Label-free classification of hepatocellular-carcinoma grading using second harmonic generation microscopy

Abstract

The clear and accurate understanding of the degree of hepatocellular-carcinoma (HCC) differentiation plays a key role in the determination of the patient prognosis and development of a treatment plan by the clinician. However, label-free and automated classification of the HCC grading is challenging. Here, we demonstrate second-harmonic generation (SHG) microscopy for label-free classification of HCC grading in paraffin-embedded specimens. A total of 217 images from 113 patients were obtained using SHG microscopy, and the SHG signals from the collagen within the tumor were analyzed using feature extraction and selection, the Mann-Whitney test, and the receiver operating characteristic curves. The results exhibit good correlation between the software analysis and the diagnosis by experienced pathologists. Combining the image features and clinical information, an adaptive quantification algorithm is generated for automatically determining the HCC grade. The results suggest that SHG microscopy might be a promising automated diagnostic method for clinical use, without requiring time for tissue processing and staining.