ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability (ONCO-FIRE)
2021年10月25日 更新者：Instituto de Investigacion Sanitaria La Fe
ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing
ONCO-FIRE proposes to build a novel hyper-architecture and a common data model (CDM) for oncology, as well as a rich, modular toolset enabling significantly increased interoperability, exploitability, use and reuse of diverse, multi-modal health data available in electronic Health Records (EHR) and cancer big data repositories to the benefit of health professionals, healthcare providers and researchers; this will eventually lead to more efficient and cost-effective health care procedures and workflows that support improved care delivery to cancer patients encompassing support for cancer early prediction, diagnosis, and follow-up.
The applicability, usefulness and usability of the proposed hyper-architecture, CDM and toolset for oncology and the high exploitability of health data will be demonstrated in diverse data exploitation scenarios related to breast and prostate cancer involving a number of Virtual Assistants (VAs) and advanced services offering to health care professionals (HCPs), hospital administration/healthcare providers and researchers data-driven decision-support and easy navigation across large amounts of cancer-related information.
Through the above mentioned outcomes and the (meta)data interoperability achieved, ONCO-FIRE contributes to the exploitation of large volumes, highly heterogeneous (meta)data in EHR and data repositories including imaging data, structured data (e.g.
demographics, laboratory, pathological data), as well as diverse formats of unstructured clinical reports and notes (e.g.
text, pdf), including (but not limited to) temporal information related to the patient care pathway and genomics data currently "hidden" in unstructured medical reports, and more.
Importantly, ONCO-FIRE interconnects, following a federated approach, large, distributed cancer imaging repositories, currently used for AI tools training and validation, with patient registries and EHRs of cancer-related data and supports exploitation of relevant unstructured data through novel Natural Language Processing (NLP) tools.
The ultimate goal is to establish a patient-centric, federated multi-source and interoperable data-sharing ecosystem, where healthcare providers, clinical experts, citizens and researchers contribute, access and reuse multimodal health data, thereby making a significant contribution to the creation of the European Health Data Space.
研究概览
地位
尚未招聘
研究类型
观察性的
注册 (预期的)
5000
参与标准
研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
18年 及以上 (成人、年长者)
接受健康志愿者
不
有资格学习的性别
全部
取样方法
非概率样本
研究人群
patients of breast cancer and prostate cancer
描述
Inclusion Criteria:
- Patients of age ≥ 18 years.
- Individuals referred to hospitals for diagnosis and/or treatment of breast cancer or prostate cancer, either at first diagnoses, progression, or relapses.
- Availability of radiological images: 2D mammography or 2D synthetic digital tomosynthesis, ultrasound, and magnetic resonance for breast cancer; magnetic resonance for prostate cancer.
- Availability of pathological report (surgical specimen, including immunohistochemistry and genetic information).
- Availability of treatment allocation (neoadjuvant/Adjuvant and Advanced disease): (scheme, duration, benefit).
- Availability of treatment response evaluation
Exclusion Criteria:
- Patient with incomplete or low-quality data (radiological, pathological or clinical) In relation to the use of the data already existing in the four AI4HI repositories, ONCO-FIRE will not intervene with the inclusion and exclusion criteria of each of the four projects and will select those data that fit the ONCO-FIRE research purposes.
学习计划
本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
|
Breast Cancer
patients diagnosed with breast cancer at any stage.
|
the project will interconnect, following a federated approach, large, distributed cancer imaging repositories, currently used for AI tools training and validation, with patient registries and EHRs of cancer-related data and supports exploitation of relevant unstructured data through novel Natural Language Processing (NLP) tools
|
|
Prostate cancer
patients diagnosed with prostate cancer at any stage
|
the project will interconnect, following a federated approach, large, distributed cancer imaging repositories, currently used for AI tools training and validation, with patient registries and EHRs of cancer-related data and supports exploitation of relevant unstructured data through novel Natural Language Processing (NLP) tools
|
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Estimation of Overall survival
大体时间：Date of start of treatment untill Date of death or last contact/visit, assessed up to 2 years.
|
The lenght (in days) of time form date of start of treatment for a disease that patients is still alive.
|
Date of start of treatment untill Date of death or last contact/visit, assessed up to 2 years.
|
|
Estimation of progression free survival
大体时间：Date of start treatment until date of progression (measured by increase size in millimeters using radiological images), assessed up to 2 years.
|
The length of time (days) during and after treatment of a disease that a patient lives with the disease but it does not get worse.
|
Date of start treatment until date of progression (measured by increase size in millimeters using radiological images), assessed up to 2 years.
|
次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Estimation (%) of tumor aggressiveness non-respondents vs respondents to neoadjuvant treatment (breast):
大体时间：Date of start of treatment until date of ending treatmen, responses will be assessed during the following 6 months after starting treatment in neoadyuvancy unless toxicity or progression has occurred
|
Proportion of patients who have complete response evaluating the target lesion according to Miller/Payne Grading system [Ogston et al., 2003]: 1A.
Evaluation of target Tumor: G5 as pathological complete response, no tumor left; G4: more than 90% loss of tumor cells; G3: between 30-90% reduction in tumor cells; G2: loss of tumor <30%; G1: no reduction.
1B: Evaluating the lymph nodes: A: negative; B: lymph nodes with metastasis and without changes by chemotherapy; C: lymph nodes with metastasis with evidence of partial response, D: lymph nodes with changes attributed to response without residual infiltration.
1C: Using images to evaluated radiological response: Size and diameter in millimeters of the target lesion using RM and TC or PET/CT for extension analysis (lymph nodes and metastasis).
|
Date of start of treatment until date of ending treatmen, responses will be assessed during the following 6 months after starting treatment in neoadyuvancy unless toxicity or progression has occurred
|
合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (预期的)
2023年6月1日
初级完成 (预期的)
2025年6月1日
研究完成 (预期的)
2025年12月1日
研究注册日期
首次提交
2021年9月20日
首先提交符合 QC 标准的
2021年9月20日
首次发布 (实际的)
2021年9月29日
研究记录更新
最后更新发布 (实际的)
2021年10月29日
上次提交的符合 QC 标准的更新
2021年10月25日
最后验证
2021年9月1日
更多信息
此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.