Kidney Cancer Clinical Trial

Predicting Radiological Extranodal Extension in Oropharyngeal Carcinoma Patients Using AI

Summary

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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Full 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

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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)"

Study is for people with:

Kidney Cancer

Estimated Enrollment:

900

Study ID:

NCT05565313

Recruitment Status:

Active, not recruiting

Sponsor:

Maastricht Radiation Oncology

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There are 3 Locations for this study

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Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women's Hospital
Boston Massachusetts, 02115, United States
Princess Margaret Cancer Centre
Toronto Ontario, M5G 2, Canada
Maastro
Maastricht Limburg, 6229 , Netherlands

How clear is this clinincal trial information?

Study is for people with:

Kidney Cancer

Estimated Enrollment:

900

Study ID:

NCT05565313

Recruitment Status:

Active, not recruiting

Sponsor:


Maastricht Radiation Oncology

How clear is this clinincal trial information?

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