Can videostