Advertisement

Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches

  • Jiao Li
  • Yan Song
  • Shuoyu Xu
  • Jinhua Wang
  • Huabin Huang
  • Weimei Ma
  • Xinhua Jiang
  • Yaopan Wu
  • Hongming Cai
  • Li LiEmail author
Original Article
  • 104 Downloads

Abstract

Purpose

We aimed to investigate the feasibility of predicting invasion carcinoma from ductal carcinoma in situ (DCIS) lesions diagnosed by preoperative core needle biopsy using radiomics signatures, clinical imaging characteristics, and breast imaging reporting and data system (BI-RADS) descriptors on mammography.

Methods

Retrospectively, we enrolled 362 DCIS patients diagnosed by core needle biopsy, 110 (30.4%) of which had invasive carcinoma confirmed by operation and pathology. We analyzed the images identified suspicious calcification from 250 subjects (161 pure DCIS and 89 DCIS with invasion). A total of 569 calcification radiomics signatures were extracted from microcalcification for each patient. We included a group of routine clinical imaging characteristics and BI-RADS descriptors for comparison purpose. Five feature selection and seven classification methods were evaluated in terms of their prediction performance. We compared the area under the receiver operating characteristic curve (AUC) averaged from tenfold cross-validation of different feature sets to identify the best combination of feature selection and classification methods.

Results

Optimal feature selection and classification methods were identified after evaluating various combinations of feature sets. The best performance was achieved using both radiomics and clinical imaging characteristics (AUC = 0.72) performing better than BI-RADS descriptors or radiomics, but was no significant difference with clinical imaging characteristics (AUC = 0.66). The most significant features found were morphology signatures, first-order statistics, asymmetry/mass prevalence, and nuclear grade.

Conclusions

We found that the prediction model established using microcalcifications radiomics signatures and clinical imaging characteristics has the potential to identify an understaging of invasive breast cancer.

Keywords

Mammography Microcalcification Ductal carcinoma in situ Radiomics Machine learning 

Notes

Acknowledgements

This study was funded by the Science and Technology Planning Project of Guangdong Province, China (Nos. 2016B090918066, 201807010057), the Science and Technology Program of Guangzhou, China (No. 201704020060), the Health and Medical Collaborative Innovation Project of Guangzhou City (No. 201604020003), the National Natural Science Foundation of China (No. 61372141), Science and Technology Planning Project of Guangdong Province (No. 2016A010101013), and the Fundamental Research Fund for the Central Universities (No. 2017ZD051).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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

Informed consent

Informed consent was obtained from all individuals included in the study.

Supplementary material

11548_2018_1900_MOESM1_ESM.pdf (68 kb)
Supplementary material 1 (PDF 68 kb)

