In short, Kenneth Gilhuijs's research entails the following:
Triage of Patients with High-risk Oral Squamous cell Carcinoma using Artificial Intelligence (TOSCA)
Oral squamous cell carcinoma (OSCC) is a type of cancer that affects the mouth and throat. It is relatively rare, accounting for 1-4% of all cancers in western countries with 971 new cases in the Netherlands in 2021. The presence of lymph node metastases - or the spread of cancer to the lymph nodes - is the most important factor in predicting the outcome of the disease.
Currently, patients at risk of occult - or hidden - lymph node metastases are treated with an elective neck dissection (END), a surgical procedure that removes lymph nodes from the neck. However, this procedure turns out to be unnecessary in 80% of early OSCC patients, exposing these patients to unnecessary morbidity.
An alternative is a sentinel lymph node biopsy (SNB), which involves removing and testing a single lymph node for cancer. If the sentinel node is negative, no further treatment is needed. If the sentinel node is positive, a second, more extensive, surgery is necessary (completed neck dissection), requiring repeat hospital admission and general anesthesia. Moreover, this second surgery is more difficult to perform after an elective neck dissection, risking more morbidity and delaying follow-up (adjuvant) treatment.
In this retrospective observational cohort study with approximately 1.000 patients from seven hospitals, we aim to develop a new strategy based on deep learning of MRI to refer the largest number of patients with occult lymph node metastases directly to completed neck dissection, instead of sentinel lymph node biopsy, without referring node-negative patients to neck dissection.
TOSCA is a collaboration between the Image Sciences Institute and the departments Head-and-neck surgical oncology, Radiology, and Radiotherapy of UMC Utrecht. In addition, six other head-and-neck oncology treatment centers in the Netherlands participate.