Distant metastasis prediction via a multi-feature fusion model in breast cancer.
Distant metastasis prediction via a multi-feature fusion model in breast cancer.
复制标题
通过多特征融合模型预测乳腺癌远处转移
DOI：
10.18632/aging.103630
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发表时间：
2020-09-28
期刊：
影响因子：
--
通讯作者：
Zhang C
中科院分区：
文献类型：
--
作者：
Ma W;Wang X;Xu G;Liu Z;Yin Z;Xu Y;Wu H;Baklaushev VP;Peltzer K;Sun H;Kharchenko NV;Qi L;Mao M;Li Y;Liu P;Chekhonin VP;Zhang C
本研究旨在建立一个基于临床病理特征和磁共振成像（MRI）的多特征融合模型（多特征融合模型）来预测乳腺癌异时性远处转移（DM）。根据BC合并DM患者（n=67）和匹配的非DM患者（n=134）构建基于临床病理特征的诺模图（临床病理特征模型）和基于多特征融合模型的诺模图。糖尿病确诊时间平均（17.31±13.12）个月。临床病理特征模型包括7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、CA 153、CEA和内分泌治疗。多特征融合模型包括相同的特征和额外的三个MRI特征（多个肿块、脂肪饱和T2 WI信号和肿块大小）。多特征融合模型对糖尿病的预测效果相对较好。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）。内部和外部验证表明，多特征融合模型具有良好的临床推广性。MRI因素的纳入显着提高了诺模图的特异性和敏感性。所构建的多特征融合诺模图可用于指导糖尿病筛查和乳腺癌预防性治疗的实施。
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.
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影响因子：
1.6
作者：
Chen JH;Yen YC;Yang HC;Liu SH;Yuan SP;Wu LL;Lee FP;Lin KC;Lai MT;Wu CC;Chen TM;Chang CL;Chow JM;Ding YF;Wu SY
通讯作者：
Wu SY
影响因子：
0.8
作者：
Honda M;Yamada M;Kumasaka T;Samejima T;Satoh H;Sugimoto M
通讯作者：
Sugimoto M
DOI：
10.1007/s00432-018-2697-2
发表时间：
2018-09-01
影响因子：
3.6
作者：
Kim, Yi-Jun;Kim, Jae-Sung;Kim, In Ah
通讯作者：
Kim, In Ah
用于区分和潜在预测乳腺癌患者肝转移的列线图
影响因子：
3.9
作者：
Lin Z;Yan S;Zhang J;Pan Q
通讯作者：
Pan Q
影响因子：
2.8
作者：
Baltzer, Pascal A. T.;Zoubi, Ramy;Dietzel, Matthias
通讯作者：
Dietzel, Matthias