References

  1. 1.
    Fan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD, Chen WQ, Shao ZM, Goss PE (2014) Breast cancer in China. Lancet Oncol 15(7):e279–e289.  https://doi.org/10.1016/s1470-2045(13)70567-9 CrossRefPubMedGoogle Scholar
  2. 2.
    Cheng HD, Cai X, Chen X, Hu L, Lou X (2003) Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit 36(12):2967–2991.  https://doi.org/10.1016/S0031-3203(03)00192-4 CrossRefGoogle Scholar
  3. 3.
    Venkatesan A, Chu P, Kerlikowske K, Sickles EA, Smith-Bindman R (2009) Positive predictive value of specific mammographic findings according to reader and patient variables. Radiology 250(3):648–657.  https://doi.org/10.1148/radiol.2503080541 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Bijker N, Meijnen P, Peterse JL, Bogaerts J, Van Hoorebeeck I, Julien JP, Gennaro M, Rouanet P, Avril A, Fentiman IS, Bartelink H, Rutgers EJ (2006) Breast-conserving treatment with or without radiotherapy in ductal carcinoma-in situ: ten-year results of European Organisation for Research and Treatment of Cancer randomized phase III trial 10853—a study by the EORTC Breast Cancer Cooperative Group and EORTC Radiotherapy Group. J Clin Oncol 24(21):3381–3387.  https://doi.org/10.1200/jco.2006.06.1366 CrossRefPubMedGoogle Scholar
  5. 5.
    Ernster VL, Ballard-Barbash R, Barlow WE, Zheng Y, Weaver DL, Cutter G, Yankaskas BC, Rosenberg R, Carney PA, Kerlikowske K, Taplin SH, Urban N, Geller BM (2002) Detection of ductal carcinoma in situ in women undergoing screening mammography. J Natl Cancer Inst 94(20):1546–1554CrossRefGoogle Scholar
  6. 6.
    Macdonald HR, Silverstein MJ, Mabry H, Moorthy B, Ye W, Epstein MS, Holmes D, Silberman H, Lagios M (2005) Local control in ductal carcinoma in situ treated by excision alone: incremental benefit of larger margins. Am J Surg 190(4):521–525CrossRefGoogle Scholar
  7. 7.
    Boughey JC, Gonzalez RJ, Bonner E, Kuerer HM (2007) Current treatment and clinical trial developments for ductal carcinoma in situ of the breast. Oncologist 12(11):1276–1287CrossRefGoogle Scholar
  8. 8.
    Hermann G (2012) Ductal carcinoma in situ at core-needle biopsy: meta-analysis of underestimation and predictors of invasive breast cancer: Brennan ME, Turner RM, Ciatto S, et al (Univ. of Sydney, New South Wales, Australia) Radiology 260:119–128, 2011 §. Breast Dis Year Book Q 23(2):165–166CrossRefGoogle Scholar
  9. 9.
    Menell JH, Morris EA, Dershaw DD, Abramson AF, Brogi E, Liberman L (2005) Determination of the presence and extent of pure ductal carcinoma in situ by mammography and magnetic resonance imaging. Breast J 11(6):382–390.  https://doi.org/10.1111/j.1075-122X.2005.00121.x CrossRefPubMedGoogle Scholar
  10. 10.
    Yi M, Krishnamurthy S, Kuerer HM, Meric-Bernstam F, Bedrosian I, Ross MI, Ames FC, Lucci A, Hwang RF, Hunt KK (2008) Role of primary tumor characteristics in predicting positive sentinel lymph nodes in patients with ductal carcinoma in situ or microinvasive breast cancer. Am J Surg 196(1):81–87.  https://doi.org/10.1016/j.amjsurg.2007.08.057 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Brennan ME, Turner RM, Ciatto S, Marinovich ML, French JR, Macaskill P, Houssami N (2011) Ductal carcinoma in situ at core-needle biopsy: meta-analysis of underestimation and predictors of invasive breast cancer. Radiology 260(1):119–128.  https://doi.org/10.1148/radiol.11102368 CrossRefPubMedGoogle Scholar
  12. 12.
    Lee CH, Carter D, Philpotts LE, Couce ME, Horvath LJ, Lange RC, Tocino I (2000) Ductal carcinoma in situ diagnosed with stereotactic core needle biopsy: can invasion be predicted? Radiology 217(2):466CrossRefGoogle Scholar
  13. 13.
    Bagnall MJ, Evans AJ, Wilson AR, Pinder SE, Denley H, Geraghty JG, Ellis IO (2001) Predicting invasion in mammographically detected microcalcification. Clin Radiol 56(10):828–832CrossRefGoogle Scholar
