Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

Head and Neck Carcinoma Clinical Trial

— AI4rENE  

Official title:

Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05565313
• Other study ID #: W 22 03 00000 - P0471 V1.2
• Status: Active, not recruiting
• Start date: March 22, 2022
• Est. completion date: April 1, 2024

Study information

• Verified date: October 2023
• Source: Maastricht Radiation Oncology
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

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

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 900
• Est. completion date: April 1, 2024
• Est. primary completion date: April 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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)"

Related Conditions & MeSH terms

• Carcinoma
• Head and Neck Carcinoma

Locations

Country	Name	City	State
Canada	Princess Margaret Cancer Centre	Toronto	Ontario
Netherlands	Maastro	Maastricht	Limburg
United States	Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital	Boston	Massachusetts

Sponsors (3)

Lead Sponsor	Collaborator
Maastricht Radiation Oncology	Brigham and Women's Hospital, Princess Margaret Hospital, Canada

Countries where clinical trial is conducted

United States,  Canada,  Netherlands, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Overall Survival	Percentage of people who are alive five years after their diagnosis.	5 years
Secondary	Disease Free Survival	Percentage of people whp who are disease free five years after their diagnosis.	5 years