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Abstract：
BACKGROUND: Parotid gland carcinoma (PGC) is a rare malignant tumor. The purpose of this study was to investigate the role of immune-inflammatory-nutrition indicators and age-adjusted Charlson comorbidity index score (ACCI) of PGC and develop the nomogram model for predicting prognosis.
METHODS: All patients diagnosed with PGC in two tertiary hospitals, treated with surgical resection, from March 2012 to June 2018 were obtained. Potential prognostic factors were identified by univariate and multivariate Cox regression analyses. The nomogram models were established based on these identified independent prognostic factors. The performance of the developed prognostic model was estimated by related indexes and plots.
RESULTS: The study population consisted of 344 patients with PGC who underwent surgical resection, 285 patients without smoking (82.8%), and 225 patients (65.4%) with mucoepidermoid carcinoma, with a median age of 50.0 years. American Joint Committee on Cancer (AJCC) stage (p < 0.001), pathology (p = 0.019), tumor location (p < 0.001), extranodal extension (ENE) (p < 0.001), systemic immune-inflammation index (SII) (p = 0.004), prognostic nutrition index (PNI) (p = 0.003), ACCI (p < 0.001), and Glasgow prognostic Score (GPS) (p = 0.001) were independent indicators for disease free survival (DFS). Additionally, the independent prognostic factors for overall survival (OS) including AJCC stage (p = 0.015), pathology (p = 0.004), tumor location (p < 0.001), perineural invasion (p = 0.009), ENE (p < 0.001), systemic immune-inflammation index (SII) (p = 0.001), PNI (p = 0.001), ACCI (p = 0.003), and GPS (p = 0.033). The nomogram models for predicting DFS and OS in PGC patients were generated based on these independent risk factors. All nomogram models show good discriminative capability with area under curves (AUCs) over 0.8 (DFS 0.802, and OS 0.825, respectively). Decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) show good clinical net benefit of the two nomograms in both training and validation cohorts. Kaplan-Meier survival analyses showed superior discrimination of DFS and OS in the new risk stratification system compared with the AJCC stage system. Finally, postoperative patients with PGC who underwent adjuvant radiotherapy had a better prognosis in the high-, and medium-risk subgroups (p < 0.05), but not for the low-risk subgroup.
CONCLUSIONS: The immune-inflammatory-nutrition indicators and ACCI played an important role in both DFS and OS of PGC patients. Adjuvant radiotherapy had no benefit in the low-risk subgroup for PGC patients who underwent surgical resection. The newly established nomogram models perform well and can provide an individualized prognostic reference, which may be helpful for patients and surgeons in proper follow-up strategies.
METHODS: All patients diagnosed with PGC in two tertiary hospitals, treated with surgical resection, from March 2012 to June 2018 were obtained. Potential prognostic factors were identified by univariate and multivariate Cox regression analyses. The nomogram models were established based on these identified independent prognostic factors. The performance of the developed prognostic model was estimated by related indexes and plots.
RESULTS: The study population consisted of 344 patients with PGC who underwent surgical resection, 285 patients without smoking (82.8%), and 225 patients (65.4%) with mucoepidermoid carcinoma, with a median age of 50.0 years. American Joint Committee on Cancer (AJCC) stage (p < 0.001), pathology (p = 0.019), tumor location (p < 0.001), extranodal extension (ENE) (p < 0.001), systemic immune-inflammation index (SII) (p = 0.004), prognostic nutrition index (PNI) (p = 0.003), ACCI (p < 0.001), and Glasgow prognostic Score (GPS) (p = 0.001) were independent indicators for disease free survival (DFS). Additionally, the independent prognostic factors for overall survival (OS) including AJCC stage (p = 0.015), pathology (p = 0.004), tumor location (p < 0.001), perineural invasion (p = 0.009), ENE (p < 0.001), systemic immune-inflammation index (SII) (p = 0.001), PNI (p = 0.001), ACCI (p = 0.003), and GPS (p = 0.033). The nomogram models for predicting DFS and OS in PGC patients were generated based on these independent risk factors. All nomogram models show good discriminative capability with area under curves (AUCs) over 0.8 (DFS 0.802, and OS 0.825, respectively). Decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) show good clinical net benefit of the two nomograms in both training and validation cohorts. Kaplan-Meier survival analyses showed superior discrimination of DFS and OS in the new risk stratification system compared with the AJCC stage system. Finally, postoperative patients with PGC who underwent adjuvant radiotherapy had a better prognosis in the high-, and medium-risk subgroups (p < 0.05), but not for the low-risk subgroup.
