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Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

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Abstract

Objectives

To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

Materials and methods

Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases.

Results

Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction.

Conclusion

Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC.

Clinical relevance statement

A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes.

Key Points

• Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis.

• Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC.

• Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.

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Abbreviations

BES:

Breast Edema Score

BC:

Breast cancer

BI-RADS:

Breast Imaging and Reporting Data System

HER2:

Human epidermal growth factor receptor 2

NAC:

Neoadjuvant chemotherapy

NME:

Non-mass enhancement

pCR:

Pathologic complete response

RCB:

Residual cancer burden

TIL:

Tumor-infiltrating lymphocyte

TN:

Triple negative

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Acknowledgements

We thank Laetitia Chanas, Maud Milder, Mathieu Carton and Alexia Savignoni for their assistance with the compilation of the data.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d’Ulm, 75005, Paris, France

    Caroline Malhaire, Catherine Ala Eddine, Melodie Bereby-Kahane, Julie Manceau, Delphine Sebbag-Sfez & Herve Brisse

  2. Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France

    Caroline Malhaire, Marie-Judith Saint-Martin & Frederique Frouin

  3. Gustave Roussy, Department of Imaging, Paris-Saclay University, 94805, Villejuif, France

    Fatine Selhane

  4. Pathology Unit, Centre Léon Bérard, 28 Rue Laennec, 69008, Lyon, France

    Vincent Cockenpot

  5. Women Imaging Unit, HCL, Radiologie du Groupement Hospitalier Est, 3 Quai Des Célestins, 69002, Lyon, France

    Pia Akl

  6. Department of Surgical Oncology, Institut Curie, 26 Rue d’Ulm, 75005, Paris, France

    Enora Laas & Fabien Reyal

  7. Department of Medical Oncology, Institut Curie, 26 Rue d’Ulm, 75005, Paris, France

    Audrey Bellesoeur & Jean-Yves Pierga

  8. Department of Pathology, Institut Curie, 26 Rue d’Ulm, 75005, Paris, France

    Anne Vincent-Salomon

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  1. Caroline Malhaire
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Correspondence to Caroline Malhaire.

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The scientific guarantor of this publication is Caroline Malhaire.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Malhaire, C., Selhane, F., Saint-Martin, MJ. et al. Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy. Eur Radiol 33, 8142–8154 (2023). https://doi.org/10.1007/s00330-023-09797-5

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