ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability

NCT05060835 | Unknown

• 1 other identifier: ONCO-FIRE
• Study Type: observational
• Target: 5,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Breast Cancer; Prostate Cancer

Outcome Measures

Primary Outcomes (2)

• Estimation of Overall survival

  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

  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.

Secondary Outcomes (1)

• Estimation (%) of tumor aggressiveness non-respondents vs respondents to neoadjuvant treatment (breast):

  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

Study Arms (2)

Breast Cancer

patients diagnosed with breast cancer at any stage.

Other: Virtual assistants offering medical recommendations to health care profesionals

Prostate cancer

patients diagnosed with prostate cancer at any stage

Other: Virtual assistants offering medical recommendations to health care profesionals

Interventions

Virtual assistants offering medical recommendations to health care profesionals OTHER

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

Breast Cancer; Prostate cancer

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients of breast cancer and prostate cancer

You may qualify if:

• 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

Sponsors & Collaborators

• Instituto de Investigacion Sanitaria La Fe: lead
• Karolinska University Hospital: collaborator
• University College Cork: collaborator
• Medical University of Gdansk: collaborator

MeSH Terms

Conditions

Breast Neoplasms; Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Genital Neoplasms, Male; Urogenital Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Central Study Contacts

Ana Penades-Blasco, M.Ec

CONTACT

+34 961245633; ana_penades@iislafe.es

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 20, 2021
• First Posted: September 29, 2021
• Study Start: June 1, 2023
• Primary Completion: June 1, 2025
• Study Completion: December 1, 2025
• Last Updated: October 29, 2021

Record last verified: 2021-09

Data Sharing

• IPD Sharing: Will not share