CONCLUSIONS: The immune-inflammatory-nutrition indicators and ACCI played an important role in both DFS and OS of PGC patients. Adjuvant radiotherapy had no benefit in the low-risk subgroup for PGC patients who underwent surgical resection. The newly established nomogram models perform well and can provide an individualized prognostic reference, which may be helpful for patients and surgeons in proper follow-up strategies.
摘要：
背景：腮腺癌（PGC）是一种罕见的恶性肿瘤。目的探讨PGC免疫-炎症-营养指标和年龄调整后的Charlson合并症指数评分(ACCI)的作用,建立预测预后的列线图模型。
方法：在两家三级医院诊断为PGC的所有患者，手术切除治疗，从2012年3月至2018年6月获得。通过单因素和多因素Cox回归分析确定潜在的预后因素。根据这些确定的独立预后因素建立列线图模型。通过相关指标和图估计开发的预后模型的性能。
结果：研究人群包括344例接受手术切除的PGC患者，285例无吸烟患者（82.8%），225例（65.4%）粘液表皮样癌，平均年龄为50.0岁。美国癌症联合委员会（AJCC）阶段（p<0.001），病理学（p=0.019），肿瘤位置（p<0.001），结外延伸（ENE）（p<0.001），全身免疫炎症指数（SII）（p=0.004），预后营养指数（PNI）（p=0.003），ACCI（p<0.001），格拉斯哥预后评分（GPS）（p=0.001）是无病生存（DFS）的独立指标。此外,总生存期（OS）的独立预后因素包括AJCC分期（p=0.015），病理学（p=0.004），肿瘤位置（p<0.001），神经周浸润（p=0.009），ENE（p<0.001），全身免疫炎症指数（SII）（p=0.001），PNI（p=0.001），ACCI（p=0.003），和GPS（p=0.033）。根据这些独立的危险因素，生成预测PGC患者DFS和OS的列线图模型。所有列线图模型均显示出良好的判别能力，曲线下面积（AUC）超过0.8（分别为DFS0.802和OS0.825）。决策曲线分析(DCA)综合歧视改进(IDI)，和净重新分类指数（NRI）在训练和验证队列中显示两个列线图的良好临床净效益。Kaplan-Meier生存分析显示，与AJCC分期系统相比，新的风险分层系统中DFS和OS的区分度更高。最后,术后接受辅助放疗的PGC患者预后较好,和中等风险亚组(p<0.05)，但不是针对低风险亚组。
结论：免疫炎症营养指标和ACCI在PGC患者的DFS和OS中起重要作用。对于接受手术切除的PGC患者，辅助放疗在低风险亚组中没有益处。新建立的列线图模型表现良好，可以提供个性化的预后参考，这可能有助于患者和外科医生采取适当的随访策略。
方法：在两家三级医院诊断为PGC的所有患者，手术切除治疗，从2012年3月至2018年6月获得。通过单因素和多因素Cox回归分析确定潜在的预后因素。根据这些确定的独立预后因素建立列线图模型。通过相关指标和图估计开发的预后模型的性能。
结果：研究人群包括344例接受手术切除的PGC患者，285例无吸烟患者（82.8%），225例（65.4%）粘液表皮样癌，平均年龄为50.0岁。美国癌症联合委员会（AJCC）阶段（p<0.001），病理学（p=0.019），肿瘤位置（p<0.001），结外延伸（ENE）（p<0.001），全身免疫炎症指数（SII）（p=0.004），预后营养指数（PNI）（p=0.003），ACCI（p<0.001），格拉斯哥预后评分（GPS）（p=0.001）是无病生存（DFS）的独立指标。此外,总生存期（OS）的独立预后因素包括AJCC分期（p=0.015），病理学（p=0.004），肿瘤位置（p<0.001），神经周浸润（p=0.009），ENE（p<0.001），全身免疫炎症指数（SII）（p=0.001），PNI（p=0.001），ACCI（p=0.003），和GPS（p=0.033）。根据这些独立的危险因素，生成预测PGC患者DFS和OS的列线图模型。所有列线图模型均显示出良好的判别能力，曲线下面积（AUC）超过0.8（分别为DFS0.802和OS0.825）。决策曲线分析(DCA)综合歧视改进(IDI)，和净重新分类指数（NRI）在训练和验证队列中显示两个列线图的良好临床净效益。Kaplan-Meier生存分析显示，与AJCC分期系统相比，新的风险分层系统中DFS和OS的区分度更高。最后,术后接受辅助放疗的PGC患者预后较好,和中等风险亚组(p<0.05)，但不是针对低风险亚组。
结论：免疫炎症营养指标和ACCI在PGC患者的DFS和OS中起重要作用。对于接受手术切除的PGC患者，辅助放疗在低风险亚组中没有益处。新建立的列线图模型表现良好，可以提供个性化的预后参考，这可能有助于患者和外科医生采取适当的随访策略。
