Radiomics and artificial intelligence for predicting response to neoadjuvant drug therapy in patients with breast cancer: a review

Cover Page

Cite item

Full Text

Abstract

Breast cancer remains one of the most pressing challenges in modern oncology and is the most common malignant neoplasm among women worldwide. Breast cancer treatment requires a comprehensive approach, including surgery, chemotherapy, radiation therapy, targeted therapy, and hormone therapy. A particularly important role in current clinical practice belongs to neoadjuvant therapy—an approach administered prior to surgery, aimed at reducing tumor size, increasing the likelihood of breast-conserving surgery, and evaluating the tumor’s individual sensitivity to drug therapy. Neoadjuvant therapy is the standard of care for locally advanced, initially inoperable invasive breast cancer. It is also recommended as a first-line treatment for patients with initially operable but biologically aggressive tumor subtypes, such as triple-negative and HER2-positive breast cancer. However, individual responses to therapy vary significantly: some patients demonstrate a good response to neoadjuvant treatment, which markedly improves their prognosis, whereas in others the treatment may prove ineffective. Early prediction of therapeutic response to neoadjuvant treatment helps to avoid unnecessary drug dose exposure, reduce the financial burden on the healthcare system, and minimize the risk of adverse effects. In recent years, radiomics and artificial intelligence methods have been actively developed to analyze medical imaging and detect hidden biomarkers associated with treatment response. This review analyzes articles from recent decades in which diverse prognostic models were developed to evaluate neoadjuvant treatment response through the application of radiomics and artificial intelligence methods. Special attention is given to papers demonstrating the potential of machine learning and deep data analysis aimed at personalizing breast cancer therapy. These innovative approaches offer new opportunities for improving treatment effectiveness and patient survival.

About the authors

Maria M. Suleymanova

A.V. Vishnevsky National Medical Research Center of Surgery; Moscow City Hospital named after S.S. Yudin

Author for correspondence.
Email: maria.suleymanova95@gmail.com
ORCID iD: 0000-0002-5776-2693
SPIN-code: 7193-6122

MD

Russian Federation, Moscow; Moscow

Grigory G. Karmazanovsky

A.V. Vishnevsky National Medical Research Center of Surgery; The Russian National Research Medical University named after N.I. Pirogov

Email: karmazanovsky@yandex.ru
ORCID iD: 0000-0002-9357-0998
SPIN-code: 5964-2369

MD, Dr. Sci. (Medicine), Professor, academician of the Russian Academy of Sciences

Russian Federation, Moscow; Moscow

Evgeny V. Kondratyev

A.V. Vishnevsky National Medical Research Center of Surgery

Email: evgenykondratiev@gmail.com
ORCID iD: 0000-0001-7070-3391
SPIN-code: 2702-6526

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Anatoly Yu. Popov

A.V. Vishnevsky National Medical Research Center of Surgery

Email: vishnevskogo@ixv.ru
ORCID iD: 0000-0001-6267-8237
SPIN-code: 6197-2060

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Valentin A. Nechaev

Moscow City Hospital named after S.S. Yudin

Email: dfkz2005@gmail.com
ORCID iD: 0000-0002-6716-5593
SPIN-code: 2527-0130

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Maria V. Ermoshchenkova

Moscow City Hospital named after S.S. Yudin; Sechenov First Moscow State Medical University (Sechenov University)

Email: ermoshchenkova_m_v@staff.sechenov.ru
ORCID iD: 0000-0002-4178-9592
SPIN-code: 2557-7700

