Development and validation of a model that predicts rENE from radiological imaging using annotated / labeled scans by means of deep learning
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
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)"
Primary outcome measure(s)
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.
Trial sites (3)
Facility
City
Region
Status
Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital
Boston
Massachusetts
Princess Margaret Cancer Centre
Toronto
Ontario
Maastro
Maastricht
Limburg
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This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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