Research for accurate auxiliary diagnosis of lung cancer based on intracellular fluorescent fingerprint information

The distinctions in pathological types and genetic subtypes of lung cancer have a direct impact on the choice of treatment choices and clinical prognosis in clinical practice. This study used pathological histological sections of surgically removed or biopsied tumor tissue from 36 patients. Based on a small sample size, millions of spectral data points were extracted to investigate the feasibility of employing intracellular fluorescent fingerprint information to diagnose the pathological types and mutational status of lung cancer. The intracellular fluorescent fingerprint information revealed the EGFR gene mutation characteristics in lung cancer, and the area under the curve (AUC) value for the optimal model was 0.98. For the classification of lung cancer pathological types, the macro average AUC value for the ensemble‐learning model was 0.97. Our research contributes new idea for pathological diagnosis of lung cancer and offers a quick, easy, and accurate auxiliary diagnostic approach. The spectral fingerprint information within lung cancer cells is captured using hyperspectral imaging, and then spatial and spectral information is extracted pixel by pixel to form a new two‐dimensional image, which is then input into different algorithmic models for identification and classification.