Prostate cancer risk stratification via computational 3D pathology

通过计算 3D 病理学进行前列腺癌风险分层

基本信息

批准号：
10459767
负责人：
Jonathan T.C. Liu
金额：
$ 62.44万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10459767
关键词：
3-Dimensional Adjuvant Therapy Archives Biochemical Biological Assay Biopsy Biopsy Specimen Bread Categories Classification Clinic Clinical Clinical Management Complex Computer software Computing Methodologies Data Data Set Development Diagnostic Disease Excision Gene Expression Profiling Generations Genitourinary system Glass Gleason Grade for Prostate Cancer Goals Guidelines Hand Histology Histopathology Image Imaging Device Indolent Intuition Light Localized Disease Magnetic Resonance Imaging Malignant neoplasm of prostate Methods Microscope Microscopic Microscopy Microtomy Modeling Molecular Morphology National Comprehensive Cancer Network Neoplasm Metastasis Nomograms Nuclear Oncologist Oncology Operative Surgical Procedures Optics Outcome Pathologist Pathology Patients Pennsylvania Performance Phenotype Prognostic Marker Prostate Prostate Cancer therapy Prostatectomy Public Health Radiation Radical Prostatectomy Recurrence Resolution Risk Sampling Serinus Slide Specimen Structure System Technology Three-dimensional analysis Tissue Sample Tissues Training Universities Urologist Validation Visual Washington base cancer imaging cellular imaging clinical risk curative treatments feature extraction improved innovation instrumentation men novel patient stratification predictive marker preservation prognostic prognostic assays prognostic value prognostication prostate biopsy prostate cancer risk prototype risk stratification success surgical risk surveillance imaging three dimensional structure tool validation studies whole slide imaging

项目摘要

Summary. Prostate cancer (PCa) treatment management is currently heavily reliant upon slide-based histology of prostate biopsies and surgical specimens (prostatectomies). In particular, Gleason grading of histology sections provides a basis for stratifying patients for clinical management, and can result in dramatically different treatment paths. However, prognostication via Gleason grading suffers from several shortcomings, including subjective visual interpretation of complex 3D glandular morphologies based on 2D images, and analysis of a limited amount of tissue (~1% of the biopsy). These shortcomings contribute to poor inter-observer concordance amongst pathologists and poor stratification of patients with indolent vs. lethal disease. For the clinical management of PCa, two major challenges faced by urologists and oncologists, respectively, are: (1) correctly identifying men with low-risk PCa for active surveillance and (2) identifying men who are likely to have disease recurrence and metastasis after curative therapy (surgery or radiation), and hence would benefit from adjuvant therapy. With our open-top light-sheet (OTLS) microscope technologies, our team at the University of Washington (Liu group) has demonstrated the technical feasibility of achieving high-throughput slide-free 3D histology of biopsy and surgical specimens in a nondestructive and reversible manner that does not interfere with current histology methods. Potential benefits over traditional pathology include: (1) comprehensive imaging of specimens (biopsies and surgical bread loafs) rather than sparse sampling of thin sections on glass slides; (2) volumetric imaging of 3D structures that are prognostic; and (3) non-destructive imaging, which allows valuable biopsy specimens to be used for downstream assays. Our team at Case Western Reserve University (Madabhushi group) has also developed computational pathology classifiers, based on intuitive and interpretable “hand-crafted features,” for characterization of PCa aggressiveness based on 2D whole-slide imaging (WSI). In this R01 project, we seek to combine nondestructive 3D pathology with 3D computational pathology approaches to develop a novel prognostic assay, Prostate cancer Image Risk Score via 3D pathology (ProsIRiS3D), for discriminating between indolent and aggressive PCa. In Aim 1, we will develop the core technologies (hardware and software) for ProsIRiS3D. In particular, the goal of Aim 1a is to develop a “4th-generation” OTLS microscopy system capable of achieving sub-nuclear-resolution to explore the added prognostic benefit provided by such high-resolution features. In Aim 1b, computational imaging tools will be developed for extraction of novel 3D quantitative histomorphometric features for PCa prognostication. Our clinical validation studies will show that ProsIRiS3D is superior to analogous 2D approaches for urologists (Aim 2), to determine which newly biopsied patients should be placed on active surveillance vs. curative therapy, as well as for oncologists (Aim 3), to determine which prostatectomy patients have aggressive disease that may warrant adjuvant therapies.

总结：前列腺癌 (PCa) 治疗管理目前严重依赖于基于载玻片的治疗。前列腺活检和手术标本（前列腺切除术）的组织学，特别是格里森分级。组织学切片为临床管理的患者分层提供了基础，并可显着改善患者的预后。然而，通过格里森分级进行预测有几个缺点，包括基于 2D 图像对复杂 3D 腺体形态的主观视觉解释，以及分析有限数量的组织（约 1% 的活检），这些缺点导致观察者间的观察结果不佳。病理学家之间的一致性以及惰性与致命疾病患者的不良分层。 PCa的临床管理，泌尿科医师和肿瘤科医师分别面临的两大挑战是：（1）正确识别患有低风险前列腺癌的男性进行主动监测，以及 (2) 识别可能患有前列腺癌的男性治愈性治疗（手术或放射）后疾病复发和转移，因此将受益于借助我们的开顶光片 (OTLS) 显微镜技术，我们大学的团队华盛顿（刘组）论证了实现高通量无载玻片3D的技术可行性以不干扰的非破坏性和可逆方式对活检和手术标本进行组织学检查与传统病理学相比，当前的组织学方法的潜在优势包括：（1）综合成像。标本（活组织检查和外科面包），而不是载玻片上的薄切片的稀疏采样； (2) 具有预测意义的 3D 结构体积成像；以及 (3) 无损成像，它允许有价值的活检标本可用于下游分析。（Madabhushi 小组）还开发了基于直观和可解释的计算病理学分类器 “手工制作的特征”，用于基于 2D 全切片成像 (WSI) 表征 PCa 侵袭性。在这个 R01 项目中，我们寻求将无损 3D 病理学与 3D 计算病理学方法相结合开发一种新的预后测定方法，通过 3D 病理学进行前列腺癌图像风险评分 (ProsIRiS3D)，用于区分惰性和攻击性 PCa 在目标 1 中，我们将开发核心技术（硬件）。特别是，Aim 1a 的目标是开发“第四代”OTLS 显微镜。能够实现亚核分辨率的系统，以探索此类技术提供的额外预后益处在目标 1b 中，将开发计算成像工具来提取新颖的 3D 特征。我们的临床验证研究将表明 PCa 预测的定量组织形态学特征。对于泌尿科医生来说，ProsIRiS3D 优于类似的 2D 方法（目标 2），可以确定哪些新活检与治疗相比，患者以及肿瘤科医生应接受主动监测（目标 3），以确定哪些前列腺切除术患者患有可能需要辅助治疗的侵袭性疾病。