Moi Sin-Hua, Lee Yi-Chen, Chuang Li-Yeh, Yuan Shyng-Shiou F, Ou-Yang Fu, Hou Ming-Feng, Yang Cheng-Hong, Chang Hsueh-Wei
1Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Cancer Cell Int. 2018 Feb 9;18:19. doi: 10.1186/s12935-018-0517-z. eCollection 2018.
Visfatin has been reported to be associated with breast cancer progression, but the interaction between the visfatin and clinicopathologic factors in breast cancer progression status requires further investigation. To address this problem, it is better to simultaneously consider multiple factors in sensitivity and specificity assays.
In this study, a dataset for 105 breast cancer patients (84 disease-free and 21 progressing) were chosen. Individual and cumulative receiver operating characteristics (ROC) were used to analyze the impact of each factor along with interaction effects.
In individual ROC analysis, only 3 of 13 factors showed better performance for area under curve (AUC), i.e., AUC > 7 for hormone therapy (HT), tissue visfatin, and lymph node (LN) metastasis. Under our proposed scoring system, the cumulative ROC analysis provides higher AUC performance (0.746-0.886) than individual ROC analysis in predicting breast cancer progression. Considering the interaction between these factors, a minimum of six factors, including HT, tissue visfatin, LN metastasis, tumor stage, age, and tumor size, were identified as being highly interactive and associated with breast cancer progression, providing potential and optimal discriminators for predicting breast cancer progression.
Taken together, the cumulative ROC analysis provides better prediction for breast cancer progression than individual ROC analysis.
已有报道称内脂素与乳腺癌进展相关，但内脂素与乳腺癌进展状态下临床病理因素之间的相互作用尚需进一步研究。为解决这一问题，在敏感性和特异性分析中最好同时考虑多个因素。
在本研究中，选取了105例乳腺癌患者的数据集（84例无病，21例病情进展）。采用个体和累积受试者工作特征曲线（ROC）分析每个因素的影响以及相互作用效应。
在个体ROC分析中，13个因素中只有3个在曲线下面积（AUC）方面表现较好，即激素治疗（HT）、组织内脂素和淋巴结（LN）转移的AUC>0.7。在我们提出的评分系统下，累积ROC分析在预测乳腺癌进展方面比个体ROC分析具有更高的AUC性能（0.746 - 0.886）。考虑到这些因素之间的相互作用，至少六个因素，包括HT、组织内脂素、LN转移、肿瘤分期、年龄和肿瘤大小，被确定为具有高度交互性且与乳腺癌进展相关，为预测乳腺癌进展提供了潜在的最佳判别指标。
综上所述，累积ROC分析比个体ROC分析能更好地预测乳腺癌进展。
