Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images
使用全玻片组织病理学图像进行高效且经济的乳腺癌风险分层
基本信息
- 批准号:10649978
- 负责人:
- 金额:$ 19.09万
- 依托单位:
- 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:2023-04-06 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:AgeAttentionBiological AssayBiological MarkersBiopsyBreast Cancer PatientBreast Cancer Risk FactorBreast Cancer therapyCancer PrognosisCategoriesCell NucleusCellsClassificationClinicalClinical ResearchClinical TrialsConsumptionDataDeveloping CountriesDiagnosisDiagnosticDiseaseDuct (organ) structureERBB2 geneEffectivenessEnsureEpitheliumEstrogen ReceptorsEstrogen receptor positiveEstrogensGene ExpressionGenesHealthHematoxylin and Eosin Staining MethodHistologicHistologyHistopathologyHumanImageImage AnalysisInternetInterobserver VariabilityLearningMalignant NeoplasmsMeasuresMethodsMissionModelingNatureOutcomePatientsPerformancePlayPopulationPremature MenopauseProgesteroneProgesterone ReceptorsPrognosisPublic HealthRecurrenceRecurrence ScoreRegional AnatomyResearchResource-limited settingResourcesReverse Transcriptase Polymerase Chain ReactionRiskSlideStainsStratificationTimeTissuesTrainingTumor TissueTumor stageUnited StatesUnited States National Institutes of HealthValidationWomanbreast imagingchemotherapycohortcostcost effectivedesigndetection methoddiagnostic accuracyerbB-2 Receptorhigh riskhormone receptor-positiveimaging biomarkerimprovedlarge datasetsmalignant breast neoplasmmortalitynovel strategiesoncotypeoptimal treatmentsoverexpressionpatient stratificationpersonalized carepreventprogesterone receptor positivereceptorrisk stratificationrural areaside effecttooltumorweb-based toolwhole slide imaging
项目摘要
Efficient and cost-effective breast cancer risk stratification using whole-slide histopathology
images
Breast cancer prognosis depends highly on receptor status, as optimal treatment depends on
the presence or absence of overexpression of estrogen, progesterone, or HER-2/neu receptors.
To prevent over-treating patients with chemotherapy, it is crucial to quantify the risk of
recurrence for estrogen receptor (ER) positive (ER+), HER2 negative (HER2-) breast cancer. A
common assessment method to meet this need is the Oncotype DX (ODX) Recurrence Score.
Unfortunately, ODX and similar gene assays are expensive, time-consuming, and tissue
destructive. As an alternative, we propose estimating the ODX recurrence score using routine,
ubiquitous, and inexpensive hematoxylin and eosin (H&E) staining of biopsies. There are other
efforts to predict ODX recurrence risk from H&E. These automated methods detect histological
primitives (e.g., nuclei) often in specific, also automatically detected, anatomical regions (e.g.,
ducts, tubules, lumen, epithelium, and stroma). Classification is performed into two or three risk
categories, often collapsing two categories into one. The performance of these models is
promising but still modest. One way to improve the performance of the models is to train on
larger datasets; however, annotating larger datasets is challenging. Here, we propose an
automated method to predict ODX recurrence risk without annotations. If successful, this
method would have a wide range of applications, including but not limited to the availability of an
inexpensive, web-based tool to predict ODX in developing countries or rural areas with internet
access where standard Oncotype Dx assay would be cost-prohibitive or take too long to obtain.
Furthermore, our method would find use in clinical research where valuable tumor tissue could
be saved by obtaining correlative research data based on standard H&E-stained slides.
