Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
- Diagnostic core needle biopsy confirms the diagnosis of invasive BC;
- Preoperative breast MRI performed at our department with full MRI data;
- Availability of the final histological analysis on the surgical specimen, which includes the tumor biological profile (ER and PgR status, HER-2 status) and Ki67 status;
- No prior history of surgical, radiant, or neoadjuvant chemotherapy breast treatment prior to the MRI examination
2.2. MRI Examination and Evaluation
- T2-weighted axial single-shot fast spin echo sequence with a modified Dixon technique (IDEAL) for intravoxel fat–water separation (TR/TE 3500–5200/120–135 ms, slice thickness 3.5 mm).
- Diffusion-weighted axial single-shot echo-planar sequence (TR/TE 2700/58 ms, slice thickness 5 mm) with a diffusion-sensitizing gradient with a b-value of 0, 500, and 1000 s/mm2.
- Dynamic 3D-T1w axial and sagittal gradient echo sequence with fat suppression after injection of 0.1 mmol/kg body weight of Gadoteric acid (Dotarem®, Guerbet S.p.A, Villepinte France, or Claricyclic®, GE Healthcare S.r.l., Chicago, IL, USA) at a rate of 2 mL/s, followed by a bolus of 15 mL saline flush (TR/TE 4/2 ms, slice thickness 2.4 mm) before and five to ten times after intravenous contrast medium injection.
- Size (mm) and shape;
- Type of enhancement (mass/non-mass like);
- Intralesional enhancement: homogeneous, heterogeneous, or rim;
- Tumor-associated edema on T2-weighted images;
- Intratumoral necrosis;
- Tumor multifocality/multicentricity (in these situations, the statistical analysis included the largest lesion, which was regarded as the index one);
- Axillary lymph-node involvement;
- Level of background parenchymal enhancement (BPE) according to the BI-RADS lexicon.
2.3. Histologic Evaluation
- Luminal A;
- Luminal B;
- Triple Negative (TN);
- HER2+.
2.4. Statistical Analysis
- p = 0.10 was used for candidate predictor variables in the regression model;
- p = 0.025 (Bonferroni correction) was used for the OR of the final proposed model, which uses two predictor radiological variables;
- p = 0.00625 (Bonferroni correction) was used for assessing the reliability of the proposed model as a whole, accounting for all eight radiological variables that were screened for the model after the exploratory analysis.
3. Results
3.1. Clinicopathologic Features
- Patients with high TIL levels (≥10%; 54/145 patients).
- Patients with low TIL levels (<10%; 91/145 patients).
3.2. MRI Features
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Taube, J.M.; Klein, A.; Brahmer, J.R.; Xu, H.; Pan, X.; Kim, J.H.; Chen, L.; Pardoll, D.M.; Topalian, S.L.; Anders, R.A. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti–PD-1 therapy. Clin. Cancer Res. 2014, 20, 5064–5074. [Google Scholar] [CrossRef] [PubMed]
- Borghaei, H.; Paz-Ares, L.; Horn, L.; Spigel, D.R.; Steins, M.; Ready, N.E.; Chow, L.Q.; Vokes, E.E.; Felip, E.; Holgado, E. Nivolumab versus docetaxel in advanced non-squamous non–small-cell lung cancer. N. Engl. J. Med. 2015, 373, 1627–1639. [Google Scholar] [CrossRef]
- Robert, C.; Long, G.V.; Brady, B.; Dutriaux, C.; Maio, M.; Mortier, L.; Hassel, J.C.; Rutkowski, P.; McNeil, C.; Kalinka-Warzocha, E. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 2015, 372, 320–330. [Google Scholar] [CrossRef] [PubMed]
- Schmid, P.; Adams, S.; Rugo, H.S.; Schneeweiss, A.; Barrios, C.H.; Iwata, H.; Diéras, V.; Hegg, R.; Im, S.-A.; Shaw Wright, G.; et al. Atezolizumab and Nab-Paclitaxel in Advanced Triple-Negative Breast Cancer. N. Engl. J. Med. 2018, 379, 2108–2121. [Google Scholar] [CrossRef]
