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Sampieri C, Azam MA, Ioppi A, Baldini C, Moccia S, Kim D, Tirrito A, Paderno A, Piazza C, Mattos LS, Peretti G. Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning. Laryngoscope 2024; 134:2826-2834. [PMID: 38174772 DOI: 10.1002/lary.31255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos. METHODS A retrospective study was conducted extracting and annotating white light (WL) and Narrow-Band Imaging (NBI) frames to train a segmentation model (SegMENT-Plus). Two external datasets were used for validation. The model's performances were compared with those of two otolaryngology residents. In addition, the model was tested on real intraoperative laryngoscopy videos. RESULTS A total of 3933 images of laryngeal cancer from 557 patients were used. The model achieved the following median values (interquartile range): Dice Similarity Coefficient (DSC) = 0.83 (0.70-0.90), Intersection over Union (IoU) = 0.83 (0.73-0.90), Accuracy = 0.97 (0.95-0.99), Inference Speed = 25.6 (25.1-26.1) frames per second. The external testing cohorts comprised 156 and 200 images. SegMENT-Plus performed similarly on all three datasets for DSC (p = 0.05) and IoU (p = 0.07). No significant differences were noticed when separately analyzing WL and NBI test images on DSC (p = 0.06) and IoU (p = 0.78) and when analyzing the model versus the two residents on DSC (p = 0.06) and IoU (Senior vs. SegMENT-Plus, p = 0.13; Junior vs. SegMENT-Plus, p = 1.00). The model was then tested on real intraoperative laryngoscopy videos. CONCLUSION SegMENT-Plus can accurately delineate laryngeal cancer boundaries in endoscopic images, with performances equal to those of two otolaryngology residents. The results on the two external datasets demonstrate excellent generalization capabilities. The computation speed of the model allowed its application on videolaryngoscopies simulating real-time use. Clinical trials are needed to evaluate the role of this technology in surgical practice and resection margin improvement. LEVEL OF EVIDENCE III Laryngoscope, 134:2826-2834, 2024.
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Affiliation(s)
- Claudio Sampieri
- Department of Experimental Medicine (DIMES), University of Genova, Genoa, Italy
- Functional Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Otorhinolaryngology Department, Hospital Clínic, Barcelona, Spain
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genova, Genoa, Italy
| | - Alessandro Ioppi
- Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genova, Genoa, Italy
- Department of Otorhinolaryngology-Head and Neck Surgery, "S. Chiara" Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Chiara Baldini
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genova, Genoa, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Dahee Kim
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Alessandro Tirrito
- Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genova, Genoa, Italy
| | - 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, Brescia, 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, Brescia, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Giorgio Peretti
- Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genova, Genoa, Italy
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