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

• 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
• 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

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）的治疗管理目前严重依赖于基于幻灯片