Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

900 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-03-22

Study Completion Date

2026-08-01

Brief Summary

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

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Detailed Description

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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

Conditions

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Head and Neck Carcinoma

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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)"

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No