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. 2023 Apr;17(2):339-349.
doi: 10.1007/s12072-022-10444-2. Epub 2022 Nov 11.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis

Feng Gao #  1 , De-Chan Lu #  2 , Tian-Lei Zheng #  3   4 , Shi Geng  3 , Jun-Cheng Sha  5 , Ou-Yang Huang  6 , Liang-Jie Tang  6 , Pei-Wu Zhu  7 , Yang-Yang Li  8 , Li-Li Chen  6 , Giovanni Targher  9 , Christopher D Byrne  10 , Zu-Fang Huang #  11 , Ming-Hua Zheng #  12   13   14 ; CHESS-MAFLD consortium
Affiliations

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis

Feng Gao et al. Hepatol Int. 2023 Apr.

Abstract

Background/purpose of the study: There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH.

Methods: Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module.

Results: Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups.

Conclusions: Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Keywords: NAFLD (non-alcoholic fatty liver disease); NASH (non-alcoholic steatohepatitis); Raman; SERS (surface-enhanced Raman spectroscopy).

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Conflict of interest statement

Feng Gao, De-Chan Lu, Tian-Lei Zheng, Shi Geng, Jun-Cheng Sha, Ou-Yang Huang, Liang-Jie Tang, Pei-Wu Zhu, Yang-Yang Li, Li-Li Chen, Giovanni Targher, Christopher D. Byrne, Zu-Fang Huang, Ming-Hua Zheng: nothing to declare.

Figures

Fig. 1
Fig. 1
Schematic illustration of combining silver nanoparticle-based SERS spectroscopy with fully connected neural network analysis to differentiate NASH from NAFL. a Collection of spectroscopic data of serum by SERS; b Collection of histology data by ultrasound-guided liver biopsy; c In the validation cohort, using machine learning to differentiate NAFL from NASH patients
Fig. 2
Fig. 2
Diagnostic performance of the SERS analysis for the non-invasive diagnosis of NASH. a comparison of the mean spectrum for NAFL blood serum samples (black line) versus NASH blood serum samples (red line). b heat map of the association between NASH and selected SERS bands. c AUROC of the validation cohort, and d calibration curve of the validation cohort
See this image and copyright information in PMC

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