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

Study Overview
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Brief Summary
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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.

Official Title
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ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing

Conditions
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Breast Cancer, Prostate Cancer

Intervention / Treatment
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* Other: Virtual assistants offering medical recommendations to health care profesionals


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Breast Cancer<br>patients diagnosed with breast cancer at any stage. | Other: Virtual assistants offering medical recommendations to health care profesionals<br>* 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<br>|
| Prostate cancer<br>patients diagnosed with prostate cancer at any stage | Other: Virtual assistants offering medical recommendations to health care profesionals<br>* 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<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Estimation (%) of tumor aggressiveness non-respondents vs respondents to neoadjuvant treatment (breast): | 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 |