In short, Frank Hoebers' fellowship entails the following:
Predicting radiological extranodal extension in oropharyngeal carcinoma patients using AI
In oropharyngeal squamous cell carcinoma, one of the most important prognostic factors is nodal status, including the presence of extranodal extension (ENE) with tumor cells infiltrating beyond the lymph node capsule into the surrounding tissues. In surgically treated patients, ENE can be diagnosed by pathological examination (pENE). However, most patients with oropharyngeal carcinoma are treated non-surgically by means of radiation or chemoradiation and thus information about pENE is lacking. Recently, extranodal extension based on radiological imaging (rENE) has also been associated with poor prognosis in these patients.
The scientific goal of this fellowship is to develop an AI tool that will support the radiologist in detecting rENE on radiological imaging.
Although AI has great potential for clinical practice, implementation in the clinic has proven to be difficult. This is in part attributed to the fact that for medical doctors it is often difficult to understand the underlying technology and computational methods that are used to create the algorithm. This may lead to lack of trust and skepticism. So, there is an urgent need to train clinicians in the field of AI.
The second goal of this fellowship is that I – as a clinician radiation-oncologist – will be trained to acquire knowledge and background skills in order to understand the development of AI software, with the particular use-case of detecting rENE on imaging. After this fellowship I will aim to become a future liaison between clinical data-scientists (developers) and the medical community (users) to increase support of the use of AI in daily routine.
Through generous funding by the Hanarth Fonds, I will be able to spend this personal fellowship at the Harvard Medical School in Boston, USA at the Artificial Intelligence in Medicine (AIM) Program.