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Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI
NCT05565313 · View on ClinicalTrials.gov ↗
Study Summary
Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
Conditions Studied
Study Locations (3)
Massachusetts
- Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital — Boston
Ontario
- Princess Margaret Cancer Centre — Toronto
Limburg
- Maastro — Maastricht
Trial Details
| Field | Value |
|---|---|
| Enrollment Target | 900 participants |
| Start Date | 2022-03-22 |
| Est. Completion | 2026-08-01 |
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Full Details on ClinicalTrials.gov ↗What the Registry Record Tells You About NCT05565313
The ClinicalTrials.gov registry entry for NCT05565313 describes a study currently listed as active not recruiting. It is categorized as an unspecified phase, which is the standard way researchers label where a study sits along the investigational pathway from early safety work through later efficacy and post-marketing evaluation. The registered enrollment target is 900 participants, a figure that helps gauge the scale of data the investigators plan to collect. The listed sponsor is Maastricht Radiation Oncology, which has 1 total studies on file at ClinicalTrials.gov, and sponsors are the parties responsible for study design, oversight, and regulatory filings.
The record links to 1 condition, with Head and Neck Carcinoma appearing as the primary indexed condition, and to 0 interventions. Interventions can include drugs, devices, procedures, behavioral programs, or observational arms, and each is tracked as a separate registry field so that downstream queries can filter accurately. When a trial lists multiple interventions, it usually reflects a multi-arm design or a comparison protocol rather than a single treatment being tested in isolation. The brief summary published in the registry is the clearest source of protocol intent and should be read before drawing conclusions from any sidebar tags.
Geographic footprint matters for practical reasons: NCT05565313 reports 3 study locations spanning 3 distinct geographic areas — top geographies include Massachusetts, Ontario, Limburg. A larger site network tends to correlate with broader recruitment capacity, but it does not imply anything about study quality, and site-level enrollment status can diverge from the overall registry status shown above. Every data point on this page comes from the public ClinicalTrials.gov dataset and is reproduced here for reference only; it is not a medical recommendation, an endorsement of the sponsor, or an invitation to enroll. Verify current status, eligibility criteria, and contact details directly at ClinicalTrials.gov, and discuss any participation decision with your own healthcare provider.
Frequently Asked Questions
What is clinical trial NCT05565313 about?
NCT05565313 is a clinical study titled "Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI". Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
What is the current status of trial NCT05565313?
This trial is currently active not recruiting. The enrollment target is 900 participants. The study started on 2022-03-22. Estimated completion is 2026-08-01.
What conditions does trial NCT05565313 study?
This clinical trial studies the following conditions: Head and Neck Carcinoma. These conditions were identified from the trial registry and reflect the primary focus areas of the research.
Who is sponsoring clinical trial NCT05565313?
This trial is sponsored by Maastricht Radiation Oncology, which has 1 total clinical trials registered on ClinicalTrials.gov. The sponsor is responsible for the study's design, funding, and regulatory compliance.
Where is trial NCT05565313 being conducted?
This trial has 3 study locations across Massachusetts, Ontario, Limburg. Contact the study sites directly through ClinicalTrials.gov for enrollment availability.
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