- Krasniqi, E.; Barchiesi, G.; Pizzuti, L.; Mazzotta, M.; Venuti, A.; Maugeri-Saccà, M.; Sanguineti, G.; Massimiani, G.; Sergi, D.; Carpano, S.; et al. Immunotherapy in HER2-positive breast cancer: State of the art and future perspectives. J. Hematol. Oncol. 2019, 12, 111. [Google Scholar] [CrossRef] [PubMed]
- Goldberg, J.; Pastorello, R.G.; Vallius, T.; Davis, J.; Cui, Y.X.; Agudo, J.; Waks, A.G.; Keenan, T.; McAllister, S.S.; Tolaney, S.M.; et al. The Immunology of Hormone Receptor Positive Breast Cancer. Front. Immunol. 2021, 12, 674192. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Nanda, R.; Liu, M.C.; Yau, C.; Shatsky, R.; Pusztai, L.; Wallace, A.; Chien, A.J.; Forero-Torres, A.; Ellis, E.; Han, H.; et al. Effect of Pembrolizumab Plus Neoadjuvant Chemotherapy on Pathologic Complete Response in Women With Early-Stage Breast Cancer: An Analysis of the Ongoing Phase 2 Adaptively Randomized I-SPY2 Trial. JAMA Oncol. 2020, 6, 676–684. [Google Scholar] [CrossRef]
- Topalian, S.L.; Taube, J.M.; Anders, R.A.; Pardoll, D.M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer 2016, 16, 275–287. [Google Scholar] [CrossRef]
- Fridman, W.H.; Zitvogel, L.; Sautès–Fridman, C.; Kroemer, G. The immune contexture in cancer prognosis and treatment. Nat. Rev. Clin. Oncol. 2017, 14, 717–734. [Google Scholar] [CrossRef]
- Denkert, C.; Loibl, S.; Noske, A.; Roller, M.; Müller, B.M.; Komor, M.; Budczies, J.; Darb-Esfahani, S.; Kronenwett, R.; Hanusch, C.; et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J. Clin. Oncol. 2010, 28, 105–113. [Google Scholar] [CrossRef]
- Loi, S.; Sirtaine, N.; Piette, F.; Salgado, R.; Viale, G.; Van Eenoo, F.; Rouas, G.; Francis, P.; Crown, J.P.; Hitre, E.; et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J. Clin. Oncol. 2013, 31, 860–867. [Google Scholar] [CrossRef] [PubMed]
- Loi, S.; Michiels, S.; Salgado, R.; Sirtaine, N.; Jose, V.; Fumagalli, D.; Kellokumpu-Lehtinen, P.L.; Bono, P.; Kataja, V.; Desmedt, C.; et al. Tumor infiltrating lymphocytes is prognostic and predictive for trastuzumab benefit in early breast cancer: Results from the FinHER trial. Ann. Oncol. 2014, 25, 1544–1550. [Google Scholar] [CrossRef] [PubMed]
- Adams, S.; Gray, R.J.; Demaria, S.; Goldstein, L.; Perez, E.A.; Shulman, L.N.; Martino, S.; Wang, M.; Jones, V.E.; Saphner, T.J.; et al. Prognostic value of tumor-infiltrating lymphocytes (TILs) in Triple-Negative Breast Cancers (TNBC) from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. J. Clin. Oncol. 2014, 32, 2959–2966. [Google Scholar] [CrossRef]
- Yamaguchi, R.; Tanaka, M.; Yano, A.; Tse, G.M.; Yamaguchi, M.; Koura, K.; Kanomata, N.; Kawaguchi, A.; Akiba, J.; Naito, Y.; et al. Tumor-infiltrating lymphocytes are important pathologic predictors for neoadjuvant chemotherapy in patients with breast cancer. Hum. Pathol. 2012, 43, 1688–1694. [Google Scholar] [CrossRef]
- Salgado, R.; Denkert, C.; Demaria, S.; Sirtaine, N.; Klauschen, F.; Pruneri, G.; Wienert, S.; Van den Eynden, G.; Baehner, F.L.; Penault-Llorca, F.; et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: Recommendations by an International TILs Working Group 2014. Ann. Oncol. 2015, 26, 259–271. [Google Scholar] [CrossRef] [PubMed]
- Solinas, C.; Ceppi, M.; Lambertini, M.; Scartozzi, M.; Buisseret, L.; Garaud, S.; Fumagalli, D.; de Azambuja, E.; Salgado, R.; Sotiriou, C.; et al. Tumour infiltrating lymphocytes in patients with HER-2 positive breast cancer treated with neoadjuvant chemotherapy plus trastuzumab, lapatinib or their combination: A meta-analysis of randomized controlled trials. Cancer Treat Rev. 2017, 57, 8–15. [Google Scholar] [CrossRef]
