Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma
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Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma. / Matthies, Levi; Amir-Kabirian, Hendrik; Gebrekidan, Medhanie T; Braeuer, Andreas S; Speth, Ulrike S; Smeets, Ralf; Hagel, Christian; Gosau, Martin; Knipfer, Christian; Friedrich, Reinhard E.
In: PLOS ONE, Vol. 19, No. 4, e0302017, 2024.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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T1 - Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma
AU - Matthies, Levi
AU - Amir-Kabirian, Hendrik
AU - Gebrekidan, Medhanie T
AU - Braeuer, Andreas S
AU - Speth, Ulrike S
AU - Smeets, Ralf
AU - Hagel, Christian
AU - Gosau, Martin
AU - Knipfer, Christian
AU - Friedrich, Reinhard E
N1 - Copyright: © 2024 Matthies et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024
Y1 - 2024
N2 - In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.
AB - In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.
KW - Humans
KW - Spectrum Analysis, Raman/methods
KW - Artificial Intelligence
KW - Neurofibroma/diagnosis
KW - Neurofibromatosis 1/diagnosis
KW - Skin Neoplasms/diagnosis
KW - Neural Networks, Computer
KW - Neuroma
U2 - 10.1371/journal.pone.0302017
DO - 10.1371/journal.pone.0302017
M3 - SCORING: Journal article
C2 - 38603731
VL - 19
JO - PLOS ONE
JF - PLOS ONE
SN - 1932-6203
IS - 4
M1 - e0302017
ER -