits, when
compared to the radiomics signature and clinical model.
Conclusion
The nomogram model based on preoperative DCE-MRI images exhibits satisfactory efficacy
for the noninvasive prediction of LVI determined by D2-40 in invasive breast cancer.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
REFERENCES
- Clinicopathological evaluation of the potential anatomic pathways of systemic metastasis from primary breast cancer suggests an orderly spread through the regional lymph nodes.Ann Surg Oncol. 2020; 27: 4810-4818https://doi.org/10.1245/s10434-020-08904-w
- Immunohistochemical detection improves the prognostic value of lymphatic and blood vessel invasion in primary ductal breast cancer.BMC Cancer. 2014; 14: 676https://doi.org/10.1186/1471-2407-14-676
- Detection of lymphovascular invasion in early breast cancer by D2-40 (podoplanin): a clinically useful predictor for axillary lymph node metastases.Breast Cancer Res Treat. 2008; 112: 503-511https://doi.org/10.1007/s10549-007-9875-2
- Lympho-vascular invasion impacts the prognosis in breast-conserving surgery: a systematic review and meta-analysis.BMC Cancer. 2022; 22: 102https://doi.org/10.1186/s12885-022-09193-0
- The role of lymphatic and blood vessel invasion in predicting survival and methods of detection in patients with primary operable breast cancer.Crit Rev Oncol Hematol. 2014; 89: 231-241https://doi.org/10.1016/j.critrevonc.2013.08.014
- D2-40 A helpful marker in assessment of lymphatic vessel invasion in carcinoma of breast.Iran J Pathol. 2021; 16: 96-102https://doi.org/10.30699/IJP.2020.114511.2245
- Issues relating to lymphovascular invasion in breast carcinoma.Adv Anat Pathol. 2006; 13: 308-315https://doi.org/10.1097/01.pap.0000213048.69564.26
- Objective assessment of lymphatic and blood vascular invasion in lymph node-negative breast carcinoma: findings from a large case series with long-term follow-up.J Pathol. 2011; 223: 358-365https://doi.org/10.1002/path.2810
- Distinguishing blood and lymph vessel invasion in breast cancer: a prospective immunohistochemical study.Br J Cancer. 2006; 94: 1643-1649https://doi.org/10.1038/sj.bjc.6603152
- Up-regulation of the lymphatic marker podoplanin, a mucin-type transmembrane glycoprotein, in human squamous cell carcinomas and germ cell tumors.Am J Pathol. 2005; 166: 913-921https://doi.org/10.1016/S0002-9440(10)62311-5
- Comparison of the diagnostic accuracy of lymphatic endothelium markers: Bayesian approach.Mod Pathol. 2005; 18: 1490-1497https://doi.org/10.1038/modpathol.3800457
- D2-40 in breast cancer: should we detect more vascular emboli?.Mod Pathol. 2009; 22: 216-222https://doi.org/10.1038/modpathol.2008.151
- Is lymphovascular invasion degree one of the important factors to predict neoadjuvant chemotherapy efficacy in breast cancer?.Breast Cancer. 2011; 18: 309-313https://doi.org/10.1007/s12282-010-0211-z
- Intra-tumoural lipid composition and lymphovascular invasion in breast cancer via non-invasive magnetic resonance spectroscopy.Eur Radiol. 2021; 31: 3703-3711https://doi.org/10.1007/s00330-020-07502-4
- Meta-analysis of MR imaging in the diagnosis of breast lesions.Radiology. 2008; 246: 116-124https://doi.org/10.1148/radiol.2461061298
- Breast MRI: State of the Art.Radiology. 2019; 292: 520-536https://doi.org/10.1148/radiol.2019182947
- Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer.World J Surg Oncol. 2021; 19: 76https://doi.org/10.1186/s12957-021-02189-3
- Breast MRI for prediction of lymphovascular invasion in breast cancer patients with clinically negative axillary lymph nodes.Eur J Radiol. 2018; 107: 111-118https://doi.org/10.1016/j.ejrad.2018.08.024
- Preoperative MRI features associated with lymphovascular invasion in node-negative invasive breast cancer: a propensity-matched analysis.J Magn Reson Imaging. 2017; 46: 1037-1044https://doi.org/10.1002/jmri.25710
- Development of a risk-stratification scoring system for predicting lymphovascular invasion in breast cancer.BMC Cancer. 2020; 20: 94https://doi.org/10.1186/s12885-020-6578-0
- MRI Radiomics of breast cancer: machine learning-based prediction of lymphovascular invasion status.Acad Radiol. 2022; 29: S126-S134https://doi.org/10.1016/j.acra.2021.10.026
- Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma.Eur Radiol. 2022; 32: 4079-4089https://doi.org/10.1007/s00330-021-08504-6
- Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.J Magn Reson Imaging. 2019; 50: 847-857https://doi.org/10.1002/jmri.26688
- Intratumoral and peritumoral radiomics based on functional parametric maps from breast DCE-MRI for prediction of HER-2 and Ki-67 status.J Magn Reson Imaging. 2021; 54: 703-714https://doi.org/10.1002/jmri.27651
- Preoperative axillary lymph node evaluation in breast cancer patients by breast magnetic resonance imaging (MRI): can breast MRI exclude advanced nodal disease?.Eur Radiol. 2016; 26: 386-3873https://doi.org/10.1007/s00330-016-4235-4
- Breast cancer-major changes in the American joint committee on cancer eighth edition cancer staging manual [.CA Cancer J Clin. 2017; 67: 290-303https://doi.org/10.3322/caac.21393
- Prognostic value of lymphovascular invasion in women with lymph node negative invasive breast carcinoma.Eur J Cancer. 2006; 42: 357-362https://doi.org/10.1016/j.ejca.2005.10.021
- Obvious peritumoral emboli: an elusive prognostic factor reappraised. Multivariate analysis of 1320 node-negative breast cancers.Eur J Cancer. 1998; 34: 5-65https://doi.org/10.1016/s0959-8049(97)00344-4
- Peritumoral apparent diffusion coefficients for prediction of lymphovascular invasion in clinically node-negative invasive breast cancer [published correction appears in Eur Radiol. 2016 Feb;26(2):340-1].Eur Radiol. 2016; 26: 331-339https://doi.org/10.1007/s00330-015-3847-4
- Peritumoral edema in breast cancer at preoperative MRI: an interpretative study with histopathological review toward understanding tumor microenvironment.Sci Rep. 2021; 11: 12992https://doi.org/10.1038/s41598-021-92283-z
- Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.Eur Radiol. 2022; 32: 793-805https://doi.org/10.1007/s00330-021-08221-0
- Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives.Eur Radiol. 2021; 31: 1049-1058https://doi.org/10.1007/s00330-020-07141-9
- Radiomics of liver metastases: a systematic review.Cancers (Basel). 2020; 12: 2881https://doi.org/10.3390/cancers12102881
- A Radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma.Liver Cancer. 2019; 8: 373-386https://doi.org/10.1159/000494099
Article info
Publication history
Published online: December 29, 2022
Accepted:
November 18,
2022
Received in revised form:
November 14,
2022
Received:
October 12,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