- Choi, W.J.; Kim, Y.; Cha, J.H.; Shin, H.J.; Chae, E.Y.; Yoon, G.Y.; Kim, H.H. Correlation between magnetic resonance imaging and the level of tumor-infiltrating lymphocytes in patients with estrogen receptor-negative HER2-positive breast cancer. Acta Radiol. 2020, 61, 3–10. [Google Scholar] [CrossRef]
- Ku, Y.J.; Kim, H.H.; Cha, J.H.; Shin, H.J.; Chae, E.Y.; Choi, W.J.; Lee, H.J.; Gong, G. Predicting the level of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer: Usefulness of breast MRI computer-aided detection and diagnosis. J. Magn. Reson. Imaging 2018, 47, 760–766. [Google Scholar] [CrossRef]
- Wu, J.; Li, X.; Teng, X.; Rubin, D.L.; Napel, S.; Daniel, B.L.; Li, R. Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. Breast Cancer Res. 2018, 20, 101. [Google Scholar] [CrossRef]
- D’Orsi, C.J.; Sickles, E.A.; Mendelson, E.B.; Morris, E.A. ACR BI-RADS Athlas: Breast Imaging Reporting and Data System; American College of Radiology: Reston, VA, USA, 2013. [Google Scholar]
- Çelebi, F.; Agacayak, F.; Ozturk, A.; Ilgun, S.; Ucuncu, M.; Iyigun, Z.E.; Ordu, Ç.; Pilanci, K.N.; Alco, G.; Gultekin, S.; et al. Usefulness of imaging findings in predicting tumor-infiltrating lymphocytes in patients with breast cancer. Eur. Radiol. 2019, 30, 2049–2057. [Google Scholar] [CrossRef]
- Burstein, H.J.; Curigliano, G.; Thürlimann, B.; Weber, W.P.; Poortmans, P.; Regan, M.M.; Senn, H.J.; Winer, E.P.; Gnant, M.; Panelists of the St Gallen Consensus Conference. Customizing local and systemic therapies for women with early breast cancer: The St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021. Ann. Oncol. 2021, 32, 1216–1235. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Mao, Y.; Qu, Q.; Zhang, Y.; Liu, J.; Chen, X.; Shen, K. The value of tumor infiltrating lymphocytes (TILs) for predicting response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis. PLoS ONE 2014, 9, e115103. [Google Scholar] [CrossRef] [PubMed]
- Denkert, C.; von Minckwitz, G.; Darb-Esfahani, S.; Lederer, B.; Heppner, B.I.; Weber, K.E.; Budczies, J.; Huober, J.; Klauschen, F.; Furlanetto, J.; et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: A pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 2018, 1, 40–50. [Google Scholar] [CrossRef] [PubMed]
- Lee, H.-J.; Lee, J.E.; Jeong, W.G.; Ki, S.Y.; Park, M.H.; Lee, J.S.; Nah, Y.K.; Lim, H.S. HER2-Positive Breast Cancer: Association of MRI and Clinicopathologic Features With Tumor-Infiltrating Lymphocytes. Am. J. Roentgenol. 2022, 218, 258–269. [Google Scholar] [CrossRef] [PubMed]
- Fogante, M.; Tagliati, C.; De Lisa, M.; Berardi, R.; Giuseppetti, G.M.; Giovagnoni, A. Correlation between apparent diffusion coefficient of magnetic resonance imaging and tumor-infiltrating lymphocytes in breast cancer. Radiol. Med. 2019, 124, 581–587. [Google Scholar] [CrossRef]
- Bian, T.; Wu, Z.; Lin, Q.; Mao, Y.; Wang, H.; Chen, J.; Chen, Q.; Fu, G.; Cui, C.; Su, X. Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer Using Preoperative MRI-Based Radiomics. J. Magn. Reson. Imaging 2022, 55, 772–784. [Google Scholar] [CrossRef] [PubMed]
- Perez, E.A.; Ballman, K.V.; Tenner, K.S.; Thompson, E.A.; Badve, S.S.; Bailey, H.; Baehner, F.L. Association of stromal tumor-infiltrating lymphocytes with recurrence-free survival in the N9831 adjuvant trial in patients with early-stage HER2-positive breast cancer. JAMA Oncol. 2016, 2, 56–64. [Google Scholar] [CrossRef]
