Diagnosis of triple negative breast cancer based on radiomics signatures extracted from preoperative contrast-enhanced chest computed tomography
Diagnosis of triple negative breast cancer based on radiomics signatures extracted from preoperative contrast-enhanced chest computed tomography
复制标题
基于从术前对比增强胸部计算机断层扫描中提取的放射组学特征诊断三阴性乳腺癌
DOI：
10.1186/s12885-020-07053-3
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发表时间：
2020-06-22
期刊：
影响因子：
3.8
通讯作者：
Yin, Ziyi
中科院分区：
文献类型：
--
作者：
Feng, Qingliang;Hu, Qiang;Yin, Ziyi
背景资料：为探讨术前CT放射组学特征对三阴性乳腺癌（TNBC）的诊断价值，更好地指导乳腺癌患者的治疗，对我院2016年6月至2018年1月收治的890例乳腺癌患者进行分析。经手术病理证实为肿块型和浸润型乳腺癌，术前均行胸部CT增强检查。随机选择300例患者进行研究，包括100例TNBC和200例非TNBC（NTNBC）患者。其中发现组180只，验证组120只。采用免疫组化法对乳腺癌患者进行分子亚型的检测。从三维CT图像中提取放射组学特征。使用LASSO逻辑方法选择图像特征并计算放射组学评分。结果：5个影像学特征与TNBC亚型相关（P <0.001），其中5个影像学特征与TNBC亚型相关（P < 0.001）。这些图像特征基础放射学标记对TNBC具有良好的预测值，发现组中的ROC曲线下面积（AUC）分别为0.881（95%CI：0.781-0.921），验证组中的AUC分别为0.851（95%CI：0.761-0.961）。发现组的敏感性和特异性分别为0.767和0.873，验证组的敏感性和特异性分别为0.785和0.915。结论：基于术前CT的放射组学特征能够区分TNBC和NTNBC患者。它为传统的胸部对比增强CT增加了额外的价值，并有助于规划患者的临床治疗策略。
Background: To explore the diagnostic value of radiomics features of preoperative computed tomography (CT) for triple negative breast cancer (TNBC) for better treatment of patients with breast cancer.Methods: A total of 890 patients with breast cancer admitted to our hospital from June 2016 to January 2018 were analyzed. They were diagnosed by surgery and pathology to have mass and invasive breast cancer and had contrast-enhanced chest CT examination before operation. 300 patients were randomly selected for the study, including 100 TNBC and 200 non-TNBC (NTNBC) patients. Among them 180 were used in discovery group and 120 were used in validation group. The molecular subtypes of breast cancer in the patients were determined immunohistochemistrially. Radiomics features were extracted from three dimensional CT-images. The LASSO logistic method was used to select image features and calculate radiomics scores. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of radiomics scores for TNBC.Results: Five image features were found to be related to TNBC subtype (P < 0.001). These image features base-dradiomic signatures had good predictive values for TNBC with the areas under ROC curve (AUC) of 0.881 (95% CI: 0.781-0.921) in the discovery group and 0.851 (95% CI: 0.761-0.961) in the validation group, respectively. The sensitivities and specificities were 0.767, and 0.873 in the discovery group and 0.785 and 0.915 in the validation group.Conclusions: Radiomic signature based on preoperative CT is capable of distinguishing patients with TNBC and NTNBC. It adds additional value for conventional chest contrast-enhanced CT and helps plan the strategy for clinical treatment of the patients.