Seban Romain-David, Rebaud Louis, Djerroudi Lounes, Vincent-Salomon Anne, Bidard Francois-Clement, Champion Laurence, Buvat Irene
Department of Nuclear Medicine and Endocrine Oncology, Institut Curie, 92210, Saint-Cloud, France.
Laboratoire d'Imagerie Translationnelle en Oncologie, Inserm U1288, Institut Curie, PSL University, Paris Saclay University, 91400, Orsay, France.
Ann Nucl Med. 2025 Aug 12. doi: 10.1007/s12149-025-02098-5.
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.
肿瘤浸润淋巴细胞（TILs）是与早期乳腺癌（BC）的预后和治疗反应相关的关键免疫生物标志物，尤其是在三阴性亚型中。本研究旨在使用机器学习模型评估[18F]FDG PET/CT成像和常规血细胞计数（CBC）衍生的生物标志物是否可作为TILs的非侵入性替代指标。
我们回顾性分析了358例经活检证实的早期浸润性BC患者，这些患者在治疗前接受了[18F]FDG PET/CT成像。从原发性肿瘤、淋巴结和淋巴器官（脾脏和骨髓）中提取PET衍生的生物标志物。CBC衍生的生物标志物包括中性粒细胞与淋巴细胞比值（NLR）和血小板与淋巴细胞比值（PLR）。通过组织学评估TILs，并将其分类为低（0-10%）、中（11-59%）或高（≥60%）。使用Spearman等级系数评估相关性，并使用几种机器学习算法建立分类和回归模型。
肿瘤SUVmax和肿瘤SUVmean与TIL水平的相关性最高（分别为ρ = 0.29和0.30，两者p均<0.001），但TILs与PET或CBC衍生的生物标志物之间的总体关联较弱。没有CBC衍生的生物标志物显示出显著的相关性或鉴别性能。机器学习模型未能以令人满意的准确性预测TIL水平（最大平衡准确率 = 0.66）。淋巴器官指标（SLR、BLR）和CBC衍生参数并未显著提高预测价值。
在本研究中，[18F]FDG PET/CT和常规CBC衍生的生物标志物均不能可靠地预测早期BC中的TILs水平。这一观察结果是在存在潜在的扫描仪相关变异性且针对一组有限的常用PET指标的情况下得出的。未来的模型应纳入更多靶向成像方法，如免疫PET，以更高特异性地非侵入性评估免疫浸润并改善个性化治疗策略。
