METHODS: A retrospective analysis was performed on 418 patients who underwent radical surgical resection and had postoperative pathological confirmation of ICC between January 2016 and January 2020 at three medical centers. The cohort was divided into a training set (n = 272) and a validation set (n = 146). The prognostic significance of 16 relevant markers was assessed, and the TIIN score was derived using LASSO regression. Subsequently, the TIIN-nomogram models for OS and RFS were developed based on the TIIN score and the results of multivariate analysis. The predictive performance of the TIIN-nomogram models was evaluated using ROC survival curves, calibration curves, and clinical decision curve analysis (DCA).
RESULTS: The TIIN score, derived from albumin-to-alkaline phosphatase ratio (AAPR), albumin-globulin ratio (AGR), monocyte-to-lymphocyte ratio (MLR), and tumor burden score (TBS), effectively categorized patients into high-risk and low-risk groups using the optimal cutoff value. Compared to individual metrics, the TIIN score demonstrated superior predictive value for both OS and RFS. Furthermore, the TIIN score exhibited strong associations with clinical indicators including obstructive jaundice, CEA, CA19-9, Child-pugh grade, perineural invasion, and 8th edition AJCC N stage. Univariate and multivariate analysis confirmed the TIIN score as an independent risk factor for postoperative OS and RFS in ICC patients (p < 0.05). Notably, the TIIN-nomogram models for OS and RFS, constructed based on the multivariate analysis and incorporating the TIIN score, demonstrated excellent predictive ability for postoperative survival in ICC patients.
CONCLUSIONS: The development and validation of the TIIN score, a comprehensive composite index incorporating tumor morphology, immune function, inflammatory level, and nutritional status, significantly contribute to the prognostic assessment of ICC patients. Furthermore, the successful application of the TIIN-nomogram prediction model underscores its potential as a valuable tool in guiding individualized treatment strategies for ICC patients. These findings emphasize the importance of personalized approaches in improving the clinical management and outcomes of ICC.
方法:回顾性分析2016年1月至2020年1月在三个医疗中心接受根治性手术切除并术后病理证实ICC的418例患者。将队列分为训练集(n=272)和验证集(n=146)。评估了16个相关标志物的预后意义,使用LASSO回归得出TIN评分。随后,OS和RFS的TIIN列线图模型是根据TIIN评分和多变量分析结果建立的.使用ROC存活曲线评估TIN-列线图模型的预测性能,校正曲线,和临床决策曲线分析(DCA)。
结果:TIN评分,来自白蛋白与碱性磷酸酶比(AAPR),白蛋白-球蛋白比(AGR),单核细胞与淋巴细胞比率(MLR),和肿瘤负荷评分(TBS),使用最佳临界值将患者有效地分为高风险和低风险组.与单个指标相比,TIIN评分对OS和RFS均具有较好的预测价值.此外,TIN评分与包括梗阻性黄疸在内的临床指标密切相关,CEA,CA19-9,儿童级,神经周浸润,和第8版AJCCN阶段。单因素和多因素分析证实TIIN评分是ICC患者术后OS和RFS的独立危险因素(p<0.05)。值得注意的是,用于OS和RFS的TIN列线图模型,基于多变量分析并结合TIN评分构建,表现出对ICC患者术后生存的良好预测能力。
结论:TIN评分的开发和验证,纳入肿瘤形态学的综合综合指数,免疫功能,炎症水平,和营养状况,显著有助于ICC患者的预后评估。此外,TIIN-列线图预测模型的成功应用凸显了其作为指导ICC患者个体化治疗策略的有价值工具的潜力.这些发现强调了个性化方法在改善ICC临床管理和结果方面的重要性。