用于预测接受新辅助化疗的可手术II期和III期三阴性乳腺癌患者病理完全缓解的综合临床模型
An Integrative Clinical Model for the Prediction of Pathological Complete Response in Patients with Operable Stage II and Stage III Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
作者信息
Chung Wai-Shan, Chen Shin-Cheh, Ko Tai-Ming, Lin Yung-Chang, Lin Sheng-Hsuan, Lo Yung-Feng, Tseng Shu-Chi, Yu Chi-Chang
机构信息
Department of Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan.
College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.
出版信息
Cancers (Basel). 2022 Aug 28;14(17):4170. doi: 10.3390/cancers14174170.
Triple-negative breast cancer (TNBC) is treated with neoadjuvant chemotherapy (NAC). The response to NAC, particularly the probability of a complete pathological response (pCR), guides the surgical approach and adjuvant therapy. We developed a prediction model using a nomogram integrating blood tests and pre-treatment ultrasound findings for predicting pCR in patients with stage II or III operable TNBC receiving NAC. Clinical data before and after the first cycle of NAC collected from patients between 2012 and 2019 were analyzed using univariate and multivariate analyses to identify correlations with pCR. The coefficients of the significant parameters were calculated using logistic regression, and a nomogram was developed based on the logistic model to predict the probability of pCR. Eighty-eight patients were included. Five parameters correlated with the probability of pCR, including the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte (PLR) ratio, percentage change in PLR, presence of echogenic halo, and tumor height-to-width ratio. The discrimination performance of the nomogram was indicated by an area under the curve of 87.7%, and internal validation showed that the chi-square value of the Hosmer-Lemeshow test was 7.67 ( = 0.363). Thus, the integrative prediction model using clinical data can predict the probability of pCR in patients with TNBC receiving NAC.
三阴性乳腺癌(TNBC)采用新辅助化疗(NAC)进行治疗。对NAC的反应,尤其是完全病理缓解(pCR)的概率,指导着手术方式和辅助治疗。我们开发了一种预测模型,使用一种整合血液检测和治疗前超声检查结果的列线图,来预测接受NAC治疗的II期或III期可手术TNBC患者的pCR。对2012年至2019年间从患者收集的NAC第一个周期前后的临床数据进行单因素和多因素分析,以确定与pCR的相关性。使用逻辑回归计算显著参数的系数,并基于逻辑模型开发列线图以预测pCR的概率。纳入了88例患者。五个参数与pCR概率相关,包括中性粒细胞与淋巴细胞比值、血小板与淋巴细胞(PLR)比值、PLR的变化百分比、有无回声晕以及肿瘤高宽比。列线图的辨别性能通过曲线下面积为87.7%来表示,内部验证显示Hosmer-Lemeshow检验的卡方值为7.67(P = 0.363)。因此,使用临床数据的综合预测模型可以预测接受NAC治疗的TNBC患者的pCR概率。
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