An active learning approach for rapid characterization of endothelial cells in human tumors.
An active learning approach for rapid characterization of endothelial cells in human tumors.
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DOI：
10.1371/journal.pone.0090495
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lee WM
中科院分区：
文献类型：
--
作者：
Padmanabhan RK;Somasundar VH;Griffith SD;Zhu J;Samoyedny D;Tan KS;Hu J;Liao X;Carin L;Yoon SS;Flaherty KT;Dipaola RS;Heitjan DF;Lal P;Feldman MD;Roysam B;Lee WM
目前，尚无可用的肿瘤血管生成的病理或分子测量方法来预测临床实践中使用的抗血管生成疗法的反应。认识到肿瘤内皮细胞（EC）和EC激活和生存信号是这些治疗的直接目标，我们试图开发一个自动化平台，通过组织病理学来量化肿瘤患者EC中关键信号通路和其他生物事件的活性。由于主观性和选择偏差，使用由人类专家选择的示例训练的统计分类器对高度异质人类肿瘤中的EC进行计算机图像分析表现不佳。我们假设分析可以通过更积极的过程来优化，以帮助专家识别信息丰富的训练示例。为了验证这一假设，我们将一种新颖的主动学习（AL）算法整合到FARSIGHT图像分析软件中，该软件通过为操作员寻找信息丰富的示例来帮助专家进行标记。由此产生的FARSIGHT-AL系统识别EC的特异性和灵敏度始终大于0.9，优于传统的监督分类算法。该系统模拟了操作人员的个人偏好，并产生了可重复的结果。利用EC分类的结果，我们还量化了免疫染色的人透明细胞肾细胞癌和其他肿瘤的增殖（Ki67）和重要信号转导通路（MAP激酶，STAT3）的活性。FARSIGHT-AL能够在更自动化的过程中对传统保存的人类肿瘤中的EC进行表征，适合在临床试验中进行测试和验证。我们的研究结果为定量血管生成提供了一个独特的机会，现在可以对其识别新的预测和反应生物标志物的能力进行测试。
Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.
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对肾细胞癌生物学的新见解。
DOI：
10.1016/j.hoc.2011.04.004
发表时间：
2011-08
期刊：
Hematology/oncology clinics of North America
影响因子：
--
作者：
Li L;Kaelin WG Jr
通讯作者：
Kaelin WG Jr
影响因子：
3
作者：
Bjornsson, Christopher S.;Lin, Gang;Roysam, Badrinath
通讯作者：
Roysam, Badrinath
影响因子：
3.6
作者：
Lassoued, Wiem;Murphy, Danielle;Lee, William M. F.
通讯作者：
Lee, William M. F.
DOI：
10.1109/tgrs.2008.2005268
发表时间：
2009-05-01
影响因子：
8.2
作者：
Liao, Xuejun;Carin, Lawrence
通讯作者：
Carin, Lawrence
影响因子：
158.5
作者：
Hurwitz, H;Fehrenbacher, L;Kabbinavar, F
通讯作者：
Kabbinavar, F