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow; Moscow

Evgeniya S. Kuzmina

Moscow City Hospital named after S.S. Yudin

Email: saparts@mail.ru
ORCID iD: 0009-0007-2856-5176
SPIN-code: 9668-5733

MD

Russian Federation, Moscow

References

  1. Giaquinto AN, Sung H, Miller KD, et al. Breast cancer statistics, 2022. CA: A Cancer Journal for Clinicians. 2022;72(6):524–541. doi: 10.3322/caac.21754 EDN: CTOZIC
  2. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209–249. doi: 10.3322/caac.21660 EDN: MRLXRI
  3. Tyulyandin SA, Artamonova EV, Zhigulev AN, et al. Breast cancer. Malignant tumours. 2023;13(3S2-1):157–200. (In Russ.) doi: 10.18027/2224-5057-2023-13-3s2-1-157-200 EDN: VMPFLQ
  4. Shien T, Iwata H. Adjuvant and neoadjuvant therapy for breast cancer. Japanese Journal of Clinical Oncology. 2020;50(3):225–229. doi: 10.1093/jjco/hyz213EDN: THCTHG
  5. Nounou MI, ElAmrawy F, Ahmed N, et al. Breast cancer: conventional diagnosis and treatment modalities and recent patents and technologies. Breast Cancer: Basic and Clinical Research. 2015;9:17–34. doi: 10.4137/BCBCR.S29420 EDN: VEUPUJ
  6. Spring LM, Bar Y, Isakoff SJ. The evolving role of neoadjuvant therapy for operable breast cancer. Journal of the National Comprehensive Cancer Network. 2022;20(6):723–734. doi: 10.6004/jnccn.2022.7016 EDN: HXCBOX
  7. Spring LM, Fell G, Arfe A, et al. Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis. Clinical Cancer Research. 2020;26(12):2838–2848. doi: 10.1158/1078-0432.CCR-19-3492 EDN: EGVDWS
  8. Wang H, Yee D. I-SPY 2: a neoadjuvant adaptive clinical trial designed to improve outcomes in high-risk breast cancer. Current Breast Cancer Reports. 2019;11(4):303–310. doi: 10.1007/s12609-019-00334-2 EDN: PGXZPD
  9. Boughey JC, McCall LM, Ballman KV, et al. Tumor biology correlates with rates of breast-conserving surgery and pathologic complete response after neoadjuvant chemotherapy for breast cancer. Annals of Surgery. 2014;260(4):608–616. doi: 10.1097/SLA.0000000000000924 EDN: UOPXUR
  10. Murphy BL, Day CN, Hoskin TL, et al. Neoadjuvant chemotherapy use in breast cancer is greatest in excellent responders: triple-negative and HER2+ subtypes. Annals of Surgical Oncology. 2018;25(8):2241–2248. doi: 10.1245/s10434-018-6531-5 EDN: YIOYKL
  11. Bossuyt V, Provenzano E, Symmans WF, et al. Recommendations for standardized pathological characterization of residual disease for neoadjuvant clinical trials of breast cancer by the BIG-NABCG collaboration. Annals of Oncology. 2015;26(7):1280–1291. doi: 10.1093/annonc/mdv161 EDN: VETAZF
  12. Pesapane F, Rotili A, Agazzi GM, et al. Recent radiomics advancements in breast cancer: lessons and pitfalls for the next future. Current Oncology. 2021;28(4):2351–2372. doi: 10.3390/curroncol28040217 EDN: YCIMNC
  13. Pesapane F, De Marco P, Rapino A, et al. How radiomics can improve breast cancer diagnosis and treatment. Journal of Clinical Medicine. 2023;12(4):1372. doi: 10.3390/jcm12041372 EDN: KNQSSO
  14. Szilágyi L, Kovács L. Special issue: artificial intelligence technology in medical image analysis. Applied Sciences. 2024;14(5):2180. doi: 10.3390/app14052180 EDN: XKFDCF
  15. Saltybaeva N, Tanadini-Lang S, Vuong D, et al. Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: multi-center study. Physics and Imaging in Radiation Oncology. 2022;22:131–136. doi: 10.1016/j.phro.2022.05.006 EDN: YAXEPH
  16. Madabhushi A, Udupa JK. New methods of MR image intensity standardization via generalized scale. Medical Physics. 2006;33(9):3426–3434. doi: 10.1118/1.2335487
  17. Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. European Radiology Experimental. 2018;2(1):1–8. doi: 10.1186/s41747-018-0068-z EDN: FCYFNJ
  18. Baeßler B, Weiss K, Pinto dos Santos D. Robustness and reproducibility of radiomics in magnetic resonance imaging. Invest Radiol. 2019;54(4):221–228. doi: 10.1097/RLI.0000000000000530
  19. van Timmeren JE, Cester D, Tanadini-Lang S, et al. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020;11(1):91. doi: 10.1186/s13244-020-00887-2
  20. Zanca F, Brusasco C, Pesapane F, et al. Regulatory aspects of the use of artificial intelligence medical software. Seminars in Radiation Oncology. 2022;32(4):432–441. doi: 10.1016/j.semradonc.2022.06.012 EDN: WHHHQD
