Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May;130(5):753-765.
doi: 10.1007/s11547-025-01984-2. Epub 2025 Mar 21.

Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria

Charlotte Trombadori #  1 , Edda Boccia #  2 , Elena Huong Tran  2 , Antonio Franco  3 , Armando Orlandi  4 , Gianluca Franceschini  5 , Luisa Carbognin  4 , Alba Di Leone  5 , Valeria Masiello  6 , Fabio Marazzi  6 , Antonella Palazzo  4 , Ida Paris  4 , Roberta Dattoli  1 , Antonino Mulè  7 , Nikola Dino Capocchiano  8 , Diana Giannarelli  9 , Riccardo Masetti  5 , Paolo Belli  1 , Luca Boldrini  2 , Anna D'Angelo  1 , Alessandra Fabi  9
Affiliations

Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria

Charlotte Trombadori et al. Radiol Med. 2025 May.

Abstract

Background: Breast cancer (BC) is a major global health issue with significant heterogeneity among its subtypes. Neoadjuvant treatment (NAT) has been extended to include early BC patients, particularly those with HER2 + and triple-negative subtypes, to achieve pathological complete response and improve long-term outcomes. However, disease recurrence remains a challenge, highlighting the need for predictive biomarkers. This study evaluates the role of radiomics from pre-treatment breast MRI, integrated with clinical and radiological variables, in predicting early disease recurrence (EDR) after NAT.

Methods: A retrospective analysis was conducted on 238 BC patients treated with NAT and assessed using pre- and post-treatment breast MRI. Radiomic features were extracted and combined with clinical and radiological data to develop predictive models for EDR. Models were evaluated using AUC, accuracy, sensitivity, and specificity metrics.

Results: The radiological-radiomic model, which integrated pre-treatment MRI radiomics with RECIST response data, demonstrated the highest predictive performance for EDR (AUC 0.77, sensitivity 0.85). Internal validation confirmed the robustness of the model.

Conclusion: Combining radiomic features from pre-NAT MRI with RECIST response evaluation from post-NAT MRI enhances the prediction of EDR in BC patients, supporting precision medicine in treatment strategies and follow-up planning. Further validation on larger cohorts is needed to confirm these findings.

Keywords: Breast cancer; Disease-free survival; Magnetic resonance imaging; RECIST criteria; Radiomics.

PubMed Disclaimer

Conflict of interest statement

Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Human participants and/or animals: This study did not involve human participants or animals, as it is a retrospective analysis. Informed consent: This study was approved by our institutional ethics committee. Written informed consent from patients was waived due to the retrospective nature of the research.

References

    1. Iwamoto Y, Kaucher S, Lorenz E, Bärnighausen T, Winkler V (2019) Development of breast cancer mortality considering the implementation of mammography screening programs - a comparison of western European countries. BMC Public Health 19(1):823. https://doi.org/10.1186/s12889-019-7166-6 - DOI - PubMed - PMC
    1. Balic M, Thomssen C, Würstlein R, Gnant M, Harbeck N (2019) St. Gallen/Vienna 2019: a brief summary of the consensus discussion on the optimal primary breast cancer treatment. Breast Care (Basel). 14(2):103–110. https://doi.org/10.1159/000499931 - DOI - PubMed - PMC
    1. Gralow JR, Burstein HJ, Wood W, Hortobagyi GN, Gianni L, von Minckwitz G, Buzdar AU, Smith IE, Symmans WF, Singh B, Winer EP (2008) Preoperative therapy in invasive breast cancer: pathologic assessment and systemic therapy issues in operable disease. J Clin Oncol 26(5):814–819. https://doi.org/10.1200/JCO.2007.15.3510 - DOI - PubMed
    1. Schott AF, Hayes DF (2012) Defining the benefits of neoadjuvant chemotherapy for breast cancer. J Clin Oncol 30(15):1747–1749. https://doi.org/10.1200/JCO.2011.41.3161 - DOI - PubMed
    1. Rouzier R, Perou CM, Symmans WF, Ibrahim N, Cristofanilli M, Anderson K, Hess KR, Stec J, Ayers M, Wagner P, Morandi P, Fan C, Rabiul I, Ross JS, Hortobagyi GN, Pusztai L (2005) Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 11(16):5678–5685. https://doi.org/10.1158/1078-0432.CCR-04-2421 - DOI - PubMed

LinkOut - more resources