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Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer

  • Authors:
    • Joongyo Lee
    • Sang Kyun Yoo
    • Kangpyo Kim
    • Byung Min Lee
    • Vivian Youngjean Park
    • Jin Sung Kim
    • Yong Bae Kim
  • View Affiliations / Copyright

    Affiliations: Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea, nce, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea, Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
    Copyright: © Lee et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 422
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    Published online on: August 11, 2023
       https://doi.org/10.3892/ol.2023.14008
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Abstract

Locoregional recurrence (LRR) is the predominant pattern of relapse after definitive breast cancer treatment. The present study aimed to develop machine learning (ML)‑based radiomics models to predict LRR in patients with breast cancer by using preoperative magnetic resonance imaging (MRI) data. Data from patients with localized breast cancer that underwent preoperative MRI between January 2013 and December 2017 were collected. Propensity score matching (PSM) was performed to adjust for clinical factors between patients with and without LRR. Radiomics features were obtained from T2‑weighted with and without fat‑suppressed MRI and contrast‑enhanced T1‑weighted with fat‑suppressed MRI. In the present study five ML models were designed, three base models (support vector machine, random forest, and logistic regression) and two ensemble models (voting model and stacking model) composed of the three base models, and the performance of each base model was compared with the stacking model. After PSM, 28 patients with LRR and 86 patients without LRR were included. Of these 114 patients, 80 patients were randomly selected to train the models, and the remaining 34 patients were used to evaluate the performance of the trained models. In total, 5,064 features were obtained from each patient, and 47‑51 features were selected by applying variance threshold and least absolute shrinkage and selection operator. The stacking model demonstrated superior performance in area under the receiver operating characteristic curve (AUC), with an AUC of 0.78 compared to a range of 0.61 to 0.70 for the other models. An external validation study to investigate the efficacy of the stacking model of the present study was initiated and is still ongoing (Korean Radiation Oncology Group 2206).
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View References

1 

Van Laar C, Van Der Sangen M, Poortmans P, Nieuwenhuijzen GA, Roukema JA, Roumen RM, Tjan-Heijnen VC and Voogd AC: Local recurrence following breast-conserving treatment in women aged 40 years or younger: Trends in risk and the impact on prognosis in a population-based cohort of 1143 patients. Eur J Cancer. 49:3093–3101. 2013. View Article : Google Scholar : PubMed/NCBI

2 

van Dongen JA, Voogd AC, Fentiman IS, Legrand C, Sylvester RJ, Tong D, van der Schueren E, Helle PA, van Zijl K and Bartelink H: Long-term results of a randomized trial comparing breast-conserving therapy with mastectomy: European organization for research and treatment of cancer 10801 trial. J Natl Cancer Inst. 92:1143–1150. 2000. View Article : Google Scholar : PubMed/NCBI

3 

Wapnir IL, Anderson SJ, Mamounas EP, Geyer CE Jr, Jeong JH, Tan-Chiu E, Fisher B and Wolmark N: Prognosis after ipsilateral breast tumor recurrence and locoregional recurrences in five national surgical adjuvant breast and bowel project node-positive adjuvant breast cancer trials. J Clin Oncol. 24:2028–2037. 2006. View Article : Google Scholar : PubMed/NCBI

4 

Katz A, Strom EA, Buchholz TA, Thames HD, Smith CD, Jhingran A, Hortobagyi G, Buzdar AU, Theriault R, Singletary SE and McNeese MD: Locoregional recurrence patterns after mastectomy and doxorubicin-based chemotherapy: Implications for postoperative irradiation. J Clin Oncol. 18:2817–2827. 2000. View Article : Google Scholar : PubMed/NCBI

5 

Koçak B, Durmaz EŞ, Ateş E and Kılıçkesmez Ö: Radiomics with artificial intelligence: A practical guide for beginners. Diagn Interv Radiol. 25:485–495. 2019. View Article : Google Scholar : PubMed/NCBI

6 

van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Oberije C, Monshouwer R, Bussink J, Brink C, Hansen O and Lambin P: Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol. 123:363–369. 2017. View Article : Google Scholar : PubMed/NCBI

7 

Bulens P, Couwenberg A, Intven M, Debucquoy A, Vandecaveye V, Van Cutsem E, D'Hoore A, Wolthuis A, Mukherjee P, Gevaert O and Haustermans K: Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiother Oncol. 142:246–252. 2020. View Article : Google Scholar : PubMed/NCBI

8 

Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, Wu N, Huddleston C, Wolfson S, Millet A, et al: Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun. 12:56452021. View Article : Google Scholar : PubMed/NCBI

