Research shows fluorescence lifetime imaging microscopy (FLIM) combined with a form of artificial intelligence (AI) known as deep learning can predict if a person has lung cancer-related EGFR mutations with no need for genetic testing or tissue staining.
As reported in the journal Cancer Research, the AI model was able to achieve 96.6% accuracy on a standard diagnostic test and could also distinguish between the two most important subtypes of the EGFR mutation, which matters because they respond differently to treatment and carry different survival outlooks.
“With this research we are able to take a single fluorescent image and can very accurately predict whether the mutation is present. We can do this with a single image, without special stains and without gene sequencing,” co-lead author Ahsan Akram, MBChB, PhD, a professor at the Institute for Regeneration and Repair, University of Edinburgh, told Inside Precision Medicine.
“All the cells in our body, to some degree are capable of emitting light when excited by the correct type of laser light, what FLIM does it takes this emitted light and measures the time taken for the fluorescence to be emitted. The FLIM signal therefore captures a snapshot of the metabolic activity through an ‘optical fingerprint’.”
Lung cancer is the second most common cancer in the U.S. and by far the deadliest. For many lung cancer patients, particularly non-smokers, the key question is whether their tumor carries a mutation in a gene called EGFR, encoding a protein that drives cancer cell growth. If it does, they can receive effective targeted drugs, but looking for these mutations currently requires genetic testing. This is commonly PCR or next-generation sequencing, both of which are slow, expensive, and result in the tumor tissue sample being lost after testing.
The team used FLIM to scan lung tissue samples from 85 patients and trained a deep learning model (DenseNet-169) to classify each sample as EGFR-mutant or normal. The model achieved an area under the receiver operating characteristic curve score, a standard statistical measure of diagnostic accuracy, of 0.966, outperforming all previously published methods that rely on conventionally stained tissue images. The model also distinguished between the two most common EGFR mutation subtypes, an exon 19 deletion and an exon 21 point mutation.
“The machine learning was able to extract features that are specific to the EGFR mutation, and although we don’t exactly know what these are, when we test these on samples it has never seen before we saw remarkable accuracy in the prediction,” says Akram.
Although the microscopes needed to carry out FLIM are not cheap, this technique has several advantages that counteract this including speed of testing and also allowing the sample material to be reused for other tests as destructive staining is not needed.
“Current pathways require specialized labs and next generation sequencing. These are time consuming and costly. It can take weeks to have an answer. Here, as we are taking an image using laser light the process can take minutes,” he says.
“As we are shining laser light on the sample, without any stains, this is a completely nondestructive process. The tissue sample is intact following the image and then can be used for whatever else is needed. This is particularly important in lung cancer diagnostics as often we run out of tissue to do the full suite of tests we require in a cancer diagnostic workup.”
The authors acknowledge the current FLIM imaging and analysis process is somewhat time-consuming, taking around 1-2 hours per sample, but note that faster acquisition methods are in development. If validated in larger, more diverse patient cohorts, this type of testing could shorten the amount of time from biopsy to a patient receiving targeted treatment.
“We are at the stage of compelling proof of concept with strong performance on tissue samples. The next essential step is prospective clinical validation, testing the approach on samples collected in real time within clinical pathways, and demonstrating that it performs consistently and integrates practically into NHS laboratory workflows,” says Akram.
“In parallel, we are actively exploring the extension of this platform to other cancer types and additional targetable mutations, and investigating how FLIM can be integrated into existing clinical imaging infrastructure.”
The post Specialist Microscopy and AI Analysis Can Improve Lung Cancer Testing appeared first on Inside Precision Medicine.