Fast IR imaging-based AI identifies tumour type in lung cancer

29 Jun 2021
Fast IR imaging-based AI identifies tumour type in lung cancer

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.

Treatment decision by means of a genetic mutation analysis

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.

Fast and reliable measuring technique

The potential of infrared imaging, IR imaging for short, as a diagnostic tool to classify tissue, called label-free digital pathology, was already shown by the group led by Klaus Gerwert in previous studies.

The procedure identifies cancerous tissue without prior staining or other markings and functions automatically with the aid of artificial intelligence (AI).

In contrast to the methods used to determine tumour shape and mutations in tumour tissue in everyday clinical practice, which can sometimes take several days, the new procedure only takes around half an hour.

In these 30 minutes, it is not only possible to ascertain whether the tissue sample contains tumour cells, but also what type of tumour it is and whether it contains a certain mutation.

Infrared spectroscopy makes genetic mutations visible

The Bochum researchers were able to verify the procedure on samples from over 200 lung cancer patients in their work.

When identifying mutations, they concentrated on by far the most common lung tumour, adenocarcinoma, which accounts for over 50 per cent of tumours.

Its most common genetic mutations can be determined with a sensitivity and specificity of 95 per cent compared to laborious genetic analysis.

"For the first time, we were able to identify spectral markers that allow for a spatially resolved distinction between various molecular conditions in lung tumours," explains Nina Goertzen from PRODI. A single infrared spectroscopic measurement offers information about the sample which would otherwise require several time-consuming procedures.

A further step towards personalised medicine

The results once again confirm the potential of label-free digital pathology for clinical use. "To further increase reliability and promote a translation of the method as a new diagnostic tool, studies with larger patient numbers adapted to clinical needs and external testing in everyday clinical practice are required," says Dr. Frederik Großerüschkamp, IR imaging project manager.

"In order to translate IR imaging into everyday clinical practice, it is crucial to shorten the measuring time, ensure simple and reliable operation of the measuring instruments, and provide answers to questions that are important and helpful both clinically and for the patients."

Source: Ruhr-University Bochum