MICLEAR: Intelligent molecular cytology for intraoperative margin assessment of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly mortal cancer whose only potentially curative treatment is surgical resection. Intraoperative assessment of its surgical margins is vital for patient survival. Frozen section biopsy is routinely performed for this purpose. However, its heavy reliance on pathologists' expertise often leads to diagnostic discrepancies. The inherent invasiveness of PDAC also leads to sampling errors. This study developed an intelligent molecular cytology approach that improves diagnostic objectivity and broadens sampling coverage. Our method, Multi-Instance Cytology with LEArned Raman-embedding (MICLEAR), leverages compositional information from label-free Raman imaging. First, 4085 cells were brushed off from the pancreases of 41 patients and imaged using stimulated Raman scattering microscopy. Then, a contrastive learning-based cell embedding model was developed to compress each cell's morphological and compositional information into a compact cell vector. Finally, a multi-instance learning-based diagnostic model using cell vectors was employed to predict the likelihood that a patient's margin is positive. MICLEAR achieved 80% sensitivity, 100% specificity, and an area under the receiver operating characteristic curve of 0.86 in 27 patients for validation, comprising 10 with positive margins and 17 with negative margins, in approximately 8 minutes per patient. It may hold promise for more efficient and accurate intraoperative assessment of PDAC surgical margins.