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Background and Objective: Skin cancer is the most common malignancy in whites accounting for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups for skin cancer "optical biopsy" are utilized to detect tumors based on their spectral features caused by the comparative presence of different chemical components. However, low signal-to-noise ratio in such systems may prevent accurate tumors classification. Thus, there is a challenge to develop methods for efficient skin tumors classification., Methods: We compare the performance of convolutional neural networks and the projection on latent structures with discriminant analysis for discriminating skin cancer using the analysis of Raman spectra with a high autofluorescence background stimulated by a 785 nm laser. We have registered the spectra of 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable Raman setup and created classification models both for convolutional neural networks and projection on latent structures approaches. To check the classification models stability, a 10-fold cross-validation was performed for all created models. To avoid models overfitting, the data was divided into a training set (80% of spectral dataset) and a test set (20% of spectral dataset)., Results: The results for different classification tasks demonstrate that the convolutional neural networks significantly (p<0.01) outperforms the projection on latent structures. For the convolutional neural networks implementation we obtained ROC AUCs of 0.96 (0.94 - 0.97; 95% CI), 0.90 (0.85-0.94; 95% CI), and 0.92 (0.87 - 0.97; 95% CI) for classifying a) malignant vs benign tumors, b) melanomas vs pigmented tumors and c) melanomas vs seborrheic keratosis respectively., Conclusions: The performance of the convolutional neural networks classification of skin tumors based on Raman spectra analysis is higher or comparable to the accuracy provided by trained dermatologists. The increased accuracy with the convolutional neural networks implementation is due to a more precise accounting of low intensity Raman bands in the intense autofluorescence background. The achieved high performance of skin tumors classifications with convolutional neural networks analysis opens a possibility for wide implementation of Raman setups in clinical setting., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interests., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Dermatofibrosarcoma protuberans is a rare disease and this pathology provokes insufficient oncological alertness among clinicians. A possible way to increase the accuracy of early diagnosis of rare skin neoplasms is "optical biopsy" using Raman spectroscopy tissue response. This case report of a 32-year-old woman with a dermatofibrosarcoma protuberans demonstrates that Raman spectroscopy based "optical biopsy" can help to diagnose rare tumors., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10-fold cross-validation was performed. We obtained ROC AUCs of 0.75 (0.71-0.79; 95% CI), 0.69 (0.63-0.76; 95% CI) and 0.81 (0.74-0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy- and spectroscopy-based computer-assisted diagnosis systems to increase accuracy of neoplasms classification., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

This study is an evaluation of surgical treatment results of primary retroperitoneal tumors. In Samara Regional Clinical Oncology Dispensary, from 2008 to 2015, the treatment of 187 patients (53 men and 134 women) was conducted. One hundred fifteen patients got tumor removal within the healthy tissue (R0), and 61 patients went through complete resection of tumor with wide margins (R0). Complete resection of tumor with wide margins (R0) with preoperative tumor vessel embolization was performed in 11 patients. According to the histological examination, malignant retroperitoneal tumor was detected in 85 patients (48.4%); in most cases it was presented by various forms of sarcoma. A benign tumor was diagnosed in 71 patients (40.3%), fibrolipomas (17.1%), and neurofibromas (12.5%). The diagnosis of 20 patients needs subsequent clarification, as mesenchymal tumor (6.2%) and histiocytoma (5.1%) were diagnosed. Short-term results of surgical treatment for the group, where complete resection of tumor with wide margins was performed: intraoperative blood loss 410.91 + - 113.31(ml), operation time 185.15 + -32.49(min); postoperative complications 10 (16,4%); mortality 3 (4,9%); LOS 23,14 ± 6,31; for removal of the tumor within healthy tissues : intraoperative blood loss 281.33 + -110.94 (ml), operation time 58.33 + -27.14(min) postoperative complications 7 (6,08%); mortality 2 (1,74%); LOS 6,98 ± 4,83; (t = 279, p  = 0,015). For patients who went through preoperative tumor feeding vessel embolization, intraoperative blood loss was 121.33 ± 27.94 (ml), time of operation 43.13 ± 16.11 (min), postoperative complication 1 (4.5%), mortality 0 (0%), and length of stay 12.72 ± 1.49. After the complete resection of tumor with wide margins, intraoperative blood loss, operation time, the number of postoperative complications, and postoperative LOS were significantly greater in comparison with the group of patients where the tumor was removed within healthy tissues. The method of preoperative embolization of the tumor feeding vessels can reduce intraoperative blood loss, the time of operation, and the number of postoperative complications., Competing Interests: Conflict of InterestNot applicable., (© Indian Association of Surgical Oncology 2020.)

The present paper studies the applicability of a portable cost-effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near-infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The differentiation of skin melanomas and basal cell carcinomas (BCCs) was demonstrated based on combined analysis of Raman and autofluorescence spectra stimulated by visible and NIR lasers. It was ex vivo tested on 39 melanomas and 40 BCCs. Six spectroscopic criteria utilizing information about alteration of melanin, porphyrins, flavins, lipids, and collagen content in tumor with a comparison to healthy skin were proposed. The measured correlation between the proposed criteria makes it possible to define weakly correlated criteria groups for discriminant analysis and principal components analysis application. It was shown that the accuracy of cancerous tissues classification reaches 97.3% for a combined 6-criteria multimodal algorithm, while the accuracy determined separately for each modality does not exceed 79%. The combined 6-D method is a rapid and reliable tool for malignant skin detection and classification.

Malignant skin tumors of different types were studied in vivo using optical coherence tomography (OCT), backscattering (BS), and Raman spectroscopy (RS). A multimodal method is proposed for early cancer detection based on complex analysis of OCT images by their relative alteration of scattered-radiation spectral intensities between malignant and healthy tissues. An increase in average accuracy of diagnosis was observed for a variety of cancer types (9% sensitivity, 8% specificity) by a multimodal RS-BS-OCT system in comparison with any of the three methods used separately. The proposed approach equalizes the processing rates for all methods and allows for simultaneous imaging and classification of tumors., (© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE))