Lin Fei, Zhang Li-Ping, Xie Shuang-Yan, Huang Han-Ying, Chen Xiao-Yu, Jiang Tong-Chao, Guo Ling, Lin Huan-Xin
State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Department of Radiotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China.
Front Oncol. 2022 Apr 13;12:830138. doi: 10.3389/fonc.2022.830138. eCollection 2022.
To build a predictive scoring model based on simple immune and inflammatory parameters to predict postoperative survival in patients with breast cancer.
We used a brand-new immuno-inflammatory index-pan-immune-inflammation value (PIV)-to retrospectively evaluate the relationship between PIV and overall survival (OS), and based on the results of Cox regression analysis, we established a simple scoring prediction model based on several independent prognostic parameters. The predictive accuracy of the model was evaluated and independently validated.
A total of 1,312 patients were included for analysis. PIV was calculated as follows: neutrophil count (10/L) × platelet count (10/L) × monocyte count (10/L)/lymphocyte count (10/L). According to the best cutoff value of PIV, we divided the patients into two different subgroups, high PIV (PIV > 310.2) and low PIV (PIV ≤ 310.2), associated with significantly different survival outcomes (3-year OS, 80.26% vs. 86.29%, respectively; 5-year OS, 62.5% vs. 71.55%, respectively). Six independent prognostic factors were identified and used to build the scoring system, which performed well with a concordance index (C-index) of 0.759 (95% CI: 0.715-0.802); the calibration plot showed good calibration.
We have established and verified a simple scoring system for predicting prognosis, which can predict the survival of patients with operable breast cancer. This system can help clinicians implement targeted and individualized treatment strategies.
基于简单的免疫和炎症参数构建预测评分模型,以预测乳腺癌患者的术后生存率。
我们使用一种全新的免疫炎症指数——全免疫炎症值(PIV)——回顾性评估PIV与总生存期(OS)之间的关系,并根据Cox回归分析结果,基于几个独立的预后参数建立了一个简单的评分预测模型。对该模型的预测准确性进行评估并独立验证。
共纳入1312例患者进行分析。PIV的计算方法如下:中性粒细胞计数(10⁹/L)×血小板计数(10⁹/L)×单核细胞计数(10⁹/L)/淋巴细胞计数(10⁹/L)。根据PIV的最佳截断值,我们将患者分为两个不同的亚组,高PIV(PIV>310.2)和低PIV(PIV≤310.2),其生存结果有显著差异(3年总生存率分别为80.26%和86.29%;5年总生存率分别为62.5%和71.55%)。确定了六个独立的预后因素并用于构建评分系统,该系统表现良好,一致性指数(C指数)为0.759(95%CI:0.715 - 0.802);校准图显示校准良好。
我们建立并验证了一个用于预测预后的简单评分系统,该系统可以预测可手术乳腺癌患者的生存情况。该系统可帮助临床医生实施有针对性的个体化治疗策略。