Predicting cancer outcomes from histology and genomics using convolutional networks.
Predicting cancer outcomes from histology and genomics using convolutional networks.
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DOI：
10.1073/pnas.1717139115
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发表时间：
2018-03-27
影响因子：
11.1
通讯作者：
Cooper LAD
中科院分区：
文献类型：
--
作者：
Mobadersany P;Yousefi S;Amgad M;Gutman DA;Barnholtz-Sloan JS;Velázquez Vega JE;Brat DJ;Cooper LAD
预测诊断为癌症的患者的预期结果是治疗的关键步骤。基因组学和成像技术的进步为医生提供了大量的数据，但诊断仍然很大程度上是主观的，导致次优的临床管理。我们开发了一种基于深度学习的计算方法，通过组织活检和基因组生物标志物的显微图像来预测诊断为脑肿瘤的患者的总体生存期。该方法使用自适应反馈来同时学习与患者结果相关的视觉模式和分子生物标志物。我们的方法超越了人类专家使用当前临床标准对脑肿瘤进行分类的预后准确性，并提出了一种客观，准确和综合预测患者结局的创新方法。癌症组织学反映了潜在的分子过程和疾病进展，并包含丰富的表型信息，可预测患者结局。在这项研究中，我们展示了一种使用深度学习从数字病理图像中学习患者结果的计算方法，以将自适应机器学习算法的功能与传统的生存模型联合收割机相结合。我们说明了这些生存卷积神经网络（SCNN）如何将来自组织学图像和基因组生物标志物的信息整合到一个统一的框架中，以预测事件发生时间的结果，并显示预测准确性，超过目前预测诊断为胶质瘤患者总生存期的临床范例。我们使用统计抽样技术来解决从组织学图像中学习生存的挑战，包括肿瘤异质性和对大型训练队列的需求。我们还提供了对SCNN预测机制的见解，使用热图可视化来显示SCNN识别与预后相关的重要结构，如微血管增生，并由病理学家用于分级。这些结果突出了深度学习在精准医学中的新兴作用，并表明在未来的病理学实践中，组织学计算分析的实用性不断扩大。
Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes. Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.
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DOI：
10.1109/tbme.2011.2110648
发表时间：
2011-07
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Dundar MM;Badve S;Bilgin G;Raykar V;Jain R;Sertel O;Gurcan MN
通讯作者：
Gurcan MN
DOI：
10.1109/jbhi.2016.2565515
发表时间：
2017-07-01
影响因子：
7.7
作者：
Niazi, M. Khalid Khan;Yao, Keluo;Gurcan, Metin
通讯作者：
Gurcan, Metin
影响因子：
--
作者：
Leeper HE;Caron AA;Decker PA;Jenkins RB;Lachance DH;Giannini C
通讯作者：
Giannini C
DOI：
10.1111/j.1750-3639.2012.00630.x
发表时间：
2013-05
期刊：
Brain pathology (Zurich, Switzerland)
影响因子：
--
作者：
Nguyen DN;Heaphy CM;de Wilde RF;Orr BA;Odia Y;Eberhart CG;Meeker AK;Rodriguez FJ
通讯作者：
Rodriguez FJ
影响因子：
2.7
作者：
Kothari S;Phan JH;Young AN;Wang MD
通讯作者：
Wang MD