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Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters

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Abstract

Radiomics has been a promising imaging biomarker for many malignant diseases. We developed a novel radiomics strategy that incorporating radiomics features extracted from dual-view mammograms and clinical parameters for identifying benign and malignant breast lesions, and validated whether the radiomics assessment could improve the accurate diagnosis of breast cancer. A total of 380 patients (mean age, 52 ± 7 years) with 621 breast lesions utilizing mammograms on craniocaudal (CC) and mediolateral oblique (MLO) views were randomly allocated into the training (n = 486) and testing (n = 135) sets in this retrospective study. A total of 1184 and 2368 radiomics features were extracted from single-position region of interest (ROI) and position-paired ROI, separately. Clinical parameters were then combined for better prediction. Recursive feature elimination and least absolute shrinkage and selection operator methods were applied to select optimal predictive features. Random forest was used to conduct the predictive model. Intraclass correlation coefficient test was used to assess repeatability and reproducibility of features. After preprocessing, 467 radiomics features and clinical parameters remained in the single-view and dual-view models. The performance and significance of models were quantified by the area under the curve (AUC), sensitivity, specificity, and accuracy. The correlation analysis between variables was evaluated using the correlation ratio and Pearson correlation coefficient. The model using a combination of dual-view radiomics and clinical parameters achieved a favorable performance (AUC: 0.804, 95% CI: 0.668–0.916), outperformed single-view model and model without clinical parameters. Incorporating with radiomics features of dual-view (CC&MLO) mammogram, age, breast density, and type of suspicious lesions can provide a noninvasive approach to evaluate the malignancy of breast lesions and facilitate clinical decision-making.

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Availability of data and codes

The data and codes are available from the corresponding author upon reasonable request.

References

  1. Lin F, Wang Z, Zhang K, Yang P, Ma H, Shi Y, Liu M, Wang Q, Cui J, Mao N, Xie H. Contrast-enhanced spectral mammography-based radiomics nomogram for identifying benign and malignant breast lesions of sub-1 cm. Front Oncol. 2020;10:e573630.

    Article  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.

    Article  PubMed  Google Scholar 

  3. Kerlikowske K, Su YR, Sprague BL, Tosteson ANA, Buist DSM, Onega T, Henderson LM, Alsheik N, Bissell MCS, O’Meara ES, Lee CI, Miglioretti DL. Association of screening with digital breast tomosynthesis vs digital mammography with risk of interval invasive and advanced breast cancer. JAMA. 2022;327(22):2220–30.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Falcon S, Williams A, Weinfurtner J, Drukteinis JS. Imaging management of breast density, a controversial risk factor for breast cancer. Cancer Control J Moffitt Cancer Center. 2017;24(2):125–36.

    Article  Google Scholar 

  5. Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Thakur SB, Jochelson MS, Thakur N, Baltzer PAT, Helbich TH, Pinker K. Breast lesion classification with multiparametric breast MRI using radiomics and machine learning: a comparison with radiologists’ performance. Cancers. 2022;14(7):1743–55.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lei C, Wei W, Liu Z, Xiong Q, Yang C, Yang M, Zhang L, Zhu T, Zhuang X, Liu C, Liu Z, Tian J, Wang K. Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications. Eur J Radiol. 2019;121:e108711.

    Article  Google Scholar 

  7. Wanders AJT, Mees W, Bun PAM, Janssen N, Rodríguez-Ruiz A, Dalmış MU, Karssemeijer N, van Gils CH, Sechopoulos I, Mann RM, van Rooden CJ. Interval cancer detection using a neural network and breast density in women with negative screening mammograms. Radiology. 2022;303(2):269–75.

    Article  PubMed  Google Scholar 

  8. Li H, Mendel KR, Lan L, Sheth D, Giger ML. Digital mammography in breast cancer: additive value of radiomics of breast parenchyma. Radiology. 2020;291(1):15–20.

    Article  Google Scholar 

  9. Cozzi A, Schiaffino S, Fanizza M, Magni V, Menicagli L, Monaco CG, Benedek A, Spinelli D, Di Leo G, Di Giulio G, Sardanelli F. Contrast-enhanced mammography for the assessment of screening recalls: a two-centre study. Eur Radiol. 2022. https://doi.org/10.1007/s00330-022-08868-3.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Erdim C, Yardimci AH, Bektas CT, Kocak B, Koca SB, Demir H, Kilickesmez O. Prediction of benign and malignant solid renal masses: machine learning-based CT texture analysis. Acad Radiol. 2020;27(10):1422–9.

