Deep learning based tissue analysis predicts outcome in colorectal cancer.
Deep learning based tissue analysis predicts outcome in colorectal cancer.
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DOI：
10.1038/s41598-018-21758-3
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发表时间：
2018-02-21
影响因子：
4.6
通讯作者：
Lundin J
中科院分区：
文献类型：
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作者：
Bychkov D;Linder N;Turkki R;Nordling S;Kovanen PE;Verrill C;Walliander M;Lundin M;Haglund C;Lundin J
特别是基于图像的机器学习和深度学习最近在医学图像分类中显示出专家级的准确性。在这项研究中，我们将联合收割机卷积和递归架构结合起来，训练一个深度网络，以根据肿瘤组织样本的图像预测结直肠癌的结果。我们方法的新奇在于我们直接预测患者的结果，而无需任何中间组织分类。我们评估了一组数字化的苏木精-伊红染色的肿瘤组织微阵列（TMA）样本从420例结直肠癌患者的临床病理和结果的数据。结果表明，仅以小组织区域作为输入的基于深度学习的结果预测优于（风险比2.3; CI 95% 1.79-3.03; AUC 0.69）由人类专家对两个TMA点进行目视组织学评估（HR 1.67; CI 95% 1.28-2.19; AUC 0.58）和全载玻片水平（HR 1.65; CI 95% 1.30-2.15; AUC 0.57）。我们的研究结果表明，最先进的深度学习技术可以从结直肠癌的组织形态中提取比经验丰富的人类观察者更多的预后信息。
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79–3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28–2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30–2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
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影响因子：
8.4
作者：
Linder, Nina;Martelin, Eeva;Lundin, Johan
通讯作者：
Lundin, Johan
影响因子：
4.5
作者：
Lanza, Giovanni;Messerini, Luca;Risio, Mauro
通讯作者：
Risio, Mauro
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
--
作者：
Roux L;Racoceanu D;Loménie N;Kulikova M;Irshad H;Klossa J;Capron F;Genestie C;Le Naour G;Gurcan MN
通讯作者：
Gurcan MN
DOI：
10.1109/taslp.2016.2520371
发表时间：
2016-04-01
影响因子：
5.4
作者：
Palangi, Hamid;Deng, Li;Ward, Rabab
通讯作者：
Ward, Rabab