  21. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Annals of Internal Medicine. 2015;162(1):55–63. doi: 10.7326/M14-0697
  22. Coleman C. Early detection and screening for breast cancer. Seminars in Oncology Nursing. 2017;33(2):141–155. doi: 10.1016/j.soncn.2017.02.009
  23. Prasad SN, Houserkova D. The role of various modalities in breast imaging. Biomedical Papers. 2007;151(2):209–218. doi: 10.5507/bp.2007.036
  24. Katzen J, Dodelzon K. A review of computer aided detection in mammography. Clinical Imaging. 2018;52:305–309. doi: 10.1016/j.clinimag.2018.08.014
  25. Shin HK, Kim WH, Kim HJ, et al. Prediction of pathological complete response to neoadjuvant chemotherapy using multi-scale patch learning with mammography. Lecture Notes in Computer Science. 2021;12928 LNCS:192–200. doi: 10.1007/978-3-030-87602-9_18 EDN: RSETVX
  26. Skarping I, Larsson M, Förnvik D. Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept. European Radiology. 2021;32(5):3131–3141. doi: 10.1007/s00330-021-08306-w EDN: GHBHYP
  27. Bhimani C, Matta D, Roth RG, et al. Contrast-enhanced spectral mammography. Academic Radiology. 2017;24(1):84–88. doi: 10.1016/j.acra.2016.08.019
  28. Patel BK, Lobbes MBI, Lewin J. Contrast enhanced spectral mammography: a review. Seminars in Ultrasound, CT and MRI. 2018;39(1):70–79. doi: 10.1053/j.sult.2017.08.005
  29. Richter V, Hatterman V, Preibsch H, et al. Contrast-enhanced spectral mammography in patients with MRI contraindications. Acta Radiologica. 2017;59(7):798–805. doi: 10.1177/0284185117735561
  30. Mann RM, Balleyguier C, Baltzer PA, et al. Breast MRI: EUSOBI recommendations for women’s information. European Radiology. 2015;25(12):3669–3678. doi: 10.1007/s00330-015-3807-z EDN: NQYZQI
  31. Sorin V, Sklair-Levy M. Dual-energy contrast-enhanced spectral mammography (CESM) for breast cancer screening. Quantitative Imaging in Medicine and Surgery. 2019;9(11):1914–1917. doi: 10.21037/qims.2019.10.13
  32. Xing D, Mao N, Dong J, et al. Quantitative analysis of contrast enhanced spectral mammography grey value for early prediction of pathological response of breast cancer to neoadjuvant chemotherapy. Scientific Reports. 2021;11(1):5892. doi: 10.1038/s41598-021-85353-9 EDN: KGOWWC
  33. Wang Z, Lin F, Ma H, et al. Contrast-enhanced spectral mammography-based radiomics nomogram for the prediction of neoadjuvant chemotherapy-insensitive breast cancers. Frontiers in Oncology. 2021;11(APR):605230. doi: 10.3389/fonc.2021.605230 EDN: JZIGEN
  34. Mao N, Shi Y, Lian C, et al. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography. European Radiology. 2022;32(5):3207–3219. doi: 10.1007/s00330-021-08414-7 EDN: KUXMIN
  35. Tadayyon H, Sannachi L, Gangeh M, et al. Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach. Oncotarget. 2016;7(29):45094–45111. doi: 10.18632/oncotarget.8862 EDN: WSJOFB
  36. Tadayyon H, Sadeghi-Naini A, Czarnota GJ. Noninvasive characterization of locally advanced breast cancer using textural analysis of quantitative ultrasound parametric images. Translational Oncology. 2014;7(6):759–767. doi: 10.1016/j.tranon.2014.10.007 EDN: UTKPBJ
  37. Jiang M, Li CL, Luo XM, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. European Journal of Cancer. 2021;147:95–105. doi: 10.1016/j.ejca.2021.01.028 EDN: ZEWDBI
  38. Byra M, Dobruch-Sobczak K, Piotrzkowska-Wroblewska H, et al. Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals. Physics in Medicine & Biology. 2022;67(18):185007. doi: 10.1088/1361-6560/ac8c82 EDN: IJMZEF
  39. Sadeghi-Naini A, Sannachi L, Pritchard K, et al. Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture. Oncotarget. 2014;5(11):3497–3511. doi: 10.18632/oncotarget.1950
  40. Sannachi L, Gangeh M, Tadayyon H, et al. Breast cancer treatment response monitoring using quantitative ultrasound and texture analysis: comparative analysis of analytical models. Translational Oncology. 2019;12(10):1271–1281. doi: 10.1016/j.tranon.2019.06.004
  41. DiCenzo D, Quiaoit K, Fatima K, et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study. Cancer Medicine. 2020;9(16):5798–5806. doi: 10.1002/cam4.3255 EDN: ZMGGKI
  42. Tadayyon H, Sannachi L, Gangeh MJ, et al. A priori prediction of neoadjuvant chemotherapy response and survival in breast cancer patients using quantitative ultrasound. Scientific Reports. 2017;7(1):45733. doi: 10.1038/srep45733
