Use of Pretreatment Perfusion MRI–based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy

Background Neoadjuvant chemoimmunotherapy (NACI) has significantly increased the rate of pathologic complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC), although predictors of response to this regimen have not been identified. Purpose To investigate pretreatment perfusion MRI-based radiomics as a predictive marker for pCR in patients with TNBC undergoing NACI. Materials and Methods This prospective study enrolled women with early-stage TNBC who underwent NACI at two different centers from August 2021 to July 2023. Pretreatment dynamic contrast-enhanced MRI scans obtained using scanners from multiple vendors were analyzed using the Tofts model to segment tumors and analyze pharmacokinetic parameters. Radiomics features were extracted from the rate constant for contrast agent plasma-to-interstitial transfer (or <i>K</i><sup>trans</sup>), volume fraction of extravascular and extracellular space (<i>V</i><sub>e</sub>), and maximum contrast agent uptake rate (Slope<sub>max</sub>) maps and analyzed using unsupervised correlation and least absolute shrinkage and selector operator, or LASSO, to develop a radiomics score. Score effectiveness was assessed using the area under the receiver operating characteristic curve (AUC), and multivariable logistic regression was used to develop a multimodal nomogram for enhanced prediction. The discrimination, calibration, and clinical utility of the nomogram were evaluated in an external test set. Results The training set included 112 female participants from center 1 (mean age, 52 years ± 11 [SD]), and the external test set included 83 female participants from center 2 (mean age, 47 years ± 11). The radiomics score demonstrated an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, grade, and Ki-67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) in the test set. Associations were found between higher radiomics score (>0.25) and tumor size (<i>P</i> < .001), washout enhancement (<i>P</i> = .01), androgen receptor expression (<i>P</i> = .009), and programmed death ligand 1 expression (<i>P</i> = .01), demonstrating a correlation with tumor immune environment in participants with TNBC. Conclusion A radiomics score derived from pharmacokinetic parameters at pretreatment dynamic contrast-enhanced MRI exhibited good performance for predicting pCR in participants with TNBC undergoing NACI, and could potentially be used to enhance clinical decision making. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Rauch in this issue.

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Journal
Radiology
Published
2024-09-01
DOI
https://doi.org/10.1148/radiol.240575
Citations
30
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
Field-Weighted Citation Impact
12.13
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article

Use of Pretreatment Perfusion MRI–based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy

Toulsie Ramtohul, François‐Clément Bidard, A. Tardivon, Emmanuelle Menet et al.
30 citations
Radiology
Radiomics and Machine Learning in Medical Imaging
12.13
article

Use of Pretreatment Perfusion MRI–based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy

Toulsie Ramtohul, François‐Clément Bidard, A. Tardivon, Emmanuelle Menet, Enora Laas, Anne Vincent‐Salomon, Caroline Malhaire, Hervé J. Brisse, Claire Bonneau, Luc Cabel, A. Langer, Diana Bello-Roufai, Emanuela Romano, Juliette Sauge, Victoire Lepagney, Fatima Mechta-Grigoriou, Maxime Jin
article en
30 citations

Abstract

Background Neoadjuvant chemoimmunotherapy (NACI) has significantly increased the rate of pathologic complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC), although predictors of response to this regimen have not been identified. Purpose To investigate pretreatment perfusion MRI-based radiomics as a predictive marker for pCR in patients with TNBC undergoing NACI. Materials and Methods This prospective study enrolled women with early-stage TNBC who underwent NACI at two different centers from August 2021 to July 2023. Pretreatment dynamic contrast-enhanced MRI scans obtained using scanners from multiple vendors were analyzed using the Tofts model to segment tumors and analyze pharmacokinetic parameters. Radiomics features were extracted from the rate constant for contrast agent plasma-to-interstitial transfer (or <i>K</i><sup>trans</sup>), volume fraction of extravascular and extracellular space (<i>V</i><sub>e</sub>), and maximum contrast agent uptake rate (Slope<sub>max</sub>) maps and analyzed using unsupervised correlation and least absolute shrinkage and selector operator, or LASSO, to develop a radiomics score. Score effectiveness was assessed using the area under the receiver operating characteristic curve (AUC), and multivariable logistic regression was used to develop a multimodal nomogram for enhanced prediction. The discrimination, calibration, and clinical utility of the nomogram were evaluated in an external test set. Results The training set included 112 female participants from center 1 (mean age, 52 years ± 11 [SD]), and the external test set included 83 female participants from center 2 (mean age, 47 years ± 11). The radiomics score demonstrated an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, grade, and Ki-67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) in the test set. Associations were found between higher radiomics score (>0.25) and tumor size (<i>P</i> < .001), washout enhancement (<i>P</i> = .01), androgen receptor expression (<i>P</i> = .009), and programmed death ligand 1 expression (<i>P</i> = .01), demonstrating a correlation with tumor immune environment in participants with TNBC. Conclusion A radiomics score derived from pharmacokinetic parameters at pretreatment dynamic contrast-enhanced MRI exhibited good performance for predicting pCR in participants with TNBC undergoing NACI, and could potentially be used to enhance clinical decision making. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Rauch in this issue.

RadiologyVol. 312(3)
Inserm (FR), Université de Versailles Saint-Quentin-en-Yvelines (FR), Université Paris Sciences et Lettres (FR), Institut Curie (FR)
Good health and well-being
Openalex Percentile: Top 1%
Radiomics and Machine Learning in Medical Imaging
12.13
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