使用全载玻片组织病理学进行有效且具有成本效益的乳腺癌风险分层
图像
乳腺癌预后高度依赖于受体状态,因为最佳治疗取决于
是否存在雌激素、孕酮或HER-2/neu受体的过表达。
为了防止过度治疗化疗患者,关键是量化
雌激素受体(ER)阳性(ER+)、HER 2阴性(HER 2-)乳腺癌复发。一
满足这种需要的常用评估方法是Oncotype DX(ODX)复发评分。
不幸的是,ODX和类似的基因测定是昂贵的,耗时的,并且组织损伤。
破坏性的作为替代方案,我们建议使用常规方法估计ODX复发评分,
普遍存在的和廉价的苏木精和伊红(H&E)染色的活组织检查。还有其他
努力预测来自H&E的ODX复发风险。这些自动化方法检测组织学
基元(例如,细胞核)中,通常在特定的,也是自动检测的,解剖区域(例如,
导管、小管、管腔、上皮和基质)。将风险分为两种或三种
分类,通常将两个类别合并为一个。这些模型的性能是
很有前途但还是很谦虚提高模型性能的一种方法是训练
更大的数据集;然而,注释更大的数据集是具有挑战性的。在这里,我们提出一个
自动化方法预测ODX复发风险,无需注释。如果成功,这
方法将具有广泛的应用,包括但不限于
廉价的,基于网络的工具,预测ODX在发展中国家或农村地区与互联网
在标准Oncotype Dx检测成本过高或需要太长时间才能获得的情况下,
此外,我们的方法将用于临床研究,其中有价值的肿瘤组织可以
通过标准H& E染色切片获取相关研究数据,从而保存数据。
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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Metin Nafi Gurcan其他文献
Gene pointNet for tumor classification
- DOI:
10.1007/s00521-024-10307-x - 发表时间:
2024-08-22 - 期刊:
- 影响因子:4.500
- 作者:
Hao Lu;Mostafa Rezapour;Haseebullah Baha;Muhammad Khalid Khan Niazi;Aarthi Narayanan;Metin Nafi Gurcan - 通讯作者:
Metin Nafi Gurcan
Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning
- DOI:
10.1186/s12864-025-11553-6 - 发表时间:
2025-04-10 - 期刊:
- 影响因子:3.700
- 作者:
Mostafa Rezapour;Aarthi Narayanan;Wyatt H. Mowery;Metin Nafi Gurcan - 通讯作者:
Metin Nafi Gurcan
Metin Nafi Gurcan的其他文献
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{{ truncateString('Metin Nafi Gurcan', 18)}}的其他基金
Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learning
通过使用机器学习将数字耳镜与补充数据相结合来计算机辅助诊断耳部病变
- 批准号:
10564534 - 财政年份:2023
- 资助金额:
$ 19.09万 - 项目类别:
Culturally Augmented Learning In Biomedical Informatics Research (CALIBIR) Program
生物医学信息学研究中的文化增强学习 (CALIBIR) 计划
- 批准号:
10631379 - 财政年份:2022
- 资助金额:
$ 19.09万 - 项目类别:
Culturally Augmented Learning In Biomedical Informatics Research (CALIBIR) Program
生物医学信息学研究中的文化增强学习 (CALIBIR) 计划
- 批准号:
10701848 - 财政年份:2022
- 资助金额:
$ 19.09万 - 项目类别:
Analytics & Machine-learning for Maternal-health Interventions (AMMI): A Cross-CTSA Collaboration
分析
- 批准号:
10670448 - 财政年份:2022
- 资助金额:
$ 19.09万 - 项目类别:
Auto-Scope Software-Automated Otoscopy to Diagnose Ear Pathology
Auto-Scope 软件 - 用于诊断耳部病理的自动耳镜检查
- 批准号:
9790958 - 财政年份:2018
- 资助金额:
$ 19.09万 - 项目类别:
Pathology Image Informatics Platform for visualization, analysis and management
用于可视化、分析和管理的病理图像信息学平台
- 批准号:
9341177 - 财政年份:2015
- 资助金额:
$ 19.09万 - 项目类别:
Computer-based assessment of tumor microenvironment (TME) in Follicular Lymphoma
基于计算机的滤泡性淋巴瘤肿瘤微环境 (TME) 评估
- 批准号:
9611415 - 财政年份:2009
- 资助金额:
$ 19.09万 - 项目类别:
OAMiner: Integrative Knowledge Anchored Hypothesis Discovery
OMiner:综合知识锚定假设发现
- 批准号:
7828221 - 财政年份:2009
- 资助金额:
$ 19.09万 - 项目类别:
Computer-assisted Grading and Risk Stratification of Follicular Lymphoma
滤泡性淋巴瘤的计算机辅助分级和风险分层
- 批准号:
8215904 - 财政年份:2009
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Computer-assisted Grading and Risk Stratification of Follicular Lymphoma
滤泡性淋巴瘤的计算机辅助分级和风险分层
- 批准号:
8024533 - 财政年份:2009
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