Use of artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer

Authors

  • Karen Olivia Bazzo Goulart Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil. https://orcid.org/0000-0002-4680-2813
  • Maximiliano Cassilha Kneubil Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil. Universidade de Caxias do Sul, General Hospital – Caxias do Sul (RS), Brazil. https://orcid.org/0000-0002-9017-5503
  • Janaina Brollo Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil. Universidade de Caxias do Sul, General Hospital – Caxias do Sul (RS), Brazil. https://orcid.org/0000-0001-8201-6003
  • Bruna Caroline Orlandin Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil. Universidade de Caxias do Sul, General Hospital – Caxias do Sul (RS), Brazil. https://orcid.org/0000-0001-6913-6596
  • Leandro Luis Corso Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil. https://orcid.org/0000-0001-9962-9578
  • Mariana Roesch- Ely Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil.
  • João Antonio Pêgas Henriques Universidade de Caxias do Sul,Biotechnology Institute – Caxias do Sul (RS), Brazil. https://orcid.org/0000-0002-5298-932X

DOI:

https://doi.org/10.29289/2594539420220041%20e20220041

Abstract

ABSTRACT: Introduction: Breast cancer is the object of thousands of studies worldwide. Nevertheless, few tools are available to corroborate prediction of response to neoadjuvant chemotherapy. Artificial intelligence is being researched for its potential utility in several fields of knowledge, including oncology. The development of a standardized Artificial intelligence-based predictive model for patients with breast cancer may help make clinical management more personalized and effective. We aimed to apply Artificial intelligence models to predict the response to neoadjuvant chemotherapy based solely on clinical and pathological data. Methods: Medical records of 130 patients treated with neoadjuvant chemotherapy were reviewed and divided into two groups: 90 samples to train the network and 40 samples to perform prospective testing and validate the results obtained by the Artificial intelligence method. Results: Using clinicopathologic data alone, the artificial neural network was able to correctly predict pathologic complete response in 83.3% of the cases. It also correctly predicted 95.6% of locoregional recurrence, as well as correctly determined whether patients were alive or dead at a given time point in 90% of the time. To date, no published research has used clinicopathologic data to predict the response to neoadjuvant chemotherapy in patients with breast cancer, thus highlighting the importance of the present study. Conclusions: Artificial neural network may become an interesting tool for predicting response to neoadjuvant chemotherapy, locoregional recurrence, systemic disease progression, and survival in patients with breast cancer.

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References

1. Sancho-Garnier H, Colonna M. Épidémiologie des cancers du sein [Breast cancer epidemiology]. Presse Med. 2019;48(10):1076-84. https://doi.org/10.1016/j.lpm.2019.09.022

2. Ahmed SH. Safety of neoadjuvant chemotherapy for the treatment of breast cancer. Expert Opin Drug Saf. 2019;18(9):817-27. https://doi.org/10.1080/14740338.2019.1644318

3. Li X, Wang M, Wang M, Yu X, Guo J, Sun T, et al. Predictive and Prognostic Roles of Pathological Indicators for Patients with Breast Cancer on Neoadjuvant Chemotherapy. J Breast Cancer. 2019;22(4):497-521. https://doi.org/10.4048/jbc.2019.22.e49

4. Sparano JA, Gray RJ, Ravdin PM, Makower DF, Pritchard KI, Albain KS, et al. Clinical and Genomic Risk to Guide the Use of Adjuvant Therapy for Breast Cancer. N Engl J Med. 2019;380(25):2395-405. https://doi.org/10.1056/NEJMoa1904819

5. Audeh W, Blumencranz L, Kling H, Trivedi H, Srkalovic G. Prospective Validation of a genomic assay in breast cancer: the 70-gene mammaprint assay and the MINDACT trial. Acta Med Acad. 2019;48(1):18-34. https://doi.org/10.5644/ama2006-124.239

6. Carter SM, Rogers W, Win KT, Frazer H, Richards B, Houssami N. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast. 2020;49:25-32. https://doi.org/10.1016/j.breast.2019.10.001

7. Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PC, et al. Artificial intelligence in digital breast pathology: techniques and applications. Breast. 2020;49:267-73. https://doi.org/10.1016/j.breast.2019.12.007

8. Lee CI, Houssami N, Elmore JG, Buist DSM. Pathways to breast cancer screening artificial intelligence algorithm validation. Breast. 2020;52:146-9. https://doi.org/10.1016/j.breast.2019.09.005

9. Díaz-Casas SE, Castilla-Tarra JA, Pena-Torres E, Orozco-Ospino M, Mendoza-Diaz S, Nuñez-Lemus M, et al. Pathological Response to Neoadjuvant Chemotherapy and the Molecular Classification of Locally Advanced Breast Cancer in a Latin American Cohort. Oncologist. 2019;24(12):e1360-70. https://doi.org/10.1634/theoncologist.2019-0300

10. Asaoka M, Narui K, Suganuma N, Chishima T, Yamada A, Sugae S, et al. Clinical and pathological predictors of recurrence in breast cancer patients achieving pathological complete response to neoadjuvant chemotherapy. Eur J Surg Oncol. 2019;45(12):2289-94. https://doi.org/10.1016/j.ejso.2019.08.001

11. Del Prete S, Caraglia M, Luce A, Montella L, Galizia G, Sperlongano P, et al. Clinical and pathological factors predictive of response to neoadjuvant chemotherapy in breast cancer: a single center experience. Oncol Lett. 2019;18(4):3873-9. https://doi.org/10.3892/ol.2019.10729

12. Mamounas EP, Anderson SJ, Dignam JJ, Bear HD, Julian TB, Geyer Junior CE, et al. Predictors of locoregional recurrence after neoadjuvant chemotherapy: results from combined analysis of National Surgical Adjuvant Breast and Bowel Project B-18 and B-27. J Clin Oncol. 2012;30(32):3960-6. https://doi.org/10.1200/JCO.2011.40.8369

13. Gillon P, Touati N, Breton-Callu C, Slaets L, Cameron D, Bonnefoi H. Factors predictive of locoregional recurrence following neoadjuvant chemotherapy in patients with large operable or locally advanced breast cancer: an analysis of the EORTC 10994/BIG 1-00 study. Eur J Cancer. 2017;79:226-34. https://doi.org/10.1016/j.ejca.2017.04.012

14. Li Y, Li Q, Mo H, Guan X, Lin S, Wang Z, et al. Incidence, risk factors and survival of patients with brain metastases at initial metastatic breast cancer diagnosis in China. Breast. 2021;55:30-6. https://doi.org/10.1016/j.breast.2020.11.021

15. Howlader N, Noone AM, Krapcho M, Garshell J, Miller D, Altekruse SF, et al. Tatalovich, SEER Cancer Statistics Review, 1975-2012, National Cancer Institute. Bethesda: National Cancer Institute; 2008 [cited on Nov 18, 2015]. Available from: https://seer.cancer.gov/archive/csr/1975_2012/

16. Kanghee Park H, Ali A, Kim D, An Y, Kim M, Shin H. Robust predictive model for evaluating breast cancer survivability. Eng Appl Artif Intell. 2013;26(9): 2194-205. https://doi.org/10.1016/j.engappai.2013.06.013

17. Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ. Artificial intelligence in breast imaging. Clin Radiol. 2019;74(5):357-66. https://doi.org/10.1016/j.crad.2019.02.006

18. Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, et al. Artificial intelligence in breast ultrasound. World J Radiol. 2019;11(2):19-26. https://doi.org/10.4329/wjr.v11.i2.19

19. Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, et al. Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology. 2019;290(2):305-14. https://doi.org/10.1148/radiol.2018181371

20. Yin XX, Jin Y, Gao M, Hadjiloucas S. Artificial intelligence in breast MRI radiogenomics: towards accurate prediction of neoadjuvant chemotherapy responses. Curr Med Imaging. 2021;17(4):452-8. https://doi.org/10.2174/1573405616666200825161921

21. Ayer T, Alagoz O, Chhatwal J, Shavlik JW, Kahn Junior CE, Burnside ES. Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration. Cancer. 2010;116(14):3310-21. https://doi.org/10.1002/cncr.25081

22. Dihge L, Ohlsson M, Edén P, Bendahl PO, Rydén L. Artificial neural network models to predict nodal status in clinically node-negative breast cancer. BMC Cancer. 2019;19(1):610. https://doi.org/10.1186/s12885-019-5827-6

23. Sepandi M, Taghdir M, Rezaianzadeh A, Rahimikazerooni S. Assessing breast cancer risk with an artificial neural network. Asian Pac J Cancer Prev. 2018;19(4):1017-9. https://doi.org/10.22034/APJCP.2018.19.4.1017

24. Zhang Z, Chen L, Humphries B, Brien R, Wicha MS, Luker KE, et al. Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest. Integr Biol (Camb). 2018;10(12):758-67. https://doi.org/10.1039/c8ib00106e

25. Motalleb G. Artificial neural network analysis in preclinical breast cancer. Cell J. 2014;15(4):324-31. PMID: 24381857

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Published

2026-03-13

How to Cite

Goulart, K. O. B., Kneubil, M. C., Brollo, J., Orlandin, B. C., Corso, L. L., Ely, M. R.-., & Henriques, J. A. P. (2026). Use of artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer. Mastology, 33. https://doi.org/10.29289/2594539420220041 e20220041

Issue

Vol. 33 (2023)

Section

Original Articles

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Copyright (c) 2026 Karen Olivia Bazzo Goulart, Maximiliano Cassilha Kneubil, Janaina Brollo, Bruna Caroline Orlandin, Leandro Luis Corso, Mariana Roesch- Ely, João Antonio Pêgas Henriques

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This work is licensed under a Creative Commons Attribution 4.0 International License.