Ma Wenjuan, Wang Xin, Xu Guijun, Liu Zheng, Yin Zhuming, Xu Yao, Wu Haixiao, Baklaushev Vladimir P, Peltzer Karl, Sun Henian, Kharchenko Natalia V, Qi Lisha, Mao Min, Li Yanbo, Liu Peifang, Chekhonin Vladimir P, Zhang Chao
Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China.
Department of Epidemiology and Biostatistics, First Affiliated Hospital, Army Medical University, Chongqing 400038, China.
Aging (Albany NY). 2020 Sep 28;12(18):18151-18162. doi: 10.18632/aging.103630.
This study aimed to develop a model that fused multiple features (multi-feature fusion model) for predicting metachronous distant metastasis (DM) in breast cancer (BC) based on clinicopathological characteristics and magnetic resonance imaging (MRI). A nomogram based on clinicopathological features (clinicopathological-feature model) and a nomogram based on the multi-feature fusion model were constructed based on BC patients with DM (n=67) and matched patients (n=134) without DM. DM was diagnosed on average (17.31±13.12) months after diagnosis. The clinicopathological-feature model included seven features: reproductive history, lymph node metastasis, estrogen receptor status, progesterone receptor status, CA153, CEA, and endocrine therapy. The multi-feature fusion model included the same features and an additional three MRI features (multiple masses, fat-saturated T2WI signal, and mass size). The multi-feature fusion model was relatively better at predicting DM. The sensitivity, specificity, diagnostic accuracy and AUC of the multi-feature fusion model were 0.746 (95% CI: 0.623-0.841), 0.806 (0.727-0.867), 0.786 (0.723-0.841), and 0.854 (0.798-0.911), respectively. Both internal and external validations suggested good generalizability of the multi-feature fusion model to the clinic. The incorporation of MRI factors significantly improved the specificity and sensitivity of the nomogram. The constructed multi-feature fusion nomogram may guide DM screening and the implementation of prophylactic treatment for BC.
本研究旨在基于临床病理特征和磁共振成像（MRI）开发一种融合多种特征的模型（多特征融合模型），用于预测乳腺癌（BC）的异时性远处转移（DM）。基于有DM的BC患者（n = 67）和匹配的无DM患者（n = 134）构建了基于临床病理特征的列线图（临床病理特征模型）和基于多特征融合模型的列线图。DM平均在诊断后（17.31±13.12）个月被诊断出来。临床病理特征模型包括七个特征：生育史、淋巴结转移、雌激素受体状态、孕激素受体状态、CA153、CEA和内分泌治疗。多特征融合模型包括相同的特征以及另外三个MRI特征（多个肿块、脂肪抑制T2WI信号和肿块大小）。多特征融合模型在预测DM方面相对更好。多特征融合模型的敏感性、特异性、诊断准确性和AUC分别为0.746（95%CI：0.623 - 0.841）、0.806（0.727 - 0.867）、0.786（0.723 - 0.841）和0.854（0.798 - 0.911）。内部和外部验证均表明多特征融合模型在临床中具有良好的通用性。纳入MRI因素显著提高了列线图的特异性和敏感性。构建的多特征融合列线图可能指导BC的DM筛查和预防性治疗的实施。