9 

Sannachi L, Gangeh M, Tadayyon H, Gandhi S, Wright FC, Slodkowska E, Curpen B, Sadeghi-Naini A, Tran W and Czarnota GJ: Breast cancer treatment response monitoring using quantitative ultrasound and texture analysis: Comparative analysis of analytical models. Transl Oncol. 12:1271–1281. 2019. View Article : Google Scholar : PubMed/NCBI

10 

Jo JH, Chung HW, So Y, Yoo YB, Park KS, Nam SE, Lee EJ and Noh WC: FDG PET/CT to predict recurrence of early breast invasive ductal carcinoma. Diagnostics (Basel). 12:6942022. View Article : Google Scholar : PubMed/NCBI

11 

Whitney HM, Drukker K, Edwards A, Papaioannou J and Giger ML: Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers. J Med Imaging (Bellingham). 6:0314082019.PubMed/NCBI

12 

Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R and Mazurowski MA: A machine learning approach to radiogenomics of breast cancer: A study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 119:508–516. 2018. View Article : Google Scholar : PubMed/NCBI

13 

Han L, Zhu Y, Liu Z, Yu T, He C, Jiang W, Kan Y, Dong D, Tian J and Luo Y: Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer. Eur Radiol. 29:3820–3829. 2019. View Article : Google Scholar : PubMed/NCBI

14 

Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H, et al: 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. 2019. View Article : Google Scholar : PubMed/NCBI

15 

Chan HM, van der Velden BHM, Loo CE and Gilhuijs KGA: Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI: A feasibility study. Phys Med Biol. 62:6467–6485. 2017. View Article : Google Scholar : PubMed/NCBI

16 

Harada TL, Uematsu T, Nakashima K, Kawabata T, Nishimura S, Takahashi K, Tadokoro Y, Hayashi T, Tsuchiya K, Watanabe J and Sugino T: Evaluation of breast edema findings at T2-weighted breast MRI is useful for diagnosing occult inflammatory breast cancer and can predict prognosis after neoadjuvant chemotherapy. Radiology. 299:53–62. 2021. View Article : Google Scholar : PubMed/NCBI

17 

Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK and Mazurowski MA: Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: A study using an independent validation set. Breast Cancer Res Treat. 173:455–463. 2019. View Article : Google Scholar : PubMed/NCBI

18 

Symmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, Assad L, Poniecka A, Hennessy B, Green M, et al: Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 25:4414–4422. 2007. View Article : Google Scholar : PubMed/NCBI

19 

Van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S and Aerts HJWL: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77:e104–e107. 2017. View Article : Google Scholar : PubMed/NCBI

20 

Haralick RM, Shanmugam K and Dinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern. 3:610–621. 1973. View Article : Google Scholar

21 

Galloway MM: Texture analysis using gray level run lengths. Comput Graph Image process. 4:172–179. 1975. View Article : Google Scholar

22 

Thibault G, Fertil B, Navarro C, Pereira S, Cau P, Levy N, Sequeira J and Mari JL: Shape and texture indexes application to cell nuclei classification. Int J Pattern Recognit Artif Intell. 27:13570022013. View Article : Google Scholar

23 

Sun C and Wee WG: Neighboring gray level dependence matrix for texture classification. Comput Vis Graph Image Process. 23:341–352. 1983. View Article : Google Scholar

24 

Amadasun M and King R: Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern. 19:1264–1274. 1989. View Article : Google Scholar

25 

Tibshirani R: Regression shrinkage and selection via the lasso. J R Stat Soc B (Methodol). 58:267–288. 1996.

26 

Pedregosa F, Varoquaux G, Gramfort A, Michel V and Thirion B: Scikit-learn: Machine learning in Python. J Mach Learn Res. 12:2825–2830. 2011.

27 

Wolpert DH: Stacked generalization. Neural Netw. 5:241–259. 1992. View Article : Google Scholar

28 

Steinwart I and Christmann A: Support vector machines. Springer Science & Business Media; 2008

29 

Cutler A, Cutler DR and Stevens JR: Random forests. Ensemble machine learning: Methods and applications. 157–175. 2012.

