Abstract
Objectives
To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).
Materials and methods
In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (n = 320) and test (n = 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
Results
The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84–0.91), 0.89 (95% CI, 0.84–0.94) and 0.95 (95% CI, 0.92–0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (p < 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84–0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.
Conclusions
The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI.
Clinical relevance statement
The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer.
Key Points
• The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone.
• The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets).
• A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130).
Similar content being viewed by others
Abbreviations
- ALN:
-
Axillary lymph node
- AUC:
-
Area under the ROC curve
- GLSZM:
-
Grey-level size zone matrix
- IBC:
-
Invasive breast cancer
- LVI:
-
Lymphovascular invasion
- NPV:
-
Negative predictive value
- PPV:
-
Positive predictive value
- ROC:
-
Receiving operating characteristics
- RS:
-
Radiomics signature
- VIF:
-
Variance inflation factor
References
Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer Statistics, 2021. CA Cancer J Clin 71:7–33
Aleskandarany MA, Sonbul SN, Mukherjee A, Rakha EA (2015) Molecular mechanisms underlying lymphovascular invasion in invasive breast cancer. Pathobiology 82:113–123
Ghosh P, Tie J, Muranyi A et al (2016) Girdin (GIV) Expression as a prognostic marker of recurrence in mismatch repair-proficient stage II colon cancer. Clin Cancer Res 22:3488–3498
Mathieu R, Lucca I, Rouprêt M, Briganti A, Shariat SF (2016) The prognostic role of lymphovascular invasion in urothelial carcinoma of the bladder. Nat Rev Urol 13:471–479
Kus KJB, Murad F, Smile TD et al (2022) Higher metastasis and death rates in cutaneous squamous cell carcinomas with lymphovascular invasion. J Am Acad Dermatol 86:766–773
Cheng S-P, Lee J-J, Chien M-N, Kuo C-Y, Jhuang J-Y, Liu C-L (2020) Lymphovascular invasion of papillary thyroid carcinoma revisited in the era of active surveillance. Eur J Surg Oncol 46:1814–1819
Wang C, Wu Y, Shao J, Liu D, Li W (2020) Clinicopathological variables influencing overall survival, recurrence and post-recurrence survival in resected stage I non-small-cell lung cancer. BMC Cancer 20:150
Sha N, Xie L, Chen T et al (2015) Impact of lymphovascular invasion on recurrence and progression rates in patients with pT1 urothelial carcinoma of bladder after transurethral resection. Onco Targets Ther 8:3401–3406
Liu YL, Saraf A, Lee SM et al (2016) Lymphovascular invasion is an independent predictor of survival in breast cancer after neoadjuvant chemotherapy. Breast Cancer Res Treat 157:555–564
Hamy A-S, Lam G-T, Laas E et al (2018) Lymphovascular invasion after neoadjuvant chemotherapy is strongly associated with poor prognosis in breast carcinoma. Breast Cancer Res Treat 169:295–304
Barron AU, Hoskin TL, Boughey JC (2018) Predicting non-sentinel lymph node metastases in patients with a positive sentinel lymph node after neoadjuvant chemotherapy. Ann Surg Oncol 25:2867–2874
Thiele W, Sleeman JP (2006) Tumor-induced lymphangiogenesis: a target for cancer therapy? J Biotechnol 124:224–241
Hoda SA, Hoda RS, Merlin S, Shamonki J, Rivera M (2006) Issues relating to lymphovascular invasion in breast carcinoma. Adv Anat Pathol 13:308–315
Nasute Fauerbach PV, Tyryshkin K, Rodrigo SP et al (2021) Lack of definitive presurgical pathological diagnosis is associated with inadequate surgical margins in breast-conserving surgery. Eur J Surg Oncol 47:2483–2491
Aljohani AI, Toss MS, Kurozumi S et al (2020) The prognostic significance of wild-type isocitrate dehydrogenase 2 (IDH2) in breast cancer. Breast Cancer Res Treat 179:79–90
Kulkarni A, Carrion-Martinez I, Jiang NN et al (2020) Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features. Eur Radiol 30:2853–2860
Liu Z, Feng B, Li C et al (2019) Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics. J Magn Reson Imaging 50:847–857
Li J, Jiang Y, Chen C et al (2020) Integrin β4 is an effective and efficient marker in synchronously highlighting lymphatic and blood vascular invasion, and perineural aggression in malignancy. Am J Surg Pathol 44:681–690
