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:
- 金额:$ 62.44万
- 依托单位:
- 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:
- 关键词:3-DimensionalAdjuvant TherapyArchivesBiochemicalBiological AssayBiopsyBiopsy SpecimenBreadCategoriesClassificationClinicClinicalClinical ManagementComplexComputer softwareComputing MethodologiesDataData SetDevelopmentDiagnosticDiseaseExcisionGene Expression ProfilingGenerationsGenitourinary systemGlassGleason Grade for Prostate CancerGoalsGuidelinesHandHistologyHistopathologyImageImaging DeviceIndolentIntuitionLightLocalized DiseaseMagnetic Resonance ImagingMalignant neoplasm of prostateMethodsMicroscopeMicroscopicMicroscopyMicrotomyModelingMolecularMorphologyNational Comprehensive Cancer NetworkNeoplasm MetastasisNomogramsNuclearOncologistOncologyOperative Surgical ProceduresOpticsOutcomePathologistPathologyPatientsPennsylvaniaPerformancePhenotypePrognostic MarkerProstateProstate Cancer therapyProstatectomyPublic HealthRadiationRadical ProstatectomyRecurrenceResolutionRiskSamplingSerinusSlideSpecimenStructureSystemTechnologyThree-dimensional analysisTissue SampleTissuesTrainingUniversitiesUrologistValidationVisualWashingtonbasecancer imagingcellular imagingclinical riskcurative treatmentsfeature extractionimprovedinnovationinstrumentationmennovelpatient stratificationpredictive markerpreservationprognosticprognostic assaysprognostic valueprognosticationprostate biopsyprostate cancer riskprototyperisk stratificationsuccesssurgical risksurveillance imagingthree dimensional structuretoolvalidation studieswhole 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.
摘要前列腺癌(PCa)治疗管理目前严重依赖于基于载玻片的
前列腺活检和手术标本(前列腺切除术)的组织学。特别是,Gleason分级
组织学切片为临床管理的患者分层提供了基础,
不同的治疗路径。然而,通过格里森分级的分级存在几个缺点,
包括基于2D图像的复杂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之间的沟通不畅
病理学家之间的一致性以及惰性与致命疾病患者的分层较差。为
前列腺癌的临床管理,泌尿科医生和肿瘤科医生分别面临的两个主要挑战是:(1)
正确识别低风险PCa男性进行积极监测,(2)识别可能患有PCa的男性
治愈性治疗(手术或放疗)后疾病复发和转移,因此将受益于
辅助治疗凭借我们的开放式光片(OTLS)显微镜技术,我们在密歇根大学的团队
华盛顿(Liu group)已经证明了实现高通量无载玻片3D
以无损和可逆的方式对活检和手术标本进行组织学检查,
用目前的组织学方法。与传统病理学相比的潜在优势包括:(1)全面的成像
标本(活组织检查和手术面包),而不是在载玻片上薄切片的稀疏采样;
(2)3D结构的体积成像是预后的;和(3)非破坏性成像,其允许
有价值的活检标本用于下游检测。凯斯西储大学Case Western Reserve University
(Madabhushi小组)还开发了计算病理学分类器,基于直观和可解释的
“手工制作的特征”,用于基于2D全载玻片成像(WSI)表征PCa侵袭性。在
在R 01项目中,我们寻求将联合收割机非破坏性三维病理学与三维计算病理学方法相结合
开发一种新的预后分析,通过3D病理学的前列腺癌图像风险评分(ProsIRiS 3D),
区分惰性和侵袭性前列腺癌。在目标1中,我们将开发核心技术(硬件
和软件)的ProsIRiS 3D。具体而言,目标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,以探索这种系统提供的额外预后益处。
高分辨率特征。在目标1b中,将开发计算成像工具,用于提取新的3D
定量组织形态计量学特征用于PCa鉴别。我们的临床验证研究将表明,
对于泌尿科医生来说,ProsIRiS 3D上级类似的2D方法(目的2),以确定哪些新活检
患者应接受积极的监测与治疗,以及肿瘤学家(目标3),
确定哪些前列腺切除术患者有可能需要辅助治疗的侵袭性疾病。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jonathan T.C. Liu其他文献
Artificial Intelligence–Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads
人工智能——分层三维病理学以提高食管肿瘤的检测,同时减少病理学家的工作量
- DOI:
10.1016/j.modpat.2023.100322 - 发表时间:
2023-12-01 - 期刊:
- 影响因子:5.500
- 作者:
Lindsey A. Erion Barner;Gan Gao;Deepti M. Reddi;Lydia Lan;Wynn Burke;Faisal Mahmood;William M. Grady;Jonathan T.C. Liu - 通讯作者:
Jonathan T.C. Liu
Trends and Challenges for the Clinical Adoption of Fluorescence-Trends and Challenges for the Clinical Adoption of Fluorescence-Guided Surgery Guided Surgery
荧光引导手术临床采用的趋势和挑战-荧光引导手术临床采用的趋势和挑战 引导手术
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Jonathan T.C. Liu;Nader Sanai - 通讯作者:
Nader Sanai
Jonathan T.C. Liu的其他文献
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{{ truncateString('Jonathan T.C. Liu', 18)}}的其他基金
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:
10647788 - 财政年份:2022
- 资助金额:
$ 62.44万 - 项目类别:
Instrumentation platform for 3D pathology with open-top light-sheet microscopy
具有开顶光片显微镜的 3D 病理学仪器平台
- 批准号:
10434718 - 财政年份:2021
- 资助金额:
$ 62.44万 - 项目类别:
Instrumentation platform for 3D pathology with open-top light-sheet microscopy
具有开顶光片显微镜的 3D 病理学仪器平台
- 批准号:
10178401 - 财政年份:2021
- 资助金额:
$ 62.44万 - 项目类别:
Instrumentation platform for 3D pathology with open-top light-sheet microscopy
具有开顶光片显微镜的 3D 病理学仪器平台
- 批准号:
10630094 - 财政年份:2021
- 资助金额:
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In vivo dual-axis confocal microscopy of 5-ALA-induced PpIX to guide low-grade glioma resections
5-ALA 诱导的 PpIX 体内双轴共聚焦显微镜指导低级别胶质瘤切除
- 批准号:
10407972 - 财政年份:2020
- 资助金额:
$ 62.44万 - 项目类别:
In vivo dual-axis confocal microscopy of 5-ALA-induced PpIX to guide low-grade glioma resections
5-ALA 诱导的 PpIX 体内双轴共聚焦显微镜指导低级别胶质瘤切除
- 批准号:
10684738 - 财政年份:2020
- 资助金额:
$ 62.44万 - 项目类别:
Intraoperative confocal microscopy for quantitative delineation of low-grade glio
术中共聚焦显微镜定量描绘低级别胶质细胞
- 批准号:
9118107 - 财政年份:2014
- 资助金额:
$ 62.44万 - 项目类别:
Intraoperative confocal microscopy for quantitative delineation of low-grade glio
术中共聚焦显微镜定量描绘低级别胶质细胞
- 批准号:
8696044 - 财政年份:2014
- 资助金额:
$ 62.44万 - 项目类别:
Intraoperative confocal microscopy for quantitative delineation of low-grade glio
术中共聚焦显微镜定量描绘低级别胶质细胞
- 批准号:
8890436 - 财政年份:2014
- 资助金额:
$ 62.44万 - 项目类别:
Endoscopic molecular imaging of esophageal cancer with multiplexed Raman nanopart
使用多重拉曼纳米部件对食管癌进行内窥镜分子成像
- 批准号:
8283324 - 财政年份:2013
- 资助金额:
$ 62.44万 - 项目类别:
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