Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

N/AActive Not RecruitingNCT05565313

Maastricht Radiation Oncology900 enrolled

Overview

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

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

900

Conditions

Head and Neck Carcinoma

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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)"

Locations (3)

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

Maastro

Maastricht, Limburg, Netherlands

Outcomes

Primary Outcomes

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.

Time frame: Baseline

Secondary Outcomes

Overall Survival

Percentage of people who are alive five years after their diagnosis.

Time frame: 5 years

Disease Free Survival

Percentage of people whp who are disease free five years after their diagnosis.

Time frame: 5 years

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.