Head and Neck CarcinomaAI4rENENCT05565313
Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI
An observational study of Head and Neck Carcinoma, run by Maastricht Radiation Oncology at 3 sites in 3 countries.
Trial summary
Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Head and Neck Carcinoma
- How many people
- 900 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 22 Mar 2022to 1 Aug 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Inclusion criteria: * Non-metastatic (M0) node-positive HPV+ and HPV- (human papilloma virus) oropharyngeal carcinoma * Treated between 2008 to 2019 * Curative intent * Radiation only or concurrent chemoradiation * Modern treatment modality: IMRT / VMAT (Intensity Modulated RadioTherapy / Volumetric-Modulated Arc Therapy) * diagnostic/staging image scanning protocols available (contrast-enhanced CT with 2-3 mm slice thickness and/or MR (magnetic resonance) with 3 mm slice thickness) Exclusion criteria: * removal of lymph node (LN) (excisional biopsy or neck dissection [ND]) prior to staging CT/MR scan * no available imaging within 2 months prior to radiotherapy (RT)"
Who may be able to join
- Non-metastatic (M0) node-positive HPV+ and HPV- oropharyngeal carcinoma
- Treated between 2008 to 2019
- Curative intent
- Radiation only or concurrent chemoradiation
- Modern treatment modality: IMRT / VMAT
- diagnostic/staging image scanning protocols available (contrast-enhanced CT with 2-3 mm slice thickness and/or MR with 3 mm slice thickness)
Who cannot join
- removal of lymph node (LN) (excisional biopsy or neck dissection [ND]) prior to staging CT/MR scan
- no available imaging within 2 months prior to radiotherapy (RT)"
Trial locations and contacts
3 sites in 3 countries.
3 sites in 3 countries
United States1 site
Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital
Boston, Massachusetts, 02115
About this study
Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
Read the detailed description
Oropharyngeal squamous cell carcinoma (OPSCC) is a rare cancer (incidence ~700 per year in the Netherlands), originating in the middle part of the throat. In OPSCC, nodal status is an important prognostic factor for survival. In the clinical TNM (tumor node metastases) system, nodal status is mainly defined by the size, number and laterality of nodal metastases. In surgically treated patients the pathological TNM classification includes the presence of pathological extranodal extension (pENE). pENE is a predictor for poor outcome and also an indication for the addition of chemotherapy to postoperative radiation. However, most patients with OPSCC are treated non-surgically by means of radiation or chemoradiation and thus information about pENE is lacking. Recently, extranodal extension on diagnostic imaging has been associated with prognosis in OPSCC patients. It is anticipated that in the near future radiological ENE (rENE) may be included in the cTNM classification system for refinement of outcome prediction in patients with nodal disease. The diagnosis of rENE on radiological imaging is new and not trivial and we hypothesize that Artificial Intelligence (AI) may support the radiologist in detecting rENE. In this study we aim to develop and validate a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
- Conditions
- Head and Neck Carcinoma
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction of rENE as labeled by the radiologist, using the AI model
The performance of the model will be evaluated in terms of discrimination through the Harrell's C-index and the area (AUC) under the receiver operator curve (ROC) in predicting rENE.
Measured: Baseline
Secondary outcomes (2)
Overall Survival
Percentage of people who are alive five years after their diagnosis.
Measured: 5 years
Disease Free Survival
Percentage of people whp who are disease free five years after their diagnosis.
