Are [18F]FDG PET/CT imaging and cell blood count-derived biomarkers robust non-invasive surrogates for tumor-infiltrating lymphocytes in early-stage breast cancer?
Authors
Affiliations (7)
Affiliations (7)
- Department of Nuclear Medicine and Endocrine Oncology, Institut Curie, 92210, Saint-Cloud, France. [email protected].
- Laboratoire d'Imagerie Translationnelle en Oncologie, Inserm U1288, Institut Curie, PSL University, Paris Saclay University, 91400, Orsay, France. [email protected].
- Laboratoire d'Imagerie Translationnelle en Oncologie, Inserm U1288, Institut Curie, PSL University, Paris Saclay University, 91400, Orsay, France.
- Department of Pathology, Institut Curie, 75005, Paris, France.
- Department of Medical Oncology, Institut Curie, UVSQ/Paris-Saclay University, 92210, Saint-Cloud, France.
- Circulating Tumor Biomarkers Laboratory, Inserm CIC 1428, Institut Curie, 75005, Paris, France.
- Department of Nuclear Medicine and Endocrine Oncology, Institut Curie, 92210, Saint-Cloud, France.
Abstract
Tumor-infiltrating lymphocytes (TILs) are key immune biomarkers associated with prognosis and treatment response in early-stage breast cancer (BC), particularly in the triple-negative subtype. This study aimed to evaluate whether [18F]FDG PET/CT imaging and routine cell blood count (CBC)-derived biomarkers can serve as non-invasive surrogates for TILs, using machine-learning models. We retrospectively analyzed 358 patients with biopsy-proven early-stage invasive BC who underwent pre-treatment [18F]FDG PET/CT imaging. PET-derived biomarkers were extracted from the primary tumor, lymph nodes, and lymphoid organs (spleen and bone marrow). CBC-derived biomarkers included neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). TILs were assessed histologically and categorized as low (0-10%), intermediate (11-59%), or high (≥ 60%). Correlations were assessed using Spearman's rank coefficient, and classification and regression models were built using several machine-learning algorithms. Tumor SUVmax and tumor SUVmean showed the highest correlation with TIL levels (ρ = 0.29 and 0.30 respectively, p < 0.001 for both), but overall associations between TILs and PET or CBC-derived biomarkers were weak. No CBC-derived biomarker showed significant correlation or discriminative performance. Machine-learning models failed to predict TIL levels with satisfactory accuracy (maximum balanced accuracy = 0.66). Lymphoid organ metrics (SLR, BLR) and CBC-derived parameters did not significantly enhance predictive value. In this study, neither [18F]FDG PET/CT nor routine CBC-derived biomarkers reliably predict TILs levels in early-stage BC. This observation was made in presence of potential scanner-related variability and for a restricted set of usual PET metrics. Future models should incorporate more targeted imaging approaches, such as immunoPET, to non-invasively assess immune infiltration with higher specificity and improve personalized treatment strategies.