ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability (ONCO-FIRE)
25 de octubre de 2021 actualizado por: 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.
Descripción general del estudio
Estado
Aún no reclutando
Condiciones
Intervención / Tratamiento
Tipo de estudio
De observación
Inscripción (Anticipado)
5000
Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
18 años y mayores (Adulto, Adulto Mayor)
Acepta Voluntarios Saludables
No
Géneros elegibles para el estudio
Todos
Método de muestreo
Muestra no probabilística
Población de estudio
patients of breast cancer and prostate cancer
Descripción
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.
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
|---|---|
|
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
|
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Estimation of Overall survival
Periodo de tiempo: 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
Periodo de tiempo: 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.
|
Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Estimation (%) of tumor aggressiveness non-respondents vs respondents to neoadjuvant treatment (breast):
Periodo de tiempo: 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
|
Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Anticipado)
1 de junio de 2023
Finalización primaria (Anticipado)
1 de junio de 2025
Finalización del estudio (Anticipado)
1 de diciembre de 2025
Fechas de registro del estudio
Enviado por primera vez
20 de septiembre de 2021
Primero enviado que cumplió con los criterios de control de calidad
20 de septiembre de 2021
Publicado por primera vez (Actual)
29 de septiembre de 2021
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
29 de octubre de 2021
Última actualización enviada que cumplió con los criterios de control de calidad
25 de octubre de 2021
Última verificación
1 de septiembre de 2021
Más información
Términos relacionados con este estudio
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- ONCO-FIRE
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
NO
Descripción del plan IPD
N/D
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
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