Artificial Intelligence in Oncology

Supporting scientific research

Granted applications 2023

Danielle Cohen

LUMC

In short, Danielle Cohen's research entails the following:

SalvIdentify: improving salivary gland tumor diagnostics by artificial intelligence based classification

Diagnosing salivary gland tumors (SGTs) poses one of the most challenging tasks for pathologists. Salivary glands are extremely rare and diverse. Within both malignant and benign tumors, there exist dozens of subtypes, and their number is increasing due to molecular insights constantly discovering new types. Pathologists may encounter some subtypes only a few times in their careers, making expertise difficult, if not impossible. For patients, the issue is significant: the circumstances surrounding salivary gland diagnostics are associated with delays and misdiagnoses, potentially impacting timely and appropriate treatment.

In this project, we are developing SalvIdentify, a platform inspired by the successful biodiversity classification tool 'ObsIdentify' from the Naturalis Center for Biodiversity. SalvIdentify aims to empower pathologists worldwide to upload anonymized digital images of SGTs and receive a differential diagnosis based on SALV-AI, a national digital histopathological database of SGTs.

The research is conducted by the Diagnostic SGT Consortium (DSGTC), a collaboration of head and neck pathologists and clinicians from head and neck oncology centers in the Netherlands and beyond. The goal is for SalvIdentify to enable pathologists everywhere (in the Netherlands and worldwide) to tap into decades of salivary gland pathology 'memory.' This would make it easier to identify rare subtypes, and biological behavioral patterns of subgroups will manifest more quickly.

Our research plan includes the following objectives:

• generating SALV-AI (the world's largest open-source histopathological SGT database);
• developing the SalvIdentify model;
• launching the SalvIdentify platform;
• validating the additional value of SalvIdentify in clinical practice;
• salvIdentify is a collaborative request on behalf of the 'Dutch Salivary Gland Tumor Consortium’.