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. 2025 Apr 7;17(7):1249.
doi: 10.3390/cancers17071249.

FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

David Groheux  1   2 , Loïc Ferrer  3 , Jennifer Vargas  3 , Antoine Martineau  1 , Adrien Borgel  2   4 , Luis Teixeira  2   5 , Philippe Menu  6 , Philippe Bertheau  7 , Olivier Gallinato  3 , Thierry Colin  3 , Jacqueline Lehmann-Che  2   4
Affiliations

FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

David Groheux et al. Cancers (Basel). .

Abstract

Purpose: Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. Neoadjuvant chemotherapy (NAC) is often given before surgery, and achieving a pathological complete response (pCR) has been associated with patient outcomes. There is thus strong clinical interest in the ability to accurately predict pCR status using baseline data. Materials and Methods: A cohort of 57 TNBC patients who underwent FDG-PET/CT before NAC was analyzed to develop a machine learning (ML) algorithm predictive of pCR. A total of 241 predictors were collected for each patient: 11 clinical features, 11 histopathological features, 13 genomic features, and 206 PET features, including 195 radiomic features. The optimization criterion was the area under the ROC curve (AUC). Event-free survival (EFS) was estimated using the Kaplan-Meier method. Results: The best ML algorithm reached an AUC of 0.82. The features with the highest weight in the algorithm were a mix of PET (including radiomics), histopathological, genomic, and clinical features, highlighting the importance of truly multimodal analysis. Patients with predicted pCR tended to have a longer EFS than patients with predicted non-pCR, even though this difference was not significant, probably due to the small sample size and few events observed (p = 0.09). Conclusions: This study suggests that ML applied to baseline multimodal data can help predict pCR status after NAC for TNBC patients and may identify correlations with long-term outcomes. Patients predicted as non-pCR may benefit from concomitant treatment with immunotherapy or dose intensification.

Keywords: FDG-PET/CT; artificial intelligence; machine learning; metabolic response; neoadjuvant chemotherapy; pCR; prognosis; radiomics; triple-negative breast cancer.

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Conflict of interest statement

Authors Loïc Ferrer, Jennifer Vargas, Philippe Menu, Olivier Gallinato and Thierry Colin were employed by the company SOPHiA GENETICS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Example of 3D segmentation using a semi-automatic method based on 42% of the SUVmax. The green contour delineates the segmented primary breast tumor lesion.
Figure 2
Figure 2
Outline of the multimodal analysis workflow for model development and evaluation.
Figure 3
Figure 3
Diagram representing the coefficients of the support vector machine ML model.
Figure 4
Figure 4
Kaplan–Meier estimates of event-free survival stratified according to predicted pathological complete response status by the support vector machine model, with p-values estimated by the log-rank test.
See this image and copyright information in PMC

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