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ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability (ONCO-FIRE)

25 ottobre 2021 aggiornato da: 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.

Panoramica dello studio

Stato

Non ancora reclutamento

Condizioni

Intervento / Trattamento

Tipo di studio

Osservativo

Iscrizione (Anticipato)

5000

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

18 anni e precedenti (Adulto, Adulto più anziano)

Accetta volontari sani

No

Sessi ammissibili allo studio

Tutto

Metodo di campionamento

Campione non probabilistico

Popolazione di studio

patients of breast cancer and prostate cancer

Descrizione

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.

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

Coorti e interventi

Gruppo / Coorte
Intervento / Trattamento
Breast Cancer
patients diagnosed with breast cancer at any stage.
Altro: Virtual assistants offering medical recommendations to health care profesionals
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
Altro: Virtual assistants offering medical recommendations to health care profesionals
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

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Estimation of Overall survival
Lasso di tempo: 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
Lasso di tempo: 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.

Misure di risultato secondarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Estimation (%) of tumor aggressiveness non-respondents vs respondents to neoadjuvant treatment (breast):
Lasso di tempo: 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

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Sponsor

Instituto de Investigacion Sanitaria La Fe

Collaboratori

Karolinska University Hospital
University College Cork
Medical University of Gdansk

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Anticipato)

1 giugno 2023

Completamento primario (Anticipato)

1 giugno 2025

Completamento dello studio (Anticipato)

1 dicembre 2025

Date di iscrizione allo studio

Primo inviato

20 settembre 2021

Primo inviato che soddisfa i criteri di controllo qualità

20 settembre 2021

Primo Inserito (Effettivo)

29 settembre 2021

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)

29 ottobre 2021

Ultimo aggiornamento inviato che soddisfa i criteri QC

25 ottobre 2021

Ultimo verificato

1 settembre 2021

Maggiori informazioni

Termini relativi a questo studio

Termini MeSH pertinenti aggiuntivi

Altri numeri di identificazione dello studio

  • ONCO-FIRE

Piano per i dati dei singoli partecipanti (IPD)

Hai intenzione di condividere i dati dei singoli partecipanti (IPD)?

NO

Descrizione del piano IPD

N/D

Informazioni su farmaci e dispositivi, documenti di studio

Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti

No

Studia un dispositivo regolamentato dalla FDA degli Stati Uniti

No

Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .

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