FDG-PET/CT and multimodal machine learning model prediction of pathological complete response to neoadjuvant chemotherapy in triple-negative breast cancer

Background: 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 pathological complete response (pCR) has been associated with patient outcomes. There is thus high clinical interest in the ability to predict pCR status using baseline data accurately. Methods: A cohort of 57 TNBC patients who had 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 histo-pathological features, 13 genomic features, and 206 PET features, including 195 radiomics 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 reaching an AUC of 0.82. The features with the highest weight in the algorithm were a mix of PET (including radiomics), histo-pathological, genomics, and clinical features, highlighting the importance of truly multimodal analysis. Patients with predicted pCR tended to have better EFS than patients with predicted non-pCR, even though this difference was not significant probably due to small sample size and few events observed (P=0.09). Conclusion: The study suggests that ML applied to baseline multimodal data can help predict pCR status after NAC for TNBC patients and seem correlated to long-term outcomes. Patients that would be predicted as non-pCR could benefit from concomitant treatment with immunotherapy or dose intensification.