Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma.

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.

Raman spectroscopy and U-Net deep neural network in antiresorptive drug-related osteonecrosis of the jaw.

OBJECTIVE: Application of an optical method for the identification of antiresorptive drug-related osteonecrosis of the jaw (ARONJ). METHODS: We introduce shifted-excitation Raman difference spectroscopy followed by U-Net deep neural network refinement to determine bone tissue viability. The obtained results are validated through established histological methods. RESULTS: Discrimination of osteonecrosis from physiological tissues was evaluated at 119 distinct measurement loci in 40 surgical specimens from 28 patients. Mean Raman spectra were refined from 11,900 raw spectra, and characteristic peaks were assigned to their respective molecular origin. Then, following principal component and linear discriminant analyses, osteonecrotic lesions were distinguished from physiological tissue entities, such as viable bone, with a sensitivity, specificity, and overall accuracy of 100%. Moreover, bone mineral content, quality, maturity, and crystallinity were quantified, revealing an increased mineral-to-matrix ratio and decreased carbonate-to-phosphate ratio in ARONJ lesions compared to physiological bone. CONCLUSION: The results demonstrate feasibility with high classification accuracy in this collective. The differentiation was determined by the spectral features of the organic and mineral composition of bone. This merely optical, noninvasive technique is a promising candidate to ameliorate both the diagnosis and treatment of ARONJ in the future.

Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy.

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.