Boston: The surgical procedure is frequently not predetermined when a patient has surgery to remove a tumour or treat an illness. The margins of a tumour, its stage, and whether a lesion is malignant or benign must all be known to the surgeon in order to determine how much tissue needs to be removed. These decisions frequently depend on the collection, analysis, and diagnosis of a disease while the patient is on the operating table. Speed and precision are crucial when surgeons send samples to a pathologist for analysis.
Findings are published in Nature Biomedical Engineering.
The current gold-standard method of tissue examination frequently takes too long, and a quicker method that includes freezing tissue may generate artefacts that may make diagnosis more difficult. A more effective method was developed in a recent study by researchers from the Mahmood Lab at the Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, and associates from Bogazici University. The technique uses artificial intelligence to translate between frozen sections and the gold-standard approach, improving the quality of images to increase the accuracy of rapid diagnostics.
“We are using the power of artificial intelligence to address an age-old problem at the intersection of surgery and pathology,” said corresponding author Faisal Mahmood, PhD, of the Division of Computational Pathology at BWH. “Making a rapid diagnosis from frozen tissue samples is challenging and requires specialized training, but this kind of diagnosis is a critical step in caring for patients during surgery.”
For making final diagnoses, pathologists use formalin-fixed and paraffin-embedded (FFPE) tissue samples–this method preserves tissue in a way that produces high-quality images but the process is laborious and typically takes 12 to 48 hours. For a rapid diagnosis, pathologists use an approach known as cryosectioning that involves fast freezing tissue, cutting sections, and observing these thin slices under a microscope. Cryosectioning takes minutes rather than hours but can distort cellular details and compromise or tear delicate tissue.
Mahmood and co-authors developed a deep-learning model that can be used to translate between frozen sections and more commonly used FFPE tissue. In their paper, the team demonstrated that the method could be used to subtype different kinds of cancer, including glioma and non-small-cell lung cancer. The team validated their findings by recruiting pathologists to a reader study in which they were asked to make a diagnosis from images that had gone through the AI method and traditional cryosectioning images. The AI method not only improved image quality but also improved diagnostic accuracy among experts. The algorithm was also tested on independently collected data from Turkey.
The authors note that in the future, prospective clinical studies should be conducted to validate the AI method and determine if it can contribute to diagnostic accuracy and surgical decision-making in real hospital settings.
“Our work shows that AI has the potential to make a time-sensitive, critical diagnosis easier and more accessible to pathologists,” said Mahmood. “And it could potentially be applied to any type of cancer surgery. It opens up many possibilities for improving diagnosis and patient care.”