Advertisement

Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps

  • Yu Zhang
  • Yifeng Zhu
  • Kai Zhang
  • Yajie Liu
  • Jingjing Cui
  • Juan Tao
  • Yingzi Wang
  • Shaowu WangEmail author
BREAST RADIOLOGY
  • 41 Downloads

Abstract

Purpose

The purpose of this study is to develop a radiomics model for predicting the Ki-67 proliferation index in patients with invasive ductal breast cancer through magnetic resonance imaging (MRI) preoperatively.

Materials and methods

A total of 128 patients who were clinicopathologically diagnosed with invasive ductal breast cancer were recruited. This cohort included 32 negative Ki67 expression (Ki67 proliferation index < 14%) and 96 cases with positive Ki67 expression (Ki67 proliferation index ≥ 14%). All patients had undergone diffusion-weighted imaging (DWI) MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from apparent diffusion coefficient (ADC) maps which were obtained by DWI-MRI from patients with invasive ductal breast cancer. 80% of the patients were divided into training set to build radiomics model, and the rest into test set to evaluate its performance. The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features, and then, the logistic regression (LR) model was established using fivefold cross-validation to predict the Ki-67 index. The performance was evaluated by receiver-operating characteristic (ROC) analysis, accuracy, sensitivity and specificity.

Results

Quantitative imaging features (n = 1029) were extracted from ADC maps, and 11 features were selected to construct the LR model. Good identification ability was exhibited by the ADC-based radiomics model, with areas under the ROC (AUC) values of 0.75 ± 0.08, accuracy of 0.71 in training set and 0.72, 0.70 in test set.

Conclusions

The ADC-based radiomics model is a feasible predictor for the Ki-67 index in patients with invasive ductal breast cancer. Therefore, we proposed that three-dimensional imaging features from ADC maps could be used as candidate biomarker for preoperative prediction the Ki-67 index noninvasively.

Keywords

Radiomics Invasive ductal breast cancer Ki-67 MRI 

Notes

Acknowledgements

This research was supported in part by grants from the National Natural Science Foundation of China (#81771804).

Compliance with ethical standards

Conflict of interest

The authors have declared that no competing financial interests exist.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. IRB approval was obtained. This article does not contain any studies with animals performed by any of the authors.

Informed consent

For this type of study, formal consent is not required.