30 

Kim JH, Ko ES, Lim Y, Lee KS, Han BK, Ko EY, Hahn SY and Nam SJ: Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology. 282:665–675. 2017. View Article : Google Scholar : PubMed/NCBI

31 

Park H, Lim Y, Ko ES, Cho HH, Lee JE, Han BK, Ko EY, Choi JS and Park KW: Radiomics signature on magnetic resonance imaging: Association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res. 24:4705–4714. 2018. View Article : Google Scholar : PubMed/NCBI

32 

Johansen R, Jensen LR, Rydland J, Goa PE, Kvistad KA, Bathen TF, Axelson DE, Lundgren S and Gribbestad IS: Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE-MRI. J Magn Reson Imaging. 29:1300–1307. 2009. View Article : Google Scholar : PubMed/NCBI

33 

Padhani AR, Hayes C, Assersohn L, Powles T, Makris A, Suckling J, Leach MO and Husband JE: Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: Initial clinical results. Radiology. 239:361–374. 2006. View Article : Google Scholar : PubMed/NCBI

34 

Liu X, Xiang K, Geng GY, Wang SC, Ni M, Zhang YF, Pan HF and Lv WF: Prognostic value of intratumor metabolic heterogeneity parameters on 18F-FDG PET/CT for patients with colorectal cancer. Contrast Media Mol Imaging. 2022:25862452022.PubMed/NCBI

35 

Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 366:883–892. 2012. View Article : Google Scholar : PubMed/NCBI

36 

Asselin MC, O'Connor JPB, Boellaard R, Thacker NA and Jackson A: Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer. 48:447–455. 2012. View Article : Google Scholar : PubMed/NCBI

37 

Dasgupta A, Bhardwaj D, DiCenzo D, Fatima K, Osapoetra LO, Quiaoit K, Saifuddin M, Brade S, Trudeau M, Gandhi S, et al: Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound. Oncotarget. 12:2437–2448. 2021. View Article : Google Scholar : PubMed/NCBI

38 

Xiong L, Chen H, Tang X, Chen B, Jiang X, Liu L, Feng Y, Liu L and Li L: Ultrasound-based radiomics analysis for predicting disease-free survival of invasive breast cancer. Front Oncol. 11:6219932021. View Article : Google Scholar : PubMed/NCBI

39 

Tamez-Peña JG, Rodriguez-Rojas JA, Gomez-Rueda H, Celaya-Padilla JM, Rivera-Prieto RA, Palacios-Corona R, Garza-Montemayor M, Cardona-Huerta S and Treviño V: Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer. PLoS One. 13:e01938712018. View Article : Google Scholar : PubMed/NCBI

40 

Moreno AC, Shaitelman SF and Buchholz TA: A clinical perspective on regional nodal irradiation for breast cancer. Breast. 34 (Suppl 1):S85–S90. 2017. View Article : Google Scholar : PubMed/NCBI

41 

Zeidan YH, Habib JG, Ameye L, Paesmans M, de Azambuja E, Gelber RD, Campbell I, Nordenskjöld B, Gutiérez J, Anderson M, et al: Postmastectomy radiation therapy in women with T1-T2 tumors and 1 to 3 positive lymph nodes: Analysis of the breast international group 02–98 trial. Int J Radiat Oncol Biol Phys. 101:316–324. 2018. View Article : Google Scholar : PubMed/NCBI

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Copy and paste a formatted citation
Spandidos Publications style
Lee J, Yoo S, Kim K, Lee B, Park V, Kim J and Kim Y: Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncol Lett 26: 422, 2023.
APA
Lee, J., Yoo, S., Kim, K., Lee, B., Park, V., Kim, J., & Kim, Y. (2023). Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncology Letters, 26, 422. https://doi.org/10.3892/ol.2023.14008
MLA
Lee, J., Yoo, S., Kim, K., Lee, B., Park, V., Kim, J., Kim, Y."Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer". Oncology Letters 26.4 (2023): 422.
Chicago
Lee, J., Yoo, S., Kim, K., Lee, B., Park, V., Kim, J., Kim, Y."Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer". Oncology Letters 26, no. 4 (2023): 422. https://doi.org/10.3892/ol.2023.14008
Copy and paste a formatted citation
x
Spandidos Publications style
Lee J, Yoo S, Kim K, Lee B, Park V, Kim J and Kim Y: Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncol Lett 26: 422, 2023.
APA
Lee, J., Yoo, S., Kim, K., Lee, B., Park, V., Kim, J., & Kim, Y. (2023). Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncology Letters, 26, 422. https://doi.org/10.3892/ol.2023.14008
MLA
Lee, J., Yoo, S., Kim, K., Lee, B., Park, V., Kim, J., Kim, Y."Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer". Oncology Letters 26.4 (2023): 422.
Chicago
Lee, J., Yoo, S., Kim, K., Lee, B., Park, V., Kim, J., Kim, Y."Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer". Oncology Letters 26, no. 4 (2023): 422. https://doi.org/10.3892/ol.2023.14008
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