Integrating Quantitative Histological Image and Vascular Density Patterns for Pro

集成定量组织学图像和血管密度模式以实现 Pro

• 批准号: 7941833
• 负责人: Anant Madabhushi
• 金额: $ 8.12万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-28 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7941833
• 关键词: Algorithms; Appearance; Architecture; Area; Behavior; Biological Markers; Blood Vessels; Cancerous; Cell Nucleus; Classification; Clinical; Collaborations; Computers; Core Biopsy; Data; Detection; Development; Diagnosis; Diagnostic; Disease; E-Cadherin; Early Diagnosis; Gland; Gleason Grade for Prostate Cancer; Goals; Graph; Histocytochemistry; Histology; Histopathology; Hospitals; Human; Image; Image Analysis; Imagery; Individual; Intraobserver Variability; Learning; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Methodology; Methods; Metric; Modeling; Nuclear; Outcome; Output; PECAM1 gene; Pathologist; Patients; Pattern; Pennsylvania; Policies; Prognostic Marker; Protocols documentation; Radical Prostatectomy; Randomized; Recurrence; Research; Research Personnel; Resolution; Risk; Scheme; Slide; Specimen; Staging; Staining method; Stains; Structure; TP53 gene; Testing; Tissues; Training; Tumor stage; Universities; Validation; Work; base; cancer recurrence; cohort; computerized; density; digital; imaging Segmentation; improved; novel marker; outcome forecast; prognostic; repository; tumor; vector

DESCRIPTION (provided by applicant): With increasing detection of early CaP with improved diagnostic methodologies, it has become important to predict biologic behaviors and "aggressivity" to identify patients who might benefit from a "wait and watch policy" as opposed to those who need more aggressive strategies. Traditionally, T-stage, amount of cancer in the core biopsy, the Gleason grade, and PSA at diagnosis has been used to evaluate the prognosis in localized CaP. While the Gleason score is currently assumed to be the strongest prognostic marker for CaP, there is often considerably high inter-, intra-observer variability associated with Gleason grade determination by pathologists. While some newer markers have recently shown promise, none of these methods have individually proven to be accurate enough to serve routinely as a prognostic marker for CaP. Recently, there has been a call to combine multiple prognostic markers to create an integrated meta-marker, with potentially greater accuracy in predicting CaP recurrence compared to any individual marker. While it is apparent that prognostic information resides in histopathology imagery in terms of the arrangement of nuclei and glands, sophisticated graph, and computerized image analysis algorithms are required to quantitatively model and characterize the architectural appearance of prostate cancer histopathology and thus provide a marker that is accurate and reproducible (unlike Gleason grade). In addition, while tumor micro-vascular density has been correlated to CaP outcome, prognostic information may also potentially reside in the specific spatial architectural arrangement of the micro-vascular network. The objective of the proposed work is to develop an integrated quantitative prognostic marker that combines information based on architectural arrangement of nuclear, glandular, and micro-vasculature network patterns on whole mount histology sections (WMHS) obtained via radical prostatectomy (RP) to predict prostate cancer recurrence. The proposed work comprises a total of 3 specific aims. For this project we will digitize approximately 100 annonymized WMHS obtained via RP that have been matched for Gleason score, stage, PSA, but with different clinical outcomes (half the patients having undergone cancer recurrence and the other not, following RP). Under Aim 1, segmentation algorithms will be developed to automatically identify cancerous nuclei, glands and tumor microvasculature (MV), stained immuno-histochemically via CD31. Under Aim 2 we will apply graph based image analysis algorithms to quantitatively characterize the architectural arrangement of CaP nuclei, glands and the MV network. These graph-based features will be integrated via a computerized machine learning algorithm to yield a numerical image based risk score (IbRiS) reflecting the CaP prognosis (disease recurrence or non-recurrence) of the patient. IbRiS will be evaluated in terms of its ability to distinguish between CaP progressors and non-progressors (matched for stage, Gleason grade, PSA), in a cohort of 50 independent studies (test set) for which survival and outcome data is available. This project will be a collaboration between investigators at Rutgers University (RU) and the University of Pennsylvania (UPENN). Data accrual will be done at UPENN while algorithmic development for computerized image analysis and classification will be carried out at RU.

描述（由申请人提供）： 随着诊断方法的改进，早期CaP的检测越来越多，预测生物学行为和“侵略性”以识别可能受益于“等待和观察政策”的患者而不是需要更积极策略的患者变得重要。传统上，诊断时的T分期、核心活检中的癌量、Gleason分级和PSA已被用于评估局限性CaP的预后。虽然Gleason评分目前被认为是CaP最强的预后标志物，但与病理学家的Gleason分级确定相关的观察者间、观察者内变异性通常相当高。虽然一些较新的标记物最近显示出希望，但这些方法都没有被单独证明足够准确，可以作为CaP的常规预后标记物。 最近，人们呼吁将联合收割机多个预后标志物组合以创建整合的元标志物，与任何单个标志物相比，其在预测CaP复发方面具有潜在的更高准确性。虽然很明显，预后信息存在于组织病理学图像中的细胞核和腺体的排列方面，但需要复杂的图形和计算机化的图像分析算法来定量建模和表征前列腺癌组织病理学的结构外观，从而提供准确和可再现的标记物（与Gleason分级不同）。此外，虽然肿瘤微血管密度与CaP结果相关，但预后信息也可能存在于微血管网络的特定空间结构排列中。拟议的工作的目的是开发一个综合的定量预后标记，结合信息的基础上的核，腺，和微血管网络模式的架构安排，通过根治性前列腺切除术（RP）获得的全挂载组织切片（WMHS），以预测前列腺癌复发。 拟议的工作共包括3个具体目标。在本项目中，我们将收集大约100例通过RP获得的附件化WMHS，这些WMHS在Gleason评分、分期、PSA方面匹配，但临床结局不同（RP后，一半患者经历了癌症复发，另一半患者没有经历癌症复发）。在目标1下，将开发分割算法，以自动识别癌细胞核、腺体和肿瘤微血管（MV），通过CD31进行免疫组织化学染色。在目标2下，我们将应用基于图形的图像分析算法来定量表征CaP核、腺体和MV网络的结构排列。这些基于图形的特征将通过计算机化机器学习算法进行整合，以产生反映患者CaP预后（疾病复发或不复发）的基于数字图像的风险评分（IbRiS）。将在50项独立研究（测试集）队列中评价IbRiS区分CaP进展者和非进展者（分期、Gleason分级、PSA匹配）的能力，这些研究的生存和结局数据可用。 该项目将是罗格斯大学（RU）和宾夕法尼亚大学（UPENN）研究人员之间的合作。数据累积将在宾夕法尼亚大学进行，而计算机图像分析和分类的算法开发将在RU进行。