Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI (AI4rENE)

Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning

Study Overview

• Status: Active, not recruiting
• Conditions: Head and Neck Carcinoma

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 Type: Observational
• Enrollment (Actual): 900

Contacts and Locations

Study Locations

• Canada
  • Ontario
    • Toronto, Ontario, Canada, M5G 2M9
      • Princess Margaret Cancer Centre
• Netherlands
  • Limburg
    • Maastricht, Limburg, Netherlands, 6229 ET
      • MAASTRO
• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02115
      • Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

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)"

Description

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)"

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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 Outcome Measures

Outcome Measure	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.	5 years

Collaborators and Investigators

• Sponsor: Maastricht Radiation Oncology
• Collaborators: Brigham and Women's Hospital; Princess Margaret Hospital, Canada
• Investigators: Principal Investigator: Frank Hoebers, PhD, MAASTRO

Study record dates

Study Major Dates

• Study Start (Actual): March 22, 2022
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): August 1, 2026

Study Registration Dates

• First Submitted: September 29, 2022
• First Submitted That Met QC Criteria: September 29, 2022
• First Posted (Actual): October 4, 2022

Study Record Updates

• Last Update Posted (Actual): August 14, 2025
• Last Update Submitted That Met QC Criteria: August 11, 2025
• Last Verified: August 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Neoplasms; Neoplasms by Histologic Type; Neoplasms, Glandular and Epithelial; Neoplastic Processes; Neoplasm Metastasis; Extranodal Extension; Carcinoma
• Other Study ID Numbers: W 22 03 00000 - P0471 V1.2

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: all IPD (individual participant data) underlying the results
• IPD Sharing Time Frame: Starting 6 months after publication
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No