Post-treatment MRI to predict pathological complete response in triple-negative breast cancer following neoadjuvant chemoimmunotherapy.

T Toulsie Ramtohul (Institut Curie, Paris, Ile de France, France) D Derek Lollivier (Institut Curie, 26 Rue D'ulm, Ile de France, France) J Justine Spriet (Oscar Lambret, Lille, France) M Maxime Jin (Institut Curie, Paris and St Cloud, France) L Lounes Djerroudi T Thomas Gaillard C Claire Bonneau (Department of Surgical Oncology, Institut Curie - Saint-Cloud, University of Versailles Saint-Quentin en Yvelines, INSERM U1331, Institut des Cancers de la Femme, Saint-Cloud, France and GINECO, Paris and St Cloud, France) D Delphine Loirat D Diana Bello Roufai (Medical Oncology Department, Institut Curie, Saint-Cloud, France) Y Youlia M. Kirova (Institute Curie, Department of Radiation Oncology, Paris, France) P Pierre Loap (Institute Curie, Department of Radiation Oncology, Paris, France) C Caroline Malhaire A Anne Vincent-Salomon F François Clément Bidard A Anne Tardivon A Audrey Mailliez (Department of Medical Oncology, Breast Cancer Unit, Centre Oscar Lambret, Lille, France) L Lauren Wallaert L Luc Ceugnart (Oscar Lambret Comprehensive Cancer Center, Lille, France) C Caroline Nhy (Institut Curie, Paris, France) L Luc Cabel

Abstract

590 Background: Neoadjuvant chemoimmunotherapy (NACI) has significantly improved pathological complete response (pCR) rates in early-stage triple-negative breast cancer (TNBC). However, the predictive accuracy of post-treatment MRI for pCR remains unexplored. Our objective was to assess the performance of post-treatment MRI in predicting pCR in TNBC patients treated with NACI. Methods: In this prospective, multicenter study (August 2021–June 2024), women with early-stage TNBC were recruited from three centers. Post-treatment dynamic contrast-enhanced (DCE) MRI data were analyzed across multiple vendors. The predictive performance of radiological complete response (rCR) on MRI for pCR was evaluated using the area under the curve (AUC) of receiver operating characteristics. A multivariable logistic regression model incorporating rCR, nodal involvement, and Ki-67 levels was developed and validated. For patients with residual enhancement, a radiomics score was generated using first-order and shape-based features. Results: The study included 175 women in a training set from centers #1 (mean age 49 ± 11 years) and 84 in an external test set from centers #2 and #3 (mean age 52 ± 12 years). MRI rCR achieved an AUC of 0.83 (95% CI: 0.75–0.92) for pCR prediction. A combined model with rCR, nodal status, and Ki-67 levels yielded an AUC of 0.88 (95% CI: 0.81–0.96) in the test set. Among patients with rCR, no nodal involvement, and Ki-67 >30%, the false-positive rate was 3.6% and 3.5% in the training and test sets, respectively, with all cases limited to Residual Cancer Burden-I. For patients with residual enhancement, a model incorporating a radiomics score and lesion count achieved an AUC of 0.80 (95% CI: 0.69–0.90). Conclusions: Post-treatment MRI effectively predicts pCR in early-stage TNBC after NACI, suggesting its potential role in identifying candidates for breast cancer surgery omission trials. Accuracy of rCR for predicting pCR. Set Subgroup Sensitivity (%) Specificity (%) PPV (%) NPV (%) F1 score (%) Training N0 andKi-67>30% 81 (54/67)[71,90] 86 (12/14) [67, 99] 96 (54/56)[92, 99] 48 (12/25)[28, 68] 88 External test N0 andKi-67>30% 82 (28/34)[70, 95] 92 (11/12) [76, 99] 97 (28/29)[90, 99] 65 (11/17)[42, 87] 89 Note-Unless otherwise specified, the data are presented as percentages, with the numbers of participants in parentheses and 95% CIs in brackets; pCR = pathological complete response; PPV = positive predictive value; NPV = negative predictive value.

Article Details

Volume / Issue Vol. 43, Issue 16_suppl
Published June 01, 2025
Pages 590-590
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (20)

T

Toulsie Ramtohul

Institut Curie, Paris, Ile de France, France

D

Derek Lollivier

Institut Curie, 26 Rue D'ulm, Ile de France, France

J

Justine Spriet

Oscar Lambret, Lille, France

M

Maxime Jin

Institut Curie, Paris and St Cloud, France

L

Lounes Djerroudi

T

Thomas Gaillard

C

Claire Bonneau

Department of Surgical Oncology, Institut Curie - Saint-Cloud, University of Versailles Saint-Quentin en Yvelines, INSERM U1331, Institut des Cancers de la Femme, Saint-Cloud, France and GINECO, Paris and St Cloud, France

D

Delphine Loirat

D

Diana Bello Roufai

Medical Oncology Department, Institut Curie, Saint-Cloud, France

Y

Youlia M. Kirova

Institute Curie, Department of Radiation Oncology, Paris, France

P

Pierre Loap

Institute Curie, Department of Radiation Oncology, Paris, France

C

Caroline Malhaire

A

Anne Vincent-Salomon

F

François Clément Bidard

A

Anne Tardivon

A

Audrey Mailliez

Department of Medical Oncology, Breast Cancer Unit, Centre Oscar Lambret, Lille, France

L

Lauren Wallaert

L

Luc Ceugnart

Oscar Lambret Comprehensive Cancer Center, Lille, France

C

Caroline Nhy

Institut Curie, Paris, France

L

Luc Cabel