Sci Rep:肌肉侵入性膀胱癌预后中,机器学习自动化肿瘤芽殖定量能够改善TNM分期

2019-04-12 AlexYang MedSci原创

肿瘤芽殖在一些肿瘤类型中是一个独立的预后特征。最近,有研究人员在肌肉侵入性膀胱癌患者中首次报道了肿瘤芽殖与生存评估之间的关系。研究人员利用基于机器学习的方法学来精确的对肿瘤芽进行定量,具体是100名肌肉浸润性膀胱癌患者的所有免疫荧光标签的整张图片分析。更多的是,肿瘤芽殖与TNM(p=0.00089)和pT(p=0.0078)阶段相关。研究人员还基于疾病特异性生存,建立了一个新的分类和回归树模型来对

肿瘤芽殖在一些肿瘤类型中是一个独立的预后特征。最近,有研究人员在肌肉侵入性膀胱癌患者中首次报道了肿瘤芽殖与生存评估之间的关系。

研究人员利用基于机器学习的方法学来精确的对肿瘤芽进行定量,具体是100名肌肉浸润性膀胱癌患者的所有免疫荧光标签的整张图片分析。更多的是,肿瘤芽殖与TNM(p=0.00089)和pT(p=0.0078)阶段相关。研究人员还基于疾病特异性生存,建立了一个新的分类和回归树模型来对所有阶段II、III和IV患者重新按照新的标准分成3层。对那些非转移性患者分层到高或者低风险疾病特异性死亡情况,他们的决策树模型报道了肿瘤芽殖是最明显的特性(HR=2.59, p=0.0091),并且没有临床特性用于对这些患者进行分类。

最后,研究人员指出,他们的发现阐释了利用自动化图像学习分析对肿瘤芽殖进行定量为肌肉浸润膀胱癌患者提供了预后值,并且比TNM分期模型更好。

原始出处:

Nicolas Brieu, Christos G. Gavriel, Ines P. Nearchou et al. Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis. Sci Rep. 26 Mar 2019.

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    2019-09-25 zhs3882
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    2019-10-08 Eleven11
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    2019-04-12 留走人康

    膀胱癌真怪,明明是免疫敏感性肿瘤,为什么PD-1治疗效果不好呢?难道靶点不对?将来CD47会不会有效

    0

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