References

  1. 1.
    Siegel RL, Miller KD, Jemal A (2017) Cancer Statistics, 2017. CA Cancer J Clin 67:7–30CrossRefGoogle Scholar
  2. 2.
    Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ et al (2011) 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. Ann Oncol 22:1736–1747CrossRefGoogle Scholar
  3. 3.
    MacCallum DE, Hall PA (2000) The location of pKi67 in the outer dense fibrillary compartment of the nucleolus points to a role in ribosome biogenesis during the cell division cycle. J Pathol 190:537–544CrossRefGoogle Scholar
  4. 4.
    Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J et al (2009) Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101:736–750CrossRefGoogle Scholar
  5. 5.
    Petrelli F, Viale G, Cabiddu M, Barni S (2015) Prognostic value of different cut-off levels of Ki-67 in breast cancer: a systematic review and meta-analysis of 64,196 patients. Breast Cancer Res Treat 153:477–491CrossRefGoogle Scholar
  6. 6.
    Park SH, Choi HY, Hahn SY (2015) Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla. Journal of magnetic resonance imaging. J Magn Reson Imaging JMRI 41:175–182CrossRefGoogle Scholar
  7. 7.
    Molinari C, Clauser P, Girometti R, Linda A, Cimino E, Puglisi F et al (2015) MR mammography using diffusion-weighted imaging in evaluating breast cancer: a correlation with proliferation index. Radiol Med (Torino) 120:911–918CrossRefGoogle Scholar
  8. 8.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefGoogle Scholar
  9. 9.
    Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248CrossRefGoogle Scholar
  10. 10.
    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefGoogle Scholar
  11. 11.
    Itakura H, Achrol AS, Mitchell LA, Loya JJ, Liu T, Westbroek EM et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 7:303ra138CrossRefGoogle Scholar
  12. 12.
    Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol Off J Am Soc Clin Oncol 34:2157–2164CrossRefGoogle Scholar
  13. 13.
    Fusco R, Sansone M, Filice S, Granata V, Catalano O, Amato DM et al (2015) Integration of DCE-MRI and DW-MRI Quantitative parameters for breast lesion classification. Biomed Res Int 2015:237863CrossRefGoogle Scholar
  14. 14.
    Liang C, Huang Y, He L, Chen X, Ma Z, Dong D et al (2016) The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 7:31401–31412PubMedPubMedCentralGoogle Scholar
  15. 15.
    Liu Y, Kim J, Balagurunathan Y, Li Q, Garcia AL, Stringfield O et al (2016) Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer 17(441–8):e6Google Scholar
  16. 16.
    Kickingereder P, Gotz M, Muschelli J, Wick A, Neuberger U, Shinohara RT et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRefGoogle Scholar
  17. 17.
    Ouyang FS, Guo BL, Zhang B, Dong YH, Zhang L, Mo XK et al (2017) Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III–IVb nasopharyngeal carcinoma. Oncotarget 8:74869–74879PubMedPubMedCentralGoogle Scholar
  18. 18.
    Liang C, Cheng Z, Huang Y, He L, Chen X, Ma Z et al (2018) An MRI-based radiomics classifier for preoperative prediction of Ki-67 status in breast cancer. Acad Radiol 25:1111–1117CrossRefGoogle Scholar
  19. 19.
    Freedman D, Pisani R, Purves R (2007) Statistics: fourth international student edition. W.W. Norton & Company. ISBN 9780393930436Google Scholar
  20. 20.
    Wu J, Tha KK, Xing L, Li R (2018) Radiomics and radiogenomics for precision radiotherapy. J Radiat Res 59:i25–i31CrossRefGoogle Scholar
  21. 21.
    de Azambuja E, Cardoso F, de Castro G, Colozza M, Mano MS, Durbecq V et al (2007) Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients. Br J Cancer 96:1504–1513CrossRefGoogle Scholar
  22. 22.
    Altay C, Balci P, Altay S, Karasu S, Saydam S, Canda T et al (2014) Diffusion-weighted MR imaging: role in the differential diagnosis of breast lesions. JBR-BTR: organe de la Societe royale belge de radiologie (SRBR) = orgaan van de Koninklijke Belgische Vereniging voor Radiologie (KBVR) 97:211–216Google Scholar
  23. 23.
    Jin G, An N, Jacobs MA, Li K (2010) The role of parallel diffusion-weighted imaging and apparent diffusion coefficient (ADC) map values for evaluating breast lesions: preliminary results. Acad Radiol 17:456–463CrossRefGoogle Scholar
  24. 24.
    Li L, Wang K, Sun X, Wang K, Sun Y, Zhang G et al (2015) Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer. Med Sci Monit Int Med J Exp Clin Res 21:376–382Google Scholar
  25. 25.
    Ye XH, Gao JY, Yang ZH, Liu Y (2014) Apparent diffusion coefficient reproducibility of the pancreas measured at different MR scanners using diffusion-weighted imaging. J Magn Reson imaging JMRI 40:1375–1381CrossRefGoogle Scholar
  26. 26.
    Thomassin-Naggara I, De Bazelaire C, Chopier J, Bazot M, Marsault C, Trop I (2013) Diffusion-weighted MR imaging of the breast: advantages and pitfalls. Eur J Radiol 82:435–443CrossRefGoogle Scholar
  27. 27.
    Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefGoogle Scholar
  28. 28.
    Gillies RJ (2015) Abstract CN01-01: The radiology reading room of the future. Mol Cancer Ther 14:CN01-CNGoogle Scholar
  29. 29.
    Joseph C, Papadaki A, Althobiti M, Alsaleem M, Aleskandarany MA, Rakha EA (2018) Breast cancer intra-tumour heterogeneity: Current status and clinical implications. Histopathology 73:717–731CrossRefGoogle Scholar
  30. 30.
    Fusco R, Di Marzo M, Sansone C, Sansone M, Petrillo A (2017) Breast DCE-MRI: lesion classification using dynamic and morphological features by means of a multiple classifier system. Eur Radiol Exp 1:10CrossRefGoogle Scholar
  31. 31.
    Ma W, Ji Y, Qi L, Guo X, Jian X, Liu P (2018) Breast cancer Ki67 expression prediction by DCE-MRI radiomics features. Clin Radiol 73:909PubMedGoogle Scholar
  32. 32.
    Liang M, Cai Z, Zhang H, Huang C, Meng Y, Zhao L, et al. (2019) Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol 26:1495–1504CrossRefGoogle Scholar
  33. 33.
    Sui H, Liu L, Li X, Zuo P, Cui J, Mo Z (2019) CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J of Thorac Dis 11:1809–1818CrossRefGoogle Scholar
  34. 34.
    Wang H, Hu D, Yao H, Chen M, Li S, Chen H, et al. (2019) Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol 29:6182–6190CrossRefGoogle Scholar
  35. 35.
    Fusco R, Sansone M, Filice S, Carone G, Amato DM, Sansone C et al (2016) Pattern recognition approaches for breast cancer DCE-MRI classification: a systematic review. J Med Biol Eng 36:449–459CrossRefGoogle Scholar

Copyright information

© Italian Society of Medical Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology, The Second HospitalDalian Medical UniversityDalianChina
  2. 2.Huiying Medical Technology Inc.BeijingChina
  3. 3.Department of Pathology, The Second HospitalDalian Medical UniversityDalianChina
  4. 4.Department of Gerontology, The Second HospitalDalian Medical UniversityDalianChina

Personalised recommendations