Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

Active, not recruiting

• Conditions: Head and Neck Carcinoma

• Registration Number: NCT05565313
• Lead Sponsor: Maastricht Radiation Oncology

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

Study Timeline

• Study Start: 2022-03-22
• Study First Submitted: 2022-09-29
• Study First Submitted QC: 2022-09-29
• Study First Posted: 2022-10-04
• Status Verified: 2023-10
• Last Update Submitted: 2023-10-20
• Last Update Posted: 2023-10-23
• Primary Completion: 2024-04-01
• Study Completion: 2024-04-01

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 900

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Prediction of rENE as labeled by the radiologist, using the AI model	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.

Secondary Outcome Measures

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

Trial Locations

Locations (3)

Maastro; 🇳🇱 Maastricht, Limburg, Netherlands

Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital; 🇺🇸 Boston, Massachusetts, United States

Princess Margaret Cancer Centre; 🇨🇦 Toronto, Ontario, Canada