乳腺浸润性微乳头状癌预后影响因素分析与模型构建

目的:对比分析中国和美国乳腺浸润性微乳头状癌(invasive micropapillary carcinoma,IMPC)的病理特征和预后影响因素,并预测乳腺IMPC患者预后.方法:回顾性分析2006年7月至2015年7月83例于中国人民解放军空军军医大学西京医院收治的乳腺IMPC患者临床资料,收集2010年3月至2...

Analysis of prognostic factors and construction of prognostic models for invasive micropapillary carcinoma of the breast

  Objective  To predict the prognosis of patients with invasive micropapillary carcinoma (IMPC) of the breast (IMPC-B), and compare the pathological characteristics and prognostic factors of patients with IMPC-B in China and the United States.  Methods  This retrospective analysis included 83 cases of IMPC-B diagnosed between July 2006 and July 2015 at Xijing Hospital, The Fourth Military Medical University, China. In addition, the clinical data of 415 patients diagnosed with IMPC-B between March 2010 and March 2015 were obtained from the Surveillance, Epidemiology, and End-Results (SEER) database of the American National Cancer Institute. The clinical and pathological characteristics of the Chinese and American patients were compared, and independent risk factors for overall survival (OS) and cancer-specific survival (CSS) were analyzed using univariate and multivariate Cox proportional regression models and the Fine-Gray competing risk model. Based on the results, nomograms were developed for predicting OS and CSS. The effectiveness of the nomogram models was evaluated by internal and external validation, and the clinical benefits and application value of the models were evaluated using clinical decision curve analysis.  Results  There were significant differences in age, tumor site, operation method, tumor status (for example, whether it was the primary tumor), and T-stage between the modeling set and validation set (P<0.05). Univariate and multivariate analyses using Cox proportional regression models and the Fine-Gray competing risk model showed that age, N-stage, M-stage, and molecular type were independent prognostic factors in patients with IMPC-B (P<0.05). Nomogram models were developed using the above variables. For the models for OS and CSS, the C-indexes of the modeling set were 0.85 and 0.79, those of the validation set were 0.72 and 0.70, and those for internal validation were 0.81 and 0.74, respectively. The calibration curve analysis showed that the model predictions for OS and CSS were consistent with the actual values, and the clinical decision curve analysis showed that the models had clinical utility.  Conclusions  The developed nomograms could accurately predict the prognosis of patients with IMPC-B, providinga scientific basis for clinical diagnosis and treatment.