Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning.
artificial intelligence
laryngeal cancer
laryngoscopy
narrow-band imaging
segmentation
white light imaging
Journal
The Laryngoscope
ISSN: 1531-4995
Titre abrégé: Laryngoscope
Pays: United States
ID NLM: 8607378
Informations de publication
Date de publication:
04 Jan 2024
04 Jan 2024
Historique:
revised:
05
12
2023
received:
16
06
2023
accepted:
11
12
2023
medline:
4
1
2024
pubmed:
4
1
2024
entrez:
4
1
2024
Statut:
aheadofprint
Résumé
To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos. 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. 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. 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. III Laryngoscope, 2024.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.
Références
Baird BJ, Sung CK, Beadle BM, Divi V. Treatment of early-stage laryngeal cancer: a comparison of treatment options. Oral Oncol. 2018;87:8-16.
Fiz I, Koelmel JC, Sittel C. Nature and role of surgical margins in transoral laser microsurgery for early and intermediate glottic cancer. Curr Opin Otolaryngol Head Neck Surg. 2018;26(2):78-83.
Gorphe P, Simon C. A systematic review and meta-analysis of margins in transoral surgery for oropharyngeal carcinoma. Oral Oncol. 2019;98:69-77.
Vilaseca I, Valls-Mateus M, Nogués A, et al. Usefulness of office examination with narrow band imaging for the diagnosis of head and neck squamous cell carcinoma and follow-up of premalignant lesions. Head Neck. 2017;39(9):1854-1863.
Garofolo S, Piazza C, del Bon F, et al. Intraoperative narrow band imaging better delineates superficial resection margins during transoral laser microsurgery for early glottic cancer. Ann Otol Rhinol Laryngol. 2015;124(4):294-298.
Sampieri C, Baldini C, Azam MA, et al. State of the art review artificial intelligence for upper aerodigestive tract endoscopy and laryngoscopy: a guide for physicians and state-of-the-art review. Otolaryngol Head Neck Surg. 2023;2023(00):1-19.
Azam MA, Sampieri C, Ioppi A, et al. Deep learning applied to white light and narrow band imaging Videolaryngoscopy: toward real-time laryngeal cancer detection. Laryngoscope. 2022;132(9):1798-1806.
Dunham ME, Kong KA, Mcwhorter AJ, Adkins LK. Optical biopsy: automated classification of airway endoscopic findings using a convolutional neural network. Published online 2020.
Paderno A, Piazza C, Del Bon F, et al. Deep learning for automatic segmentation of Oral and oropharyngeal cancer using narrow band imaging: preliminary experience in a clinical perspective. Front Oncol. 2021;11:11.
Azam MA, Sampieri C, Ioppi A, et al. Videomics of the upper aero-digestive tract cancer: deep learning applied to white light and narrow band imaging for automatic segmentation of endoscopic images. Front Oncol. 2022;12:12.
Computer Vision Annotation Tool.
Yue G, Zhuo G, Li S, et al. Benchmarking polyp segmentation methods in narrow-band imaging colonoscopy images. IEEE J Biomed Health Inform Published Online July 1. 2023;27:3360-3371.
Ali S, Zhou F, Braden B, et al. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Sci Rep. 2020;10(1):1-15.
Yang XX, Li Z, Shao XJ, et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig Endosc. 2021;33(7):1075-1084.
Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018;2(10):741-748.
Fiz I, Mazzola F, Fiz F, et al. Impact of close and positive margins in transoral laser microsurgery for TIS-T2 glottic cancer. Front Oncol. 2017;7(OCT):1-9.
Sampieri C, Costantino A, Spriano G, Peretti G, de Virgilio A, Kim SH. Role of surgical margins in transoral robotic surgery: a question yet to be answered. Oral Oncol. 2022;133:106043.
de Kleijn BJ, Heldens GTN, Herruer JM, et al. Intraoperative imaging techniques to improve surgical resection margins of oropharyngeal squamous cell cancer: a comprehensive review of current literature. Cancers (Basel). 2023;15(3):1-25.
Lin YC, Watanabe A, Chen WC, Lee KF, Lee IL, Wang WH. Narrowband imaging for early detection of malignant tumors and radiation effect after treatment of head and neck cancer. Arch Otolaryngol Head Neck Surg. 2010;136(3):234-239.
Rosenthal EL. Optical imaging of head and neck cancer: opportunities and challenges. JAMA Otolaryngol Head Neck Surg. 2014;140(2):93-94.
Zwakenberg MA, Westra JM, Halmos GB, Wedman J, van der Laan BFAM, Plaat BEC. Narrow-band imaging in transoral laser surgery for early glottic cancer: a randomized controlled trial. Otolaryngol Head Neck Surg (United States). Published online. 2023;39(7):1343-1348.
Valls-Mateus M, Nogués-Sabaté A, Blanch JL, Bernal-Sprekelsen M, Avilés-Jurado FX, Vilaseca I. Narrow band imaging for head and neck malignancies: lessons learned from mistakes. Head Neck. 2018;40(6):1164-1173.
Paderno A, Villani FP, Fior M, et al. Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes. Acta Otorhinolaryngol Ital. 2023;43(4):283-290.
Ji B, Ren J, Zheng X, et al. A multi-scale recurrent fully convolution neural network for laryngeal leukoplakia segmentation. Biomed Signal Process Control. 2020;59:101913.