Pathomics biomarkers for stratification of clear cell kidney cancers

用于透明细胞肾癌分层的病理组学生物标志物

基本信息

批准号：
10578582
负责人：
BEATRICE S KNUDSEN
金额：
$ 21.57万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-06 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10578582
关键词：
Adipose tissue Adjuvant Adjuvant Therapy Algorithms Architecture Biological Markers Blood Vessels Cancer Prognosis Cellular Morphology Classification Clear Cell Clear cell renal cell carcinoma Clinical Clinical Practice Guideline Clinical Trials Correlation Studies Data Detection Diagnosis Disease Progression Disease-Free Survival Fatty acid glycerol esters Funding Future Grant Growth Guidelines High-Risk Cancer Histologic Histology Immunotherapy Invaded Machine Learning Malignant Neoplasms Manuals Maps Metastatic Neoplasm to Lymph Nodes Modeling Morphology National Comprehensive Cancer Network Necrosis Nephrectomy Nuclear Grade Operative Surgical Procedures Pathologic Pathologist Pathology Patient Selection Patient-Focused Outcomes Patients Pattern Performance Pharmacotherapy Phase III Clinical Trials Practice Guidelines Probability Prognosis Prognostic Marker Recommendation Recurrence Recurrent Malignant Neoplasm Recurrent disease Recurrent tumor Regional Cancer Renal Cell Carcinoma Renal carcinoma Risk Risk Assessment Selection Criteria Slide Source Staging Stratification Systemic Therapy Testing The Cancer Genome Atlas Thrombus Tissue imaging Tissues Training Tumor Cell Invasion Tumor Expansion Tumor Tissue Tumor stage Universities Utah Venous Work anti-PD-L1 antibodies biomarker development cancer biomarkers cancer diagnosis cancer recurrence cancer surgery cancer type cohort computer generated convolutional neural network digital draining lymph node experience high risk improved innovation kidney surgery large datasets novel pathology imaging performance tests predictive marker predictive modeling prognostic prognostic assays prognostication random forest risk stratification side effect tissue biomarkers tumor tumor growth tumor progression

项目摘要

Project Summary Pathologic attributes of cancers, such as histology and tumor growth patterns are not quantitatively assessed to date. In every cancer type these parameters effect patient outcomes and are included in risk models of tumor recurrence and overall survival. Algorithms using machine learning and convolutional neural networks allow us to quantify pathology and develop Pathomics biomarkers. Here, we propose to obtain pathomics biomarkers of cancer recurrence/progression that enumerate histology growth patterns (HGPs) in clear cell renal cell cancer (ccRCC). ccRCC is the most common subtype of kidney cancer. In its localized stage, it is treated by nephrectomy. However, about 30% of patients experience disease progression after surgery and may benefit from adjuvant treatment. Deciding whether or not treatment is warranted requires identifying patients who are at a high risk of recurrence. Here, we hypothesize that quantitative biomarkers will improve the risk assessment of patients with ccRCC and propose to develop computer-generated features of tumor growth patterns. We previously defined 13 HGPs and demonstrated their ability to predict overall survival in patients treated for ccRCC. Distinctive features for each HGP will be generated and validated using frameworks of convolutional neural networks that produce probabilities of expression across cancer regions. Further, the distribution of probabilities will be used to obtain biomarkers of expression of each HGP. Using parametric and non-parametric models, HGP-biomarkers will be examined for their association with tumor stage and local mechanisms of ccRCC progression, such as formation of tumor thrombi, regional lymph node metastases or invasion into perinephric adipose tissues. The performance of each algorithm in the project will be evaluated. Altogether, biomarkers developed in this project will provide a starting point to select patients with ccRCC for adjuvant treatment after surgery.

项目摘要癌症的病理属性，例如组织学和肿瘤生长模式，未经定量评估迄今为止。在每种癌症类型中，这些参数都会影响患者的结果，并包括在风险模型中肿瘤复发和整体生存。使用机器学习和卷积神经网络的算法允许我们量化病理学并发展致病生物标志物。在这里，我们建议获得病原体在透明细胞中列举组织学生长模式（HGP）的癌症复发/进展的生物标志物肾细胞癌（CCRCC）。 CCRCC是肾癌最常见的亚型。在本地阶段，是通过肾切除术治疗。但是，大约30％的患者经历了手术后的疾病进展，辅助治疗可能会受益。确定是否需要治疗需要识别重复发生高风险的患者。在这里，我们假设定量生物标志物将改善 CCRCC患者的风险评估，并提议开发肿瘤的计算机生成特征生长模式。我们以前定义了13个HGP，并证明了他们预测总体生存的能力接受CCRCC治疗的患者。每个HGP的独特功能将被生成和验证卷积神经网络的框架，在癌症地区产生表达概率。此外，概率的分布将用于获得每个HGP的表达生物标志物。使用参数和非参数模型，将检查HGP生物标志物的与肿瘤的关联 CCRCC进展的阶段和局部机制，例如肿瘤血栓形成，区域淋巴结转移或侵袭直接脂肪组织。项目中每种算法的性能将进行评估。总共在该项目中开发的生物标志物将为选择患者提供一个起点手术后用CCRCC进行辅助治疗。