  43. Ma Y, Zhang S, Zang L, et al. Combination of shear wave elastography and Ki-67 index as a novel predictive modality for the pathological response to neoadjuvant chemotherapy in patients with invasive breast cancer. European Journal of Cancer. 2016;69:86–101. doi: 10.1016/j.ejca.2016.09.031 EDN: XTWRWB
  44. Prado-Costa R, Rebelo J, Monteiro-Barroso J, Preto AS. Ultrasound elastography: compression elastography and shear-wave elastography in the assessment of tendon injury. Insights into Imaging. 2018;9(5):791–814. doi: 10.1007/s13244-018-0642-1 EDN: BRVYAJ
  45. Fernandes J, Sannachi L, Tran WT, et al. Monitoring breast cancer response to neoadjuvant chemotherapy using ultrasound strain elastography. Translational Oncology. 2019;12(9):1177–1184. doi: 10.1016/j.tranon.2019.05.004
  46. Gu J, Polley EC, Denis M, et al. Early assessment of shear wave elastography parameters foresees the response to neoadjuvant chemotherapy in patients with invasive breast cancer. Breast Cancer Research. 2021;23(1):1–13. doi: 10.1186/s13058-021-01429-4 EDN: JJUKQT
  47. Fusco R, Sansone M, Filice S, et al. Pattern recognition approaches for breast cancer DCE-MRI classification: a systematic review. Journal of Medical and Biological Engineering. 2016;36(4):449–459. doi: 10.1007/s40846-016-0163-7
  48. Pesapane F, Agazzi GM, Rotili A, et al. Prediction of the pathological response to neoadjuvant chemotherapy in breast cancer patients with mri-radiomics: a systematic review and meta-analysis. Current Problems in Cancer. 2022;46(5):100883. doi: 10.1016/j.currproblcancer.2022.100883 EDN: QQBBCI
  49. Jimenez JE, Abdelhafez A, Mittendorf EA, et al. A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer. European Journal of Radiology. 2022;149:110220. doi: 10.1016/j.ejrad.2022.110220 EDN: WEMAMC
  50. Jahani N, Cohen E, Hsieh MK, et al. Prediction of treatment response to neoadjuvant chemotherapy for breast cancer via early changes in tumor heterogeneity captured by DCE-MRI registration. Scientific Reports. 2019;9(1):12114. doi: 10.1038/s41598-019-48465-x EDN: NCWAXK
  51. Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Research. 2020;22(1):1–11. doi: 10.1186/s13058-020-01291-w EDN: SKNHGS
  52. Fan M, Chen H, You C, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer. Frontiers in Molecular Biosciences. 2021;8(APR):622219. doi: 10.3389/fmolb.2021.622219 EDN: KRRZTI
  53. Hussain L, Huang P, Nguyen T, et al. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response. BioMedical Engineering OnLine. 2021;20(1):63. doi: 10.1186/s12938-021-00899-z EDN: XODCOF
  54. Cho N, Im SA, Park IA, et al. Breast cancer: early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging. Radiology. 2014;272(2):385–396. doi: 10.1148/radiol.14131332 EDN: UUBLDJ
  55. Drisis S, El Adoui M, Flamen P, et al. Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI. Journal of Magnetic Resonance Imaging. 2019;51(5):1403–1411. doi: 10.1002/jmri.26996
  56. Comes MC, Fanizzi A, Bove S, et al. Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs. Scientific Reports. 2021;11(1):14123. doi: 10.1038/s41598-021-93592-z EDN: IFRRXB
  57. Peng Y, Cheng Z, Gong C, et al. Pretreatment DCE-MRI-Based deep learning outperforms radiomics analysis in predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. Frontiers in Oncology. 2022;12:846775. doi: 10.3389/fonc.2022.846775 EDN: ERVNRP
  58. Li Y, Chen Y, Zhao R, et al. Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer. European Radiology. 2021;32(3):1676–1687. doi: 10.1007/s00330-021-08291-0 EDN: MWHPGI

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Supplement 1. Application of conventional and contrast-enhanced spectral mammography for evaluating the response to neoadjuvant chemotherapy in breast cancer patients
Download (27KB)
Indexing metadata
3. Supplement 2. Application of ultrasound for evaluating the response to neoadjuvant chemotherapy in breast cancer patients
Download (28KB)
Indexing metadata
4. Appendix 3. Application of magnetic resonance imaging with dynamic contrast enhancement for evaluating the response to neoadjuvant chemotherapy in breast cancer patients
Download (33KB)
Indexing metadata

Copyright (c) 2025 Eco-Vector

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

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».