- Ku, Y.J.; Kim, H.H.; Cha, J.H.; Shin, H.J.; Baek, S.H.; Lee, H.J.; Gong, G. Correlation between MRI and the level of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer. Am. J. Roentgenol. 2016, 207, 1146–1151. [Google Scholar] [CrossRef]
- Su, G.-H.; Xiao, Y.; Jiang, L.; Zheng, R.-C.; Wang, H.; Chen, Y.; Gu, Y.-J.; You, C.; Shao, Z.-M. Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer. J. Transl. Med. 2022, 20, 471. [Google Scholar] [CrossRef]
Age (mean, SD) | 56.8 (12.4) |
Post menopause (n, %) | 87 (60%) |
Localization (n, %) | |
UQQ | 50 (34.72%) |
UIQ | 16 (11.11%) |
QII | 9 (6.25%) |
LOQ | 10 (6.94%) |
LIQ | 15 (10.42%) |
LQ | 9 (6.25%) |
IQ | 5 (3.47%) |
OQ | 16 (11.11%) |
RT | 14 (9.72%) |
Type (n, %) | |
Luminal A | 53 (36.55%) |
Luminal B | 80 (55.17%) |
HER-2 | 3 (2.07%) |
TN | 9 (6.21%) |
Grade (n, %) | |
1 | 25 (17.24%) |
2 | 82 (56.55%) |
3 | 38 (26.21%) |
TILs (mean, SD) | 9.72 (13.02) |
TILs > 10% (n, %) | 32 (22.07%) |
UNIVARIATE LOGISTIC MODEL | OR | 95% CI | p-Value |
---|---|---|---|
ADC | 0.04 | 0.03–0.05 | 0.01 |
Kinetic Curve | 0.22 | ||
I | 1 (baseline) | - | |
II | 1.60 | 0.41–6.23 | 0.49 |
III | 2.75 | 0.71–10.51 | 0.14 |
Enhancement | 0.01 | ||
Homogeneous | 1 (baseline) | - | |
Heterogeneous | 0.27 | 0.12–0.61 | 0.01 |
Rim | 0.01 | 0.01 | 0.02 |
Edema | 0.83 | ||
Absent | 1 (baseline) | - | |
Peri-tumoral | 0.7741935 | 0.29–2.01 | 0.59 |
Pre-pectoral | 0.6857143 | 0.08–6.19 | 0.74 |
Subcutaneous | 2.285716 | 0.36–14.59 | 0.38 |
More than 1 component | 1.714286 | 0.15–19.85 | 0.67 |
Margins | 0.34 | ||
Regular | 1 (baseline) | - | |
Irregular | 0.625 | 0.11–3.59 | 0.60 |
Lobular | 0.91 | 0.12–6.71 | 0.91 |
Spiculated | 1.10 | 0.18–6.57 | 0.92 |
Non-mass | 0.25 | 0.03–2.24 | 0.22 |
Size | 0.99 | 0.96–1.02 | 0.67 |
Stadiation | 0.41 | ||
Unifocal | 1 (baseline) | - | |
Multifocal | 0.60 | 0.20–1.81 | 0.38 |
Multicentric | 0.63 | 0.24–1.66 | 0.35 |
Bilateral | 0.01 | - | - |
BPE | 0.49 | ||
I | 1 (baseline) | - | |
II | 0.52 | 0.21–1.28 | 0.16 |
III | 0.70 | 0.20–2.38 | 0.56 |
IV | 0.44 | 0.05–3.87 | 0.46 |
MULTIVARIABLE LOGISTIC MODEL | 0.01 | ||
ADC | 0.01 | ||
Enhancement | |||
Homogeneous | 1 (baseline) | - | |
Heterogeneous | 0.27 | 0.11–0.64 | 0.01 |
Peripheral | 0.1 | - | 0.99 |
Constant | 20.77 | 1.43–301.13 | 0.02 |
MODEL PARAMETERS | |||
Model p-value | 0.0001 | ||
Sensibility | 0.75 | ||
Specificity | 0.63 | ||
Accuracy | 0.75 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gigli, S.; David, E.; Bonito, G.; Favale, L.; di Sero, S.; Vinci, A.; Manganaro, L.; Ricci, P. Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes? Biomedicines 2025, 13, 1364. https://doi.org/10.3390/biomedicines13061364
Gigli S, David E, Bonito G, Favale L, di Sero S, Vinci A, Manganaro L, Ricci P. Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes? Biomedicines. 2025; 13(6):1364. https://doi.org/10.3390/biomedicines13061364
Chicago/Turabian StyleGigli, Silvia, Emanuele David, Giacomo Bonito, Luisa Favale, Silvia di Sero, Antonio Vinci, Lucia Manganaro, and Paolo Ricci. 2025. "Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes?" Biomedicines 13, no. 6: 1364. https://doi.org/10.3390/biomedicines13061364
APA StyleGigli, S., David, E., Bonito, G., Favale, L., di Sero, S., Vinci, A., Manganaro, L., & Ricci, P. (2025). Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes? Biomedicines, 13(6), 1364. https://doi.org/10.3390/biomedicines13061364