A composite biomarker for esophageal cancer risk from automated analysis of a non-endoscopic device
A composite biomarker for esophageal cancer risk from automated analysis of a non-endoscopic device
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来自非内窥镜设备自动分析的食管癌风险复合生物标志物
DOI：
10.1101/2021.08.20.21262366
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Berman A
中科院分区：
文献类型：
--
作者：
Berman A
含有肠化生的Barrett‘s食道易患癌症，但大多数病例未确诊。Barrett‘s节段的长度是癌症风险的关键指标，但到目前为止，测量它依赖于内窥镜检查，这是昂贵和侵入性的。Cytsponge-TFF3是一种微创检测方法，可用于确定肠化生以用于内窥镜检查。我们报告了一种机器学习技术，可以量化肠化生的程度，并根据Cytosponge-TFF3组织学切片自动生成的全幻灯片图像块计数来预测Barrett的节段长度。利用来自529名患者的数据，我们的片段长度预测模型达到了0.84的平均验证折叠精度。将该算法应用于筛选试验中162名患者的独立测试集，显示出识别短节段疾病的精度为0.90。这一进展将使高危患者优先接受内窥镜检查，同时在筛查环境中节省一半以上的Cytsponge-TFF3阳性患者的内窥镜检查。
Barrett’s esophagus containing intestinal metaplasia predisposes to cancer, yet the majority of cases are undiagnosed. The length of a Barrett’s segment is a key indicator of cancer risk, but measuring it has so far relied on endoscopy, which is expensive and invasive. Cytosponge-TFF3 is a minimally-invasive test that identifies intestinal metaplasia for endoscopic confirmation. We report a machine learning technique to quantify the extent of intestinal metaplasia and predict Barrett’s segment length from whole-slide image tile counts automatically generated from Cytosponge-TFF3 histology slides. Utilizing data from 529 patients, our segment length prediction model achieves an average validation fold accuracy of 0.84. Applying this algorithm to an independent test set of 162 patients from a screening trial shows a precision of 0.90 for identifying short-segment disease. This advance will enable higher-risk patients to be prioritized for endoscopy while saving more than half of Cytosponge-TFF3-positive patients from endoscopy in the screening setting.
DOI：
10.1093/jnci/djr203
发表时间：
2011-07-01
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
Bhat, Shivaram;Coleman, Helen G.;Murray, Liam J.
通讯作者：
Murray, Liam J.
影响因子：
29.4
作者：
Parasa, Sravanthi;Vennalaganti, Sreekar;Sharma, Prateek
通讯作者：
Sharma, Prateek
影响因子：
29.4
作者：
Sharma, Prateek;Dent, John;Vieth, Michael
通讯作者：
Vieth, Michael