Dr. Arcangelo Sebastianelli Profile

Dr. Arcangelo Sebastianelli

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Proceedings Article | 3 April 2020 Paper

A multimodal approach for the diagnosis of bladder tumours

Enrico Baria, Simone Morselli, Riccardo Fantechi, Andrea Liaci, Arcangelo Sebastianelli, Mauro Gacci, Sergio Serni, Marco Carini, Riccardo Cicchi, Francesco Pavone

Proceedings Volume 11362, 113620T (2020) https://doi.org/10.1117/12.2555757

KEYWORDS: Raman spectroscopy, Bladder, Tissues, Biopsy, Fluorescence spectroscopy, Solids, Spectroscopy, Diffuse reflectance spectroscopy, Reflectivity

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Urothelial carcinoma (UC) is the most common type of bladder cancer, and its treatment depends from both tumour invasiveness (stage) and aggressiveness (grade). The gold standard for detecting UC is white-light cystoscopy, followed by tissue biopsy and histopathological examination; however, such process is invasive, time-consuming, operatordependent and prone to sampling errors. In this framework, optical spectroscopy techniques could be a promising solution for fast and label-free diagnosis of bladder tissues and for early detection of UC. Thus, we combined autofluorescence, diffuse reflectance and Raman spectroscopy in a compact and transportable setup based on an optical fibrebundle probe. This experimental setup was used for studying fresh biopsies of urothelial tumour (140 samples) and healthy bladder (50 samples) collected from 90 patients undergoing Transurethral Resection of Bladder Tumours (TURBT). The aim of this study was to develop an automated classification of the examined tissues based on the intrinsic spectral information provided by all three techniques. We found that healthy and diseased tissues showed significant spectral differences for each technique, resulting in high accuracy (up to 90%) from a Linear Discriminant Analysis (LDA) routine. In particular, fluorescence spectroscopy – excited either with blue or UV light – provided very good results in detecting UC. However, tumour grading and staging proved to be more challenging tasks, for which no single spectroscopic technique could provide sufficient sensitivity and specificity. Therefore, we found that a multimodal approach can improve significantly the diagnosis of UC stages and grades.

Proceedings Article | 19 July 2019 Paper

Label-free spectroscopic diagnosis of urothelial carcinoma

Enrico Baria, Simone Morselli, Suresh Anand, Riccardo Fantechi, A. Sebastianelli, Mauro Gacci, Sergio Serni, Riccardo Cicchi, Francesco Pavone

Proceedings Volume 11073, 1107305 (2019) https://doi.org/10.1117/12.2526855

KEYWORDS: Raman spectroscopy, Spectroscopy, Tissues, Bladder, Principal component analysis, Diffuse reflectance spectroscopy, Fluorescence spectroscopy, Biopsy, Semiconductor lasers

Read Abstract +

The most common type of bladder cancer is urothelial carcinoma (UC), whose treatment depends from both tumour extension (stage) and aggressiveness (grade). The gold standard for detecting UC is white-light cystoscopy, followed by tissue biopsy and pathological examination for determining tumour stage and grade. However, such process is invasive, time-consuming and prone to sampling errors. In this framework, optical spectroscopy techniques provide fast, label-free and non-invasive alternatives to standard histopathology. Thus, we combined auto-fluorescence, diffuse reflectance and Raman spectroscopy in a compact and transportable setup based on an optical fibre-probe. The latter was coupled to three laser diodes (emitting at 378 nm, 445 nm and 785 nm) and to a halogen lamp for exciting and collecting autofluorescence, Raman and reflectance spectra, respectively. This experimental setup was used for studying fresh biopsies of urothelial tumour (103 samples) and healthy bladder (34 samples) collected from 63 patients undergoing Transurethral Resection of Bladder Tumours (TURBT). All spectral recordings were done within 30 minutes from surgical resection, and optical inspection required less than 2 minutes for each sample. The recorded data were analysed using Principal Component Analysis (PCA) for obtaining an automated classification of the examined samples based on the intrinsic spectral information provided by all three techniques. We found that multimodal spectroscopy provides high-sensitivity, high-specificity discriminating capability for UC detection, grading and staging. The presented strategy generates results similar to gold standard histology, but in a fast and label-free way, offering the potential for endoscopic in vivo applications.

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