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Paderno A, Villani FP, Fior M, Berretti G, Gennarini F, Zigliani G, Ulaj E, Montenegro C, Sordi A, Sampieri C, Peretti G, Moccia S, Piazza C. Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes. Acta Otorhinolaryngol Ital 2023; 43:283-290. [PMID: 37488992 PMCID: PMC10366566 DOI: 10.14639/0392-100x-n2336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/08/2023] [Indexed: 07/26/2023]
Abstract
Objective To achieve instance segmentation of upper aerodigestive tract (UADT) neoplasms using a deep learning (DL) algorithm, and to identify differences in its diagnostic performance in three different sites: larynx/hypopharynx, oral cavity and oropharynx. Methods A total of 1034 endoscopic images from 323 patients were examined under narrow band imaging (NBI). The Mask R-CNN algorithm was used for the analysis. The dataset split was: 935 training, 48 validation and 51 testing images. Dice Similarity Coefficient (Dsc) was the main outcome measure. Results Instance segmentation was effective in 76.5% of images. The mean Dsc was 0.90 ± 0.05. The algorithm correctly predicted 77.8%, 86.7% and 55.5% of lesions in the larynx/hypopharynx, oral cavity, and oropharynx, respectively. The mean Dsc was 0.90 ± 0.05 for the larynx/hypopharynx, 0.60 ± 0.26 for the oral cavity, and 0.81 ± 0.30 for the oropharynx. The analysis showed inferior diagnostic results in the oral cavity compared with the larynx/hypopharynx (p < 0.001). Conclusions The study confirms the feasibility of instance segmentation of UADT using DL algorithms and shows inferior diagnostic results in the oral cavity compared with other anatomic areas.
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Affiliation(s)
- Alberto Paderno
- Unit of Otorhinolaryngology, Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | | | - Milena Fior
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Giulia Berretti
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Francesca Gennarini
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Gabriele Zigliani
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Emanuela Ulaj
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Claudia Montenegro
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Alessandra Sordi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Claudio Sampieri
- Unit of Otorhinolaryngology, Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giorgio Peretti
- Unit of Otorhinolaryngology, Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sara Moccia
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Cesare Piazza
- Unit of Otorhinolaryngology, Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
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