Predicting Radiological Extranodal Extension in Oropharyn... | NCT05565313

Studies
Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

NCT05565313

Sponsor

Maastricht Radiation Oncology

Status

Active, not recruiting

Last Update Posted

Aug 14, 2025Actual

Enrollment

900Actual

Phase

Not provided

Conditions

Head and Neck Carcinoma

Interventions

Not provided

Countries

United States
Canada
Netherlands

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Aug 2025

Overall Recruitment Status or Expanded Access Status

Active, not recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Mar 22, 2022Actual

Primary Completion Date

Aug 1, 2026Estimated

Completion Date

Aug 1, 2026Estimated

First Submitted Date

Sep 29, 2022

First Submission Date that Met QC Criteria

Sep 29, 2022

First Posted Date

Oct 4, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Aug 11, 2025

Last Update Posted Date

Aug 14, 2025Actual

Name	Class
Brigham and Women's Hospital	OTHER
Princess Margaret Hospital, Canada	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

FDAAA801 Violation

Not provided

Description Module

Brief Summary

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

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

900Actual

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
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.	Baseline

Secondary Outcomes

Measure	Description	Time Frame
Overall Survival	Percentage of people who are alive five years after their diagnosis.	5 years
Disease Free Survival	Percentage of people whp who are disease free five years after their diagnosis.

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion criteria:

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)

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)"

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder 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)"

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Frank Hoebers, PhD	Maastro	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital		Boston	Massachusetts	02115	United States

IPD Sharing Statement Module

Plan to Share IPD

Yes

Description

all IPD (individual participant data) underlying the results

Types

Study Protocol
Clinical Study Report (CSR)
Analytic Code

Time Frame

Starting 6 months after publication

Access Criteria

Not provided

URL

Not provided