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Eur Urol Focus:基于人工智能的常规组织学检测膀胱癌FGFR3突变状态

2021-05-06 AlexYang MedSci原创

成纤维细胞生长因子受体(FGFR)抑制剂治疗已成为膀胱癌中第一个获得临床批准的靶向疗法。然而,该疗法需要事先对每为患者进行分子测试,昂贵且不是普遍可用的。

成纤维细胞生长因子受体(FGFR)抑制剂治疗已成为膀胱癌中第一个获得临床批准的靶向疗法。然而,该疗法需要事先对每为患者进行分子测试,昂贵且不是普遍可用的。

最近,有研究人员确定了人工智能系统是否能够直接从膀胱癌的常规组织学切片中预测FGFR3基因的突变情况

研究人员训练了一个深度学习网络来检测苏木精和伊红染色的数字化肌肉浸润性膀胱癌切片的FGFR3突变情况,这些切片来自癌症基因组图谱(TCGA)队列(n=327),并在 "Aachen "队列(n=182;n=121 pT2-4,n=34个色斑浸润性pT1和n=27个非浸润性pTa肿瘤)中验证了该算法。研究结果发现,在TCGA队列中,检测到FGFR3突变的AUROC为0.701(P<0.0001)。在Aachen队列中,发现FGFR3突变的AUROC为0.725(P<0.0001)。当对TCGA进行训练时,该网络可以推广到Aachen队列,并以0.625的AUROC(p=0.0112)检测到FGFR3突变体。亚组分析和组织学评估发现,乳头状生长、管状基因表达亚型、女性和美国癌症联合委员会(AJCC)II期肿瘤的准确性最高。在head-to-head的比较中,深度学习系统在检测FGFR3突变体方面的表现优于尿路病理学家。

基于AI的FGFR3突变状态预测

最后,研究人员指出,他们的基于计算机的人工智能系统能够直接从组织学切片中检测出膀胱癌患者的FGFR3基因的遗传改变情况。在未来,该系统可用于预选患者进行进一步的分子测试。然而,仍旧需要对更大的、多中心的、肌肉浸润性膀胱癌队列进行分析,以验证和扩展他们的发现。

原始出处:

Chiara Maria Lavinia Loeffler, Nadina Ortiz Bruechle , Max Jung et al. Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing? Eur Urol Focus. Apr 2021

 

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    2021-05-08 liuyiping
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    2021-05-06 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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