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一种预测新辅助化疗的三阴性乳腺癌患者无病生存期的联合列线图模型

A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy.

作者信息

Xia Bingqing, Wang He, Wang Zhe, Qian Zhaoxia, Xiao Qin, Liu Yin, Shao Zhimin, Zhou Shuling, Chai Weimin, You Chao, Gu Yajia

机构信息

International Peace Maternity and Child Health Hospital, Shanghai, China.

Shanghai Cancer Center, Fudan University, Shanghai, China.

出版信息

Front Genet. 2021 Nov 12;12:783513. doi: 10.3389/fgene.2021.783513. eCollection 2021.

Abstract

To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS. Patients ( = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital ( = 109) were used as the training group, and patients from the other hospital ( = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation. The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761-0.907) and 0.868 (95% CI, 0.787-949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709-0.743; validation group: C-index = 0.774,95% CI = 0.743-0.805). The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.

摘要

为了研究三阴性乳腺癌(TNBC)患者在新辅助化疗(NAC)前获得的动态对比增强磁共振成像(DCE-MRI)图像的放射组学特征(Rad评分)是否与无病生存期(DFS)相关。开发并验证基于放射组学特征、MRI表现和临床病理变量的直观列线图,以预测DFS。回顾性分析了2011年8月至2017年5月期间在两家医院接受NAC治疗且经病理活检确诊为TNBC的150例患者,随访至2020年5月。将其中一家医院的109例患者作为训练组,另一家医院的41例患者作为验证组。在1.5T MRI T1加权增强图像上,使用3D Slicer软件绘制肿瘤全容积的感兴趣区(ROI)。确定预测DFS的放射组学特征,确定Rad评分的最佳临界值,并分析DFS与放射组学特征、MRI表现和临床病理变量之间的关联。开发并验证用于个性化DFS估计的列线图。中位随访时间为53.5个月,150例患者中有45例(30.0%)出现复发和转移。Rad评分的最佳临界值为0.2528,该临界值将训练组患者分为DFS的高风险和低风险组(<0.001),并在外部验证组中得到验证。多因素分析确定了三个独立指标:多灶/中心性疾病状态、病理完全缓解(pCR)状态和Rad评分。基于这些因素的列线图显示出鉴别能力,该模型在训练组和验证组中的C指数分别为0.834(95%CI,0.761-0.907)和0.868(95%CI,0.787-0.949),优于临床放射学列线图(训练组:C指数=0.726,95%CI=0.709-0.743;验证组:C指数=0.774,95%CI=0.743-0.805)。术前MRI特征得出的Rad评分是TNBC患者接受NAC治疗后DFS预测的独立生物标志物,联合放射组学列线图改善了个性化DFS估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd81/8632946/17c3db3260a8/fgene-12-783513-g001.jpg

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