nach oben

Erschienen in:

30.06.2020 | Breast

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study

verfasst von: Ning Mao, Ping Yin, Qin Li, Qinglin Wang, Meijie Liu, Heng Ma, Jianjun Dong, Kaili Che, Zhongyi Wang, Shaofeng Duan, Xuexi Zhang, Nan Hong, Haizhu Xie

Erschienen in: European Radiology | Ausgabe 12/2020

Einloggen, um Zugang zu erhalten

Abstract

Objective

This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer.

Methods

This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets.

Results

The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689–0.858), 0.767 (95% CI 0.583–0.857), and 0.79 (95% CI 0.63–0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as − 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%.

Conclusions

The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer.

Key Points

• The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer.

• The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics.

• The nomogram has good application prospects in assisting clinical decision makers.

Kootstra J, Hoekstra-Weebers JE, Rietman H et al (2008) Quality of life after sentinel lymph node biopsy or axillary lymph node dissection in stage I/II breast cancer patients: a prospective longitudinal study. Ann Surg Oncol 15:29–29

Zhao J, Zhang J, Zhu QL et al (2018) The value of contrast-enhanced ultrasound for sentinel lymph node identification and characterisation in pre-operative breast cancer patients: a prospective study. Eur Radiol 28(4):1654–1661

Sodano C, Clauser P, Dietzel M et al (2020) Clinical relevance of total choline (tCho) quantification in suspicious lesions on multiparametric breast MRI. Eur Radiol 30(6):3371–3382

Fusco R, Sansone M, Granata V et al (2018) Use of quantitative morphological and functional features for assessment of axillary lymph node in breast dynamic contrast-enhanced magnetic resonance imaging. Biomed Res Int 2018:2610801

Dietzel M, Baltzer PAT, Vag T et al (2010) Application of breast MRI for prediction of lymph node metastases - systematic approach using 17 individual descriptors and a dedicated decision tree. Acta Radiol 51(8):885–894

Luczynska E, Heinze-Paluchowska S, Dyczek S, Blecharz P, Rys J, Reinfuss M (2014) Contrast-enhanced spectral mammography: comparison with conventional mammography and histopathology in 152 women. Korean J Radiol 15(6):689–696

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577CrossRefPubMed

Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446

Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

10.

Birkhahn M, Mitra AP, Cote RJ (2007) Molecular markers for bladder cancer: the road to a multimarker approach. Expert Rev Anticancer Ther 7(12):1717–1727

11.

Vellinga TT, Kranenburg O, Frenkel N et al (2017) Lymphangiogenic gene expression is associated with lymph node recurrence and poor prognosis after partial hepatectomy for colorectal liver metastasis. Ann Surg 266(5):765–771

12.

Gorelik E, Landsittel DP, Marrangoni AM et al(2005) Multiplexed immunobead-based cytokine profiling for early detection of ovarian cancer. Cancer Epidemiol Biomarkers Prev 14(4):981–987

13.

Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826

14.

Sparano JA, Gray RJ, Makowe DF et al (2015) Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med 373(21):2005–2014

15.

Dihge L, Bendahl PO, Ryden L (2017) Nomograms for preoperative prediction of axillary nodal status in breast cancer. Br J Surg 104(11):1494–1505CrossRefPubMedPubMedCentral

16.

Huang YQ, Liang CH, 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(18):2157–2164

17.

Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23(22):6904–6911

18.

Valente SA, Levine GM, Silverstein MJ et al (2012) Accuracy of predicting axillary lymph node positivity by physical examination, mammography, ultrasonography, and magnetic resonance imaging. Ann Surg Oncol 19(6):1825–1830

19.

Mortellaro VE, Marshall J, Singer L et al (2009) Magnetic resonance imaging for axillary staging in patients with breast cancer. J Magn Reson Imaging 30(2):309–312

20.

Yoshimura G, Sakurai T, Oura S et al (1999) Evaluation of axillary lymph node status in breast cancer with MRI. Breast Cancer 6(3):249–258

21.

Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22(1):81–91CrossRefPubMed

22.

Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50(1):92–98CrossRefPubMed

23.

Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Muller H (2014) Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal 18(1):176–196CrossRefPubMed

24.

Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26(30):5512–5528CrossRefPubMed

25.

Yu A, Wang S, Cheng X et al (2017) Functional connectivity of motor cortical network in patients with brachial plexus avulsion injury after contralateral cervical nerve transfer: a resting-state fMRI study. Neuroradiology 59(3):247–253

26.

Luini A, Gatti G, Ballardini B et al (2005) Development of axillary surgery in breast cancer. Ann Oncol 16(2):259–262

27.

Cianfrocca M, Goldstein LJ (2004) Prognostic and predictive factors in early-stage breast cancer. Oncologist 9(6):606–616CrossRefPubMed

28.

Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16(4):e173–e180CrossRefPubMedPubMedCentral

29.

Dong Y, Feng Q, Yang W et al (2018) Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 28(2):582–591

30.

Cui X, Wang N, Zhao Y et al (2019) Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI. Sci Rep 9(1):2240

31.

Yang J, Wang T, Yang L et al (2019) Preoperative prediction of axillary lymph node metastasis in breast cancer using mammography-based radiomics method. Sci Rep 9(1):4429

32.

Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH (2017) Quality of radiomic features in glioblastoma multiforme: impact of semi-automated tumor segmentation software. Korean J Radiol 18(3):498–509CrossRefPubMedPubMedCentral

33.

Jung SC, Choi SH, Yeom JA et al (2013) Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods. PLoS One 8(8):e69323

34.

de Hoop B, Gietema H, van Ginneken B, Zanen P, Groenewegen G, Prokop M (2009) A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. Eur Radiol 19(4):800–808

35.

Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol 12(8):862–866CrossRefPubMed

36.

Dietzel M, Baltzer PAT, Dietzel A et al (2010) Application of artificial neural networks for the prediction of lymph node metastases to the ipsilateral axilla - initial experience in 194 patients using magnetic resonance mammography. Acta Radiol 51(8):851–858

37.

Zhou LQ, Wu XL, Huang SY et al (2020) Lymph node metastasis prediction from primary breast cancer US images using deep learning. Radiology 294(1):19–28

Titel: Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study
verfasst von: Ning Mao
Ping Yin
Qin Li
Qinglin Wang
Meijie Liu
Heng Ma
Jianjun Dong
Kaili Che
Zhongyi Wang
Shaofeng Duan
Xuexi Zhang
Nan Hong
Haizhu Xie
Publikationsdatum: 30.06.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 12/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07016-z

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study