    Article  PubMed  Google Scholar 

  11. Priya S, Aggarwal T, Ward C, Bathla G, Jacob M, Gerke A, Hoffman EA, Nagpal P. Radiomics detection of pulmonary hypertension via texture-based assessments of cardiac MRI: a machine-learning model comparison-cardiac MRI radiomics in pulmonary hypertension. J Clin Med. 2021;10(9):1921–33.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Cheung YC, Chen K, Yu CC, Ueng SH, Li CW, Chen SC. Contrast-enhanced mammographic features of in situ and invasive ductal carcinoma manifesting microcalcifications only: help to predict underestimation? Cancers. 2021;13(17):4371–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292(1):60–6.

    Article  PubMed  Google Scholar 

  14. Yala A, Mikhael PG, Strand F, Lin G, Smith K, Wan YL, Lamb L, Hughes K, Lehman C, Barzilay R. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13(578):4373.

    Article  Google Scholar 

  15. Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Kerlikowske K, Shepherd J. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study. Cancer Imaging. 2019;19(1):41–9.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Dembrower K, Liu Y, Azizpour H, Eklund M, Smith K, Lindholm P, Strand F. Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology. 2020;294(2):265–72.

    Article  PubMed  Google Scholar 

  17. Hongyu W, Jun F, Zizhao Z, Hai S, Lei C, Hua H, Li L. Breast mass classification via deeply integrating the contextual information from multi-view data. Pattern Recogn. 2018;80:42–52.

    Article  Google Scholar 

  18. Gibbs P, Onishi N, Sadinski M, Gallagher KM, Hughes M, Martinez DF, Morris EA, Sutton EJ. Characterization of sub-1 cm breast lesions using radiomics analysis. J Magn Reson Imaging. 2019;50(5):1468–77.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Song D, Wang Y, Wang W, Wang Y, Cai J, Zhu K, Lv M, Gao Q, Zhou J, Fan J, Rao S, Wang M, Wang X. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. 2021;147(12):3757–67.

    Article  CAS  PubMed  Google Scholar 

  20. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–57.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9(1):77–102.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Acciavatti RJ, Cohen EA, Maghsoudi OH, Gastounioti A, Pantalone L, Hsieh MK, Conant EF, Scott CG, Winham SJ, Kerlikowske K, Vachon C, Maidment ADA, Kontos D. Incorporating robustness to imaging physics into radiomic feature selection for breast cancer risk estimation. Cancers. 2021;13(21):5497–512.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Li Z, Yu L, Wang X, Yu H, Gao Y, Ren Y, Wang G, Zhou X. Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Clin Breast Cancer. 2018;18(4):621–7.

    Article  Google Scholar 

  24. Mao N, Yin P, Wang Q, Liu M, Dong J, Zhang X, Xie H, Hong N. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol. 2018;16(4):485–91.

    Article  PubMed  Google Scholar 

  25. Wang S, Sun Y, Li R, Mao N, Li Q, Jiang T, Chen Q, Duan S, Xie H, Gu Y. Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions. Eur Radiol. 2022;32(1):639–49.

    Article  PubMed  Google Scholar 

  26. Gupta S, Markey MK. Correspondence in texture features between two mammographic views. Med Phys. 2005;32(6):1598–606.

    Article  PubMed  Google Scholar 

  27. Wang G, Shi D, Guo Q, Zhang H, Wang S, Ren K. Radiomics based on digital mammography helps to identify mammographic masses suspicious for cancer. Front Oncol. 2022;12(1):e843436.

    Article  Google Scholar 

  28. Huo Z, Giger ML, Wolverton DE, Zhong W, Cumming S, Olopade OI. Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. Med Phys. 2000;27(1):4–12.

    Article  CAS  PubMed  Google Scholar 

  29. Wang L, Yang W, Xie X, Liu W, Wang H, Shen J, Ding Y, Zhang B, Song B. Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes. Gland Surg. 2020;9(6):2005–16.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Spick C, Bickel H, Polanec SH, Baltzer PA. Breast lesions classified as probably benign (BI-RADS 3) on magnetic resonance imaging: a systematic review and meta-analysis. Eur Radiol. 2017;28(5):1919–28.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Michaels AY, Birdwell RL, Chung CS, Frost EP, Giess CS. Assessment and management of challenging BI-RADS category 3 mammographic lesions. Radiographics. 2016;36(5):1261–72.

