Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Nangang District, Harbin, 150081, Heilongjiang, China.
Clin Transl Oncol. 2024 Jun;26(6):1467-1479. doi: 10.1007/s12094-023-03371-7. Epub 2024 Jan 8.
Systemic immune-inflammatory markers have a certain predictive role in pathological complete response (pCR) after neoadjuvant treatment (NAT) in breast cancer. However, there is a lack of research exploring the predictive value of markers after treatment.
This retrospective study collected data from 1994 breast cancer patients who underwent NAT. Relevant clinical and pathological characteristics were included, and pre- and post-treatment complete blood cell counts were evaluated to calculate four systemic immune-inflammatory markers: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). The optimal cutoff values for these markers were determined using ROC curves, and patients were classified into high-value and low-value groups based on these cutoff values. Univariate and multivariate logistic regression analyses were conducted to analyze factors influencing pCR. The factors with independent predictive value were used to construct a nomogram.
After NAT, 383 (19.2%) patients achieved pCR. The area under the ROC curve is generally larger for post-treatment markers compared to pre-treatment markers. Pre-treatment NLR and PLR, as well as post-treatment LMR and SII, were identified as independent predictive factors for pCR, along with Ki-67, clinical tumor stage, clinical lymph node stage, molecular subtype, and clinical response. Higher pre-NLR (OR = 1.320; 95% CI 1.016-1.716; P = 0.038), pre-PLR (OR = 1.474; 95% CI 1.058-2.052; P = 0.022), post-LMR (OR = 1.532; 95% CI 1.175-1.996; P = 0.002), and lower post-SII (OR = 0.596; 95% CI 0.429-0.827; P = 0.002) are associated with a higher likelihood of achieving pCR. The established nomogram had a good predictive performance with an area under the ROC curve of 0.754 (95% CI 0.674-0.835).
Both pre- and post-treatment systemic immune-inflammatory markers have a significant predictive role in achieving pCR after NAT in breast cancer patients. Indeed, it is possible that post-treatment markers have stronger predictive ability compared to pre-treatment markers.
系统免疫炎症标志物在新辅助治疗（NAT）后乳腺癌病理完全缓解（pCR）中具有一定的预测作用。然而，目前缺乏研究探索治疗后标志物的预测价值。
本回顾性研究纳入了 1994 例接受 NAT 的乳腺癌患者。纳入了相关的临床和病理特征，并评估了治疗前后的全血细胞计数，以计算四项系统免疫炎症标志物：中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、淋巴细胞与单核细胞比值（LMR）和系统免疫炎症指数（SII）。使用 ROC 曲线确定这些标志物的最佳截断值，并根据这些截断值将患者分为高值组和低值组。使用单因素和多因素逻辑回归分析来分析影响 pCR 的因素。使用具有独立预测价值的因素构建列线图。
NAT 后，383 例（19.2%）患者达到了 pCR。与治疗前标志物相比，治疗后标志物的 ROC 曲线下面积普遍更大。治疗前 NLR 和 PLR 以及治疗后 LMR 和 SII 被确定为 pCR 的独立预测因素，此外还有 Ki-67、临床肿瘤分期、临床淋巴结分期、分子亚型和临床反应。较高的治疗前 NLR（OR=1.320；95%CI 1.016-1.716；P=0.038）、治疗前 PLR（OR=1.474；95%CI 1.058-2.052；P=0.022）、治疗后 LMR（OR=1.532；95%CI 1.175-1.996；P=0.002）和较低的治疗后 SII（OR=0.596；95%CI 0.429-0.827；P=0.002）与 pCR 发生率更高相关。建立的列线图具有良好的预测性能，ROC 曲线下面积为 0.754（95%CI 0.674-0.835）。
治疗前和治疗后的系统免疫炎症标志物均对乳腺癌患者 NAT 后获得 pCR 具有显著的预测作用。实际上，治疗后标志物的预测能力可能强于治疗前标志物。
