Imaging: revolutionizing cancer surgery

A biopsy, which involves removing a small amount of tissue from the body, is the main way for doctors to diagnose most cancers. While less invasive biopsy procedures are preferred, they can yield insufficient samples, resulting in incomplete and/or inconclusive diagnosis. A definitive diagnosis and further analysis such as cancer staging can only be made post-surgery; this long-waited information is then used to guide subsequent treatment decisions.

The STAMP technology overcomes many challenges of this clinical workflow to enable early and informative cancer diagnostics. STAMP uses programmable DNA barcodes to measure billions of protein markers in a single test - the amount as well as the distribution of these protein markers in a cell - from a small clinical sample. Using breast cancer as a model, STAMP achieves a high diagnostic accuracy of above 94 per cent, comparable with gold-standard tissue pathology, and reveals important clinical information which currently can only be obtained through post-surgery tissue analysis - all directly from a fine needle aspiration (FNA) biopsy, the least invasive form of biopsy.

Led by Assistant Professor Shao Huilin from the NUS Institute for Health Innovation & Technology (NUS iHealthtech), the 10-member research team spent over two years to develop STAMP.

"Our STAMP technology leverages the unique properties of DNA to form 3D barcodes. These barcodes can be used to measure diverse protein markers as well as detect the markers' specific locations in cells. By mapping these marker distribution patterns in cells, STAMP can provide an early indication of disease aggressiveness. Current pathology techniques only measure a small subset of protein markers and require several days of extensive processing. In comparison, STAMP is a million times more sensitive, provides highly informative analysis from scarce samples, and can be completed in as little as two hours," said Asst Prof Shao.

The team's technology breakthrough was published by the prestigious scientific journal Nature Biomedical Engineering. The study was also featured in News & Views by the journal and selected as the cover story for its September 2019 issue.

"This is the future, a huge step towards our ultimate goal of revolutionizing surgical oncology," said Parsin Haji Reza, a systems design engineering professor who leads the project. "Intraoperatively, during surgery, the surgeon will be able to see exactly what to cut and how much to cut."

A paper on the work, All-optical Reflection-mode Microscopic Histology of Unstained Human Tissues, was published Sep.t 16 in the journal Scientific Reports.

Doctors now rely primarily on pre-operation MRI images and CT scans, experience and visual inspection to determine the margins of tumors during operations.

Tissue samples are then sent to labs for testing, with waits of up to two weeks for results to show if the tumor was completely removed or not.

In about 10 per cent of cases - the rates for different kinds of cancer involving tumors vary widely - some cancerous tissue has been missed and a second operation is required to remove it.

The photoacoustic technology developed at Waterloo works by sending laser light pulses into targeted tissue, which absorbs them, heats up, expands and produces soundwaves. A second laser reads those soundwaves, which are then processed to determine if the tissue is cancerous or non-cancerous.

The system has already been used to make accurate images of even relatively thick, untreated human tissue samples for the first time ever, a key breakthrough in the development process.

Next steps include imaging fresh tissue samples taken during surgeries, integrating the technology into a surgical microscope and, finally, using the system directly on patients during operations.

"This will have a tremendous impact on the economics of health-care, be amazing for patients and give clinicians a great new tool," said Haji Reza, director of the PhotoMedicine Labs at Waterloo. "It will save a great deal of time and money and anxiety."

Researchers hope to develop a fully functioning system within about two years, a process including the need to clear ethical hurdles and securing regulatory approvals.