    Article  PubMed  Google Scholar 

  32. Wang S, Sun Y, Mao N, Duan S, Li Q, Li R, Jiang T, Wang Z, Xie H, Gu Y. Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study. Quant Imaging Med Surg. 2021;11(10):4418–30.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Du D, Feng H, Lv W, Ashrafinia S, Yuan Q, Wang Q, Yang W, Feng Q, Chen W, Rahmim A, Lu L. Machine learning methods for optimal radiomics-based differentiation between recurrence and inflammation: application to nasopharyngeal carcinoma post-therapy PET/CT images. Mol Imag Biol. 2019;22(3):730–8.

    Article  Google Scholar 

  34. Leithner D, Mayerhoefer ME, Martinez DF, Jochelson MS, Morris EA, Thakur SB, Pinker K. Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics. J Clin Med. 2020;9(6):1853–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Li C, Xu J, Liu Q, Zhou Y, Mou L, Pu Z, Xia Y, Zheng H, Wang S. Multi-view mammographic density classification by dilated and attention-guided residual learning. IEEE/ACM Trans Comput Biol Bioinf. 2020;18(3):1003–13.

    Article  Google Scholar 

  36. Chen S, Guan X, Shu Z, Li Y, Cao W, Dong F, Zhang M, Shao G, Shao F. A new application of multimodality radiomics improves diagnostic accuracy of nonpalpable breast lesions in patients with microcalcifications-only in mammography. Med Sci Monit. 2019;25:9786–93. https://doi.org/10.12659/MSM.918721.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Process Mag. 2019;36(4):132–60.

    Article  Google Scholar 

  38. Ma W, Zhao Y, Ji Y, Guo X, Jian X, Liu P, Wu S. Breast cancer molecular subtype prediction by mammographic radiomic features. Acad Radiol. 2019;26(2):196–201.

    Article  PubMed  Google Scholar 

  39. Niu S, Jiang W, Zhao N, Jiang T, Dong Y, Luo Y, Yu T, Jiang X. Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI. J Cancer Res Clin Oncol. 2022;148(1):97–106.

    Article  PubMed  Google Scholar 

  40. Sammut SJ, Crispin-Ortuzar M, Chin SF, Provenzano E, Bardwell HA, Ma W, Cope W, Dariush A, Dawson SJ, Abraham JE, Dunn J, Hiller L, Thomas J, Cameron DA, Bartlett JMS, Hayward L, Pharoah PD, Markowetz F, Rueda OM, Earl HM, Caldas C. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2022;601(7894):623–9.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors acknowledge the support in providing advice and guidance from Dr. Baiyun Liu and Mrs. Jiangfen Wu.

Funding

This research was supported in part through the Scientific Research Project of Jiangsu Maternal and Child Health Association, Grant Number FYX202020 and a grant from the Science Innovation Fund Project from the People's Hospital of SND, Grant Number SGY2021A05.

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Authors and Affiliations

  1. Department of Radiology, The People’s Hospital of Suzhou New District, Suzhou, 215129, Jiangsu, China

    Chenyi Zhou, Hui Xie, Fanglian Zhu & Yanling Wang

  2. Beijing Infervision Technology Co. Ltd., Beijing, 100025, Beijing, China

    Wanying Yan & Ruize Yu

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Contributions

C.Z. and Y.W. conceptulized the idea; C.Z. and Y.W. contributed to the methodology; C.Z. and Y.Y. were responsible for validation, formal analysis and visualization; C.Z., H.X and F.Z. were responsible for data curation; C.Z. wrote the draft; C.Z. and W.Y. reviewed paper and provided comments; Y.W. was responsible for supervision, project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yanling Wang.

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The authors declare no conflict of interest.

Ethical approval

The study was performed in line with the Declaration of Helsinki, and approved by the Institutional Review Board of the People’s Hospital of SND (protocol code was [2021] No.004 and data of approval was February 23, 2021). Patient consent was waived due to the nature of this retrospective study.

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The authors affirm that human research participants provided informed consent for publication of the images in Figs. 2 and 4.

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Zhou, C., Xie, H., Zhu, F. et al. Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters. Clin Exp Med 23, 2357–2368 (2023). https://doi.org/10.1007/s10238-022-00944-8

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