Huang Y, Liu Y, Wang Y et al (2021) Quantitative analysis of shear wave elastic heterogeneity for prediction of lymphovascular invasion in breast cancer. Br J Radiol 94:20210682
Zhou P, Jin C, Lu J et al (2021) The value of nomograms in pre-operative prediction of lymphovascular invasion in primary breast cancer undergoing modified radical surgery: based on multiparametric ultrasound and clinicopathologic indicators. Ultrasound Med Bio 47:517–526
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61:488–495
Yang L, Gu D, Wei J et al (2019) A Radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 8:373–386
Liu Z, Li Z, Qu J et al (2019) Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res 25:3538–3547
Zhang J, Wang G, Ren J et al (2022) Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma. Eur Radiol 32:4079–4089
Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics 18(1):9
Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3:185–205
Cheng J, Sun J, Yao K, Xu M, Cao Y (2022) A variable selection method based on mutual information and variance inflation factor. Spectrochim Acta A Mol Biomol Spectrosc 268:120652
Wolbers M, Koller MT, Witteman JCM, Steyerberg EW (2009) Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology 20:555–561
Cheung SM, Husain E, Mallikourti V, Masannat Y, Heys S, He J (2021) Intra-tumoural lipid composition and lymphovascular invasion in breast cancer via non-invasive magnetic resonance spectroscopy. Eur Radiol 31:3703–3711
Lauria R, Perrone F, Carlomagno C et al (1995) The prognostic value of lymphatic and blood vessel invasion in operable breast cancer. Cancer 76:1772–1778
Çelebi F, Pilancı KN, Ordu Ç et al (2015) The role of ultrasonographic findings to predict molecular subtype, histologic grade, and hormone receptor status of breast cancer. Diagn Interv Radiol 21:448–453
Wojcinski S, Stefanidou N, Hillemanns P, Degenhardt F (2013) The biology of malignant breast tumors has an impact on the presentation in ultrasound: an analysis of 315 cases. BMC Womens Health 13:47
Jimeno A, Rubio-Viqueira B, Amador ML et al (2005) Epidermal growth factor receptor dynamics influences response to epidermal growth factor receptor targeted agents. Cancer Res 65:3003–3010
Lee SK, Cho EY, Kim WW et al (2010) The prediction of lymph node metastasis in ductal carcinoma in situ with microinvasion by assessing lymphangiogenesis. J Surg Oncol 102:225–229
Morkavuk ŞB, Güner M, Çulcu S, Eroğlu A, Bayar S, Ünal AE (2021) Relationship between lymphovascular invasion and molecular subtypes in invasive breast cancer. Int J Clin Pract 75:e13897
Wong JS, O’Neill A, Recht A et al (2000) The relationship between lymphatic vessell invasion, tumor size, and pathologic nodal status: can we predict who can avoid a third field in the absence of axillary dissection? Int J Radiat Oncol Biol Phys 48:133–137
Du Y, Zha H-L, Wang H et al (2022) Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma. Br J Radiol 95:20210598
Zheng X, Yao Z, Huang Y et al (2020) Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 11:1236
Huang Y-Q, Liang C-H, He L et al (2016) Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol 34:2157–2164
Ugras S, Stempel M, Patil S, Morrow M (2014) Estrogen receptor, progesterone receptor, and HER2 status predict lymphovascular invasion and lymph node involvement. Ann Surg Oncol 21:3780–3786
Kurozumi S, Joseph C, Sonbul S et al (2019) A key genomic subtype associated with lymphovascular invasion in invasive breast cancer. Br J Cancer 120:1129–1136
Funding
The authors state that this work has not received any funding.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Guarantor
The scientific guarantor of this publication is Prof. Min Zong and Prof. Cuiying Li.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Informed consent
Written informed consent was waived by the Institutional Review Board.
Ethical approval
This retrospective study was approved by the institutional review board, the approval number was 2022-SR-421.
Study subjects or cohorts overlap
None.
Methodology
• retrospective
• Observational
• multicentre study
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Du, Y., Cai, M., Zha, H. et al. Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study. Eur Radiol 34, 136–148 (2024). https://doi.org/10.1007/s00330-023-09995-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-023-09995-1