Clinical trial NCT05565313: Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI
Official title: Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Head and Neck Carcinoma
Lead sponsor: Maastricht Radiation Oncology
Sponsor class: OTHER
Collaborators: name: Brigham and Women's Hospital
name: Princess Margaret Hospital, Canada
First posted: 2022-10-04
Enrollment: 900
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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)"

Brief 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

Eligibility 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)"

Primary outcomes:
measure: Prediction of rENE as labeled by the radiologist, using the AI model
timeFrame: Baseline
Secondary outcomes:
measure: Overall Survival
timeFrame: 5 years
measure: Disease Free Survival
timeFrame: 5 years
Other outcomes:
Locations:
city: Boston
country: United States
facility: Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital
state: Massachusetts
city: Toronto
country: Canada
facility: Princess Margaret Cancer Centre
state: Ontario
city: Maastricht
country: Netherlands
facility: Maastro
state: Limburg
Study officials:
affiliation: Maastro
name: Frank Hoebers, PhD
role: PRINCIPAL_INVESTIGATOR
References: