[18F]FDG PET/CT-Derived Metabolic Characteristics and Tumour-Infiltrating Lymphocytes for Predicting Axillary Response to Neoadjuvant Chemo-(Immuno)Therapy in Clinically Node-Positive Breast Cancer

Background: Accurate axillary response prediction is clinically relevant in clinically node-positive (cN+) breast cancer patients treated with neoadjuvant chemo-(immuno)therapy (NAC). This study evaluated the value of [18F]FDG PET/CT-derived metabolic characteristics and tumour-infiltrating lymphocytes (TILs) to predict axillary pathologic complete response (pCR). Second, the association between [18F]FDG PET/CT-derived metabolic characteristics and TILs was evaluated. Methods: This retrospective study included cN+ breast cancer patients treated with NAC and axillary surgery from the prospective multicentre RISAS trial (NCT02800317). Baseline [18F]FDG PET/CT-derived metabolic activity of the breast tumour, axillary lymph node metastases (ALNMs), and spleen was assessed, together with TILs measured in the breast tumour and ALNMs. TILs were classified as low (<10%) versus high (≥10%). Associations were evaluated using multivariable logistic regression and receiver operating characteristic analysis. Results: Of 135 patients, 46 (34.1%) achieved axillary pCR. Higher liver mean tumour-to-background ratio (2.7 vs. 1.9, p = 0.042), trends towards higher breast and ALNM maximum standardised uptake value (SUVmax), lower splenic SUVmax and high breast tumour TILs were associated with axillary pCR. Multivariable logistic regression combining tumour grade, subtype, maximum spleen-to-liver ratio, and breast tumour TILs yielded an apparent area under the curve of 0.80 [95%CI 0.73–0.88]. Higher ALNM SUVmax and SUVmean (10.5 vs. 6.3, p = 0.024; 6.4 vs. 3.7, p = 0.015) and lower splenic SUVmean (2.6 vs. 3.1, p = 0.035) were associated with high breast tumour TILs. Conclusions: A model combining clinicopathological, metabolic, and immune-related characteristics may be informative for predicting axillary response to NAC but did not perform better than clinicopathological characteristics alone. Future studies should evaluate whether accurate prediction models can support safe axillary treatment de-escalation.