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Trial Title:
Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI
NCT ID:
NCT05565313
Condition:
Head and Neck Carcinoma
Conditions: Official terms:
Carcinoma
Extranodal Extension
Study type:
Observational
Overall status:
Active, not recruiting
Study design:
Time perspective:
Retrospective
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
Criteria for eligibility:
Study pop:
Inclusion criteria:
- Non-metastatic (M0) node-positive HPV+ and HPV- (human papilloma virus)
oropharyngeal carcinoma
- Treated between 2008 to 2019
- Curative intent
- Radiation only or concurrent chemoradiation
- Modern treatment modality: IMRT / VMAT (Intensity Modulated RadioTherapy /
Volumetric-Modulated Arc Therapy)
- diagnostic/staging image scanning protocols available (contrast-enhanced CT with 2-3
mm slice thickness and/or MR (magnetic resonance) 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)"
Sampling method:
Non-Probability Sample
Criteria:
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)"
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital
Address:
City:
Boston
Zip:
02115
Country:
United States
Facility:
Name:
Princess Margaret Cancer Centre
Address:
City:
Toronto
Zip:
M5G 2M9
Country:
Canada
Facility:
Name:
Maastro
Address:
City:
Maastricht
Zip:
6229 ET
Country:
Netherlands
Start date:
March 22, 2022
Completion date:
April 1, 2024
Lead sponsor:
Agency:
Maastricht Radiation Oncology
Agency class:
Other
Collaborator:
Agency:
Brigham and Women's Hospital
Agency class:
Other
Collaborator:
Agency:
Princess Margaret Hospital, Canada
Agency class:
Other
Source:
Maastricht Radiation Oncology
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05565313
