QuBBD: Collaborative Research: Quantifying Morphologic Phenotypes in Prostate Cancer - Developing Topological Descriptors for Machine Learning Algorithms

QuBBD：合作研究：量化前列腺癌的形态表型 - 开发机器学习算法的拓扑描述符

• 批准号: 1664848
• 负责人: Carola Wenk
• 金额: $ 47.93万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664848&HistoricalAwards=false
• 关键词: QuBBD; Collaborative; Research; Quantifying; Morphologic

The long-term goal of this project is to develop quantitative methodology for detecting geometric and topological features in point clouds extracted from (histology) images. Of particular relevance, this project considers the setting of prostate cancer classification, which is based on a pathologist grading of histology slides using the Gleason grading system. These pathology slides are a source of biomedical big data that are increasingly available as archived material. Developing these quantitative methods will be a significant advance towards a (semi-)automated quantification of prostate cancer aggressiveness. This award supports an interdisciplinary team of investigators in computational mathematics, computer science, biomedical engineering, and pathology to develop mathematical and computational tools based on topological descriptors and machine learning in order to distinguish between different morphological types of prostate cancer.This research will develop quantitative topological descriptors (e.g., persistence diagrams and summaries) that describe natural histologic phenotypes in prostate cancer, in order to provide explanatory information to assist in providing improved diagnostics/prognostics and insight into the best course of treatment for the patient. This will be accomplished through developing graphical models via unsupervised machine learning that increase our understanding of prostate cancer subtypes. The long-term goal is to develop imaging biomarkers that better identify indolent from aggressive prostate cancer compared to existing, subjective, and variable human observer analyses (i.e., the Gleason score). This project takes steps towards a novel quantitative methodology for prostate cancer classification, as well as towards developing topological methods for statistically distinguishing different types of glandular architectures.

该项目的长期目标是开发定量方法，用于检测从（组织学）图像中提取的点云的几何和拓扑特征。 特别相关的是，该项目考虑了前列腺癌分类的设置，该分类基于病理学家使用格里森分级系统对组织学切片进行的分级。 这些病理学幻灯片是生物医学大数据的来源，越来越多地作为存档材料提供。 开发这些定量方法将是对前列腺癌侵袭性的（半）自动量化的重大进展。 该奖项支持计算数学、计算机科学、生物医学工程和病理学领域的跨学科研究人员团队开发基于拓扑描述符和机器学习的数学和计算工具，以区分不同形态类型的前列腺癌。这项研究将开发定量拓扑描述符（例如，持久性图和摘要），其描述了前列腺癌中的自然组织学表型，以便提供解释性信息以帮助提供改进的诊断/诊断学和对患者的最佳治疗过程的洞察。这将通过无监督机器学习开发图形模型来实现，以增加我们对前列腺癌亚型的理解。长期目标是开发成像生物标志物，与现有的、主观的和可变的人类观察者分析（即，Gleason评分）。该项目采取措施，对前列腺癌分类的一种新的定量方法，以及对统计学区分不同类型的腺体结构的拓扑方法的发展。

项目成果

Persistence Atlas for Critical Point Variability in Ensembles

• DOI: 10.1109/tvcg.2018.2864432
• 发表时间: 2018-07
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Guillaume Favelier;Noura Faraj;B. Summa;Julien Tierny

Flexible Live-Wire: Image Segmentation with Floating Anchors

灵活的火线：使用浮动锚点进行图像分割

• DOI: 10.1111/cgf.13364
• 发表时间: 2018
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Summa, B.;Faraj, N.;Licorish, C.;Pascucci, V.
• 通讯作者: Pascucci, V.

Adaptive Compositing and Navigation of Variable Resolution Images

• DOI: 10.1111/cgf.14178
• 发表时间: 2020-10
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: C. Licorish;Noura Faraj;B. Summa

Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution

• DOI: 10.1109/tvcg.2020.3030381
• 发表时间: 2020-10
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: D. Hoang;B. Summa;H. Bhatia;Peter Lindstrom;Pavol Klacansky;W. Usher;P. Bremer;Valerio Pascucci

Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology

• DOI: 10.1038/s41598-018-36798-y
• 发表时间: 2019-02-04
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Lawson, Peter;Sholl, Andrew B.;Wenk, Carola
• 通讯作者: Wenk, Carola