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Oct 2022
- Study start22 Mar 2022
- Primary completion1 Aug 2026
- Study completion1 Aug 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Head and Neck CarcinomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 900 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht Radiation Oncology
- Sponsor type
- University, hospital or other
- Collaborators
- Brigham and Women's Hospital, Princess Margaret Hospital, Canada
- Responsible party
- Sponsor
- Investigators
- Frank Hoebers, PhD, Principal investigator, Maastro
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05565313
- Sponsor's protocol ID
- W 22 03 00000 - P0471 V1.2
- Acronym
- AI4rENE
- Status last verified
- Oct 2026
- First submitted
- 29 Sep 2022
- Last updated
- 9 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: all IPD (individual participant data) underlying the results
Study at a glance
From the registry record, last updated by the sponsor on 9 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI
- ClinicalTrials.gov ID
- NCT05565313
- Acronym / sponsor ID
- AI4rENE / W 22 03 00000 - P0471 V1.2
- Conditions
- Head and Neck Carcinoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 900 participants (actual)
- Lead sponsor
- Maastricht Radiation OncologyUniversity, hospital or other
- Collaborators
- Brigham and Women's Hospital, Princess Margaret Hospital, Canada
- Study start
- 22 Mar 2022 (actual)
- Primary completion
- 1 Aug 2026 (actual)
- Study completion
- 1 Aug 2026 (actual)
- First posted
- 4 Oct 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
Detailed description
Oropharyngeal squamous cell carcinoma (OPSCC) is a rare cancer (incidence ~700 per year in the Netherlands), originating in the middle part of the throat. In OPSCC, nodal status is an important prognostic factor for survival. In the clinical TNM (tumor node metastases) system, nodal status is mainly defined by the size, number and laterality of nodal metastases. In surgically treated patients the pathological TNM classification includes the presence of pathological extranodal extension (pENE). pENE is a predictor for poor outcome and also an indication for the addition of chemotherapy to postoperative radiation. However, most patients with OPSCC are treated non-surgically by means of radiation or chemoradiation and thus information about pENE is lacking. Recently, extranodal extension on diagnostic imaging has been associated with prognosis in OPSCC patients. It is anticipated that in the near future radiological ENE (rENE) may be included in the cTNM classification system for refinement of outcome prediction in patients with nodal disease. The diagnosis of rENE on radiological imaging is new and not trivial and we hypothesize that Artificial Intelligence (AI) may support the radiologist in detecting rENE. In this study we aim to develop and validate a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Inclusion criteria: * Non-metastatic (M0) node-positive HPV+ and HPV- (human papilloma virus) oropharyngeal carcinoma * Treated between 2008 to 2019 * Curative intent * Radiation only or concurrent chemoradiation * Modern treatment modality: IMRT / VMAT (Intensity Modulated RadioTherapy / Volumetric-Modulated Arc Therapy) * diagnostic/staging image scanning protocols available (contrast-enhanced CT with 2-3 mm slice thickness and/or MR (magnetic resonance) with 3 mm slice thickness) Exclusion criteria: * removal of lymph node (LN) (excisional biopsy or neck dissection [ND]) prior to staging CT/MR scan * no available imaging within 2 months prior to radiotherapy (RT)"
Key inclusion criteria (6 listed)
- Non-metastatic (M0) node-positive HPV+ and HPV- oropharyngeal carcinoma
- Treated between 2008 to 2019
- Curative intent
- Radiation only or concurrent chemoradiation
- Modern treatment modality: IMRT / VMAT
Key exclusion criteria (2 listed)
- removal of lymph node (LN) (excisional biopsy or neck dissection [ND]) prior to staging CT/MR scan
- no available imaging within 2 months prior to radiotherapy (RT)"
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction of rENE as labeled by the radiologist, using the AI model
Time frame: Baseline
The performance of the model will be evaluated in terms of discrimination through the Harrell's C-index and the area (AUC) under the receiver operator curve (ROC) in predicting rENE.
2 secondary outcomes
Overall Survival
Time frame: 5 years
Disease Free Survival
Time frame: 5 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Oct 2026.
- Individual participant dataSharing planned: Yes. all IPD (individual participant data) underlying the results
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Frank Hoebers, PhD principal investigator
Maastro
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Head and Neck Carcinoma, run by Maastricht Radiation Oncology at 3 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United States, Canada, Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of rENE as labeled by the radiologist, using the AI model (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Oct 2026.