A group of researchers from the CIRI beamline in their latest publication entitled Pancreatic intraepithelial neoplasia detection and duct pathology grading using FT-IR imaging and machine learning published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy presented the results of their PanIN classification method, which provides opportunities for early recognition of changes in the cells lining the pancreatic ducts using infrared and machine learning.
Pancreatic intraepithelial neoplasia (PanIN) manifests itself by changes in the cells lining the pancreatic ducts. It is an early pre-cancerous lesion divided into low-grade and high-grade PanIN. In particular, high-grade PanIN is a lesion that often leads to Pancreatic ductal adenocarcinoma (PDAC). In the case of pancreatic cancer, due to the lack of characteristic symptoms of the disease in its early stage, patient survival is low. The basic examination performed to diagnose the disease is to take a fine-needle biopsy from the patient. The most common method of treatment is to remove part of the tissue affected by cancer, which increases the patient’s chances of survival, especially if it is done at an early stage of the disease. Therefore, it is so important to understand the biochemistry of lesions such as PanIN and their progression to cancer.
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Image : Scheme of sample collection (figure upper part), and FT-IR imaged TMA processing using: Random Forest classification (figure middle part), PLS Regression (figure bottom part).