  14. 14.
    Kurniawan ED, Rose A, Mou A, Buchanan M, Collins JP, Wong MH, Miller JA, Mann GB (2010) Risk factors for invasive breast cancer when core needle biopsy shows ductal carcinoma in situ. Arch Surg 145(11):1098–1104CrossRefGoogle Scholar
  15. 15.
    Park HS, Kim HY, Park S, Kim EK, Kim SI, Park BW (2013) A nomogram for predicting underestimation of invasiveness in ductal carcinoma in situ diagnosed by preoperative needle biopsy. Breast 22(5):869–873CrossRefGoogle Scholar
  16. 16.
    Sim YT, Litherland J, Lindsay E, Hendry P, Brauer K, Dobson H, Cordiner C, Gagliardi T, Smart L (2015) Upgrade of ductal carcinoma in situ on core biopsies to invasive disease at final surgery: a retrospective review across the Scottish Breast Screening Programme. Clin Radiol 70(5):502–506CrossRefGoogle Scholar
  17. 17.
    Lee CW, Wu HK, Lai HW, Wu WP, Chen ST, Chen DR, Chen CJ, Kuo SJ (2016) Preoperative clinicopathologic factors and breast magnetic resonance imaging features can predict ductal carcinoma in situ with invasive components. Eur J Radiol 85(4):780–789CrossRefGoogle Scholar
  18. 18.
    Dillon MF, McDermott EW, Quinn CM, O’Doherty A, O’Higgins N, Hill AD (2006) Predictors of invasive disease in breast cancer when core biopsy demonstrates DCIS only. J Surg Oncol 93(7):559–563.  https://doi.org/10.1002/jso.20445 CrossRefPubMedGoogle Scholar
  19. 19.
    Park HS, Park S, Cho J, Park JM, Kim SI, Park B-W (2013) Risk predictors of underestimation and the need for sentinel node biopsy in patients diagnosed with ductal carcinoma in situ by preoperative needle biopsy. J Surg Oncol 107(4):388–392.  https://doi.org/10.1002/jso.23273 CrossRefPubMedGoogle Scholar
  20. 20.
    Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY (2018) Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features. J Am Coll Radiol 15(3):527–534CrossRefGoogle Scholar
  21. 21.
    Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY (2017) Can occult invasive disease in ductal carcinoma in situ be predicted using computer-extracted mammographic features? Acad Radiol 24(9):1139–1147.  https://doi.org/10.1016/j.acra.2017.03.013 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006.  https://doi.org/10.1038/ncomms5006 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Huang Y-Q, Liang C-H, He L, Tian J, Liang C-S, Chen X, Ma Z-L, Liu Z-Y (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.  https://doi.org/10.1200/JCO.2015.65.9128 CrossRefPubMedGoogle Scholar
  24. 24.
    Nakayama R, Uchiyama Y, Watanabe R, Katsuragawa S, Namba K, Doi K (2004) Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med Phys 31(4):789–799CrossRefGoogle Scholar
  25. 25.
    Nakayama R, Watanabe R, Namba K, Takeda K, Yamamoto K, Katsuragawa S, Doi K (2007) An improved computer-aided diagnosis scheme using the nearest neighbor criterion for determining histological classification of clustered microcalcifications. Methods Inf Med 46(06):716–722CrossRefGoogle Scholar
  26. 26.
    Mohamed H, Mai SM, Sharawy A (2014) Computer aided detection system for micro calcifications in digital mammograms. Comput Methods Programs Biomed 116(3):226–235CrossRefGoogle Scholar
  27. 27.
    Rangayyan RM, Ayres FJ, Leo Desautels JE (2007) A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Frankl Inst 344(3):312–348.  https://doi.org/10.1016/j.jfranklin.2006.09.003 CrossRefGoogle Scholar
  28. 28.
    Diaz-Huerta CC, Felipe-Riveron EM, Montaño-Zetina LM (2014) Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms. Expert Syst Appl 41(16):7361–7369CrossRefGoogle Scholar
  29. 29.
    Strange H, Chen Z, Denton ERE, Zwiggelaar R (2014) Modelling mammographic microcalcification clusters using persistent mereotopology. Pattern Recognit Lett 47:157–163.  https://doi.org/10.1016/j.patrec.2014.04.008 CrossRefGoogle Scholar
  30. 30.
    Nakayama R, Watanabe R, Namba K, Takeda K, Yamamoto K, Katsuragawa S, Doiya K (2006) Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms. Acad Radiol 13(10):1219–1228.  https://doi.org/10.1016/j.acra.2006.07.005 CrossRefPubMedGoogle Scholar
  31. 31.
    Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087.  https://doi.org/10.1038/srep13087 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJWL (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 5:272.  https://doi.org/10.3389/fonc.2015.00272 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Tian J, Zhang S (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:21–27.  https://doi.org/10.1016/j.canlet.2017.06.004 CrossRefPubMedGoogle Scholar
  34. 34.
    Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S, Hall LO, Goldgof DB, Gatenby RA, Gillies RJ (2014) Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2:1418–1426.  https://doi.org/10.1109/ACCESS.2014.2373335 CrossRefGoogle Scholar
  35. 35.
    Heinlein P, Drexl J, Schneider W (2003) Integrated wavelets for enhancement of microcalcifications in digital mammography. IEEE Trans Med Imaging 22(3):402–413.  https://doi.org/10.1109/tmi.2003.809632 CrossRefPubMedGoogle Scholar
  36. 36.
    Alasadi AH, Al-Saedi AK (2017) A method for microcalcifications detection in breast mammograms. J Med Syst 41(4):68.  https://doi.org/10.1007/s10916-017-0714-7 CrossRefPubMedGoogle Scholar
  37. 37.
    Chen Z, Strange H, Oliver A, Denton ER, Boggis C, Zwiggelaar R (2015) Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans Biomed Eng 62(4):1203–1214.  https://doi.org/10.1109/tbme.2014.2385102 CrossRefPubMedGoogle Scholar
  38. 38.
    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12(1):77.  https://doi.org/10.1186/1471-2105-12-77 CrossRefGoogle Scholar
  39. 39.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  40. 40.
    Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system.  https://doi.org/10.1145/2939672.2939785
  41. 41.
    de Mascarel I, MacGrogan G, Mathoulin-Pélissier S, Soubeyran I, Picot V, Coindre J-M (2002) Breast ductal carcinoma in situ with microinvasion: a definition supported by a long-term study of 1248 serially sectioned ductal carcinomas. Cancer 94(8):2134–2142.  https://doi.org/10.1002/cncr.10451 CrossRefPubMedGoogle Scholar
  42. 42.
    Yu K-D, Wu L-M, Liu G-Y, Wu J, Di G-H, Shen Z-Z, Shao Z-M (2011) Different distribution of breast cancer subtypes in breast ductal carcinoma in situ (DCIS), DCIS with microinvasion, and DCIS with invasion component. Ann Surg Oncol 18(5):1342–1348.  https://doi.org/10.1245/s10434-010-1407-3 CrossRefPubMedGoogle Scholar
  43. 43.
    Wang W, Zhu W, Du F, Luo Y, Xu B (2017) The demographic features, clinicopathological characteristics and cancer-specific outcomes for patients with microinvasive breast cancer: a SEER database analysis. Sci Rep 7:42045CrossRefGoogle Scholar
  44. 44.
    Rauch GM, Hobbs BP, Kuerer HM, Scoggins ME, Benveniste AP, Park YM, Caudle AS, Fox PS, Smith BD, Adrada BE, Krishnamurthy S, Yang WT (2016) Microcalcifications in 1657 patients with pure ductal carcinoma in situ of the breast: correlation with clinical, histopathologic, biologic features, and local recurrence. Ann Surg Oncol 23(2):482–489.  https://doi.org/10.1245/s10434-015-4876-6 CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Department of Medical Imaging, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South ChinaGuangzhouPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  3. 3.Department of Medical Imaging CenterShenzhen Hospital of Southern Medical UniversityShenzhenPeople’s Republic of China

Personalised recommendations