A research team has been able to reliably identify tumor type using a combination of infrared microscopy and artificial intelligence.
The examined tissue does not need to be marked for this. The analysis only takes around half an hour. “This is a major step that shows that infrared imaging can be a promising methodology in future diagnostic testing and treatment prediction,” says Professor Klaus Gerwert, director of PRODI. The study is published in the American Journal of Pathology on 1 July 2021.
Lung tumours are divided into various types, such as small cell lung cancer, adenocarcinoma and squamous cell carcinoma. Many rare tumour types and sub-types also exist. This diversity hampers reliable rapid diagnostic methods in everyday clinical practice. In addition to histological typing, the tumour samples also need to be comprehensively examined for certain changes at a DNA level. “Detecting one of these mutations is important key information that influences both the prognosis and further therapeutic decisions,” says co-author Professor Reinhard Büttner, head of the Institute of General Pathology and Pathological Anatomy at University Hospital Cologne.
Patients with lung cancer clearly benefit when the driver mutations have previously been characterised: for instance, tumours with activating mutations in the EGFR (epidermal growth factor) gene often respond well to tyrosine kinase inhibitors, whereas non-EGFR-mutated tumours or tumours with other mutations, such as KRAS, do not respond at all to this medication. The differential diagnosis of lung cancer previously took place with immunohistochemical staining of tissue samples and a subsequent extensive genetic analysis to determine the mutation.