High-Precision Intelligent Diagnosis of Pancreatic Cancer: Flowing Diffuseness from Single to Whole

Raman spectroscopy, as a label-free optical technique, provides a unique solution for tissue diagnosis. However, due to the limitation of point-by-point acquisition mode and multivariate statistical analysis methods, conventional methods pose a major bottleneck toward achieving highly time-efficient, accurate, and holistic diagnosis. Here, the authors establish line-scan Raman spectrochemical holistic analysis (LRSHA), an intelligent diagnostic method for rapid Raman data collection and holistic tissue diagnosis. The line-scan technique is first used for rapid spectral acquisition (∼15 s) in a tissue block area (0.75 × 0.5 mm²), which is 2 orders of magnitude faster than conventional methods. Then, the one-dimensional (1D) Raman spectra are converted into two-dimensional (2D) Raman encoding figures by the spectral recurrence plot transformation. The 2D deep learning models achieve 96.0% accuracy, 7% higher than that of 1D deep learning models. Moreover, the neighborhood enhancement method is applied to correct the initial deep learning results, like ripple flowing diffuseness from single to whole, which ultimately greatly improves the diagnostic accuracy to 99.7%. We also demonstrated that our method can identify tissue neoplasia at resection margins that appear nearly normal to the naked eye. Together, the LRSHA method shows valuable potential for rapid, efficient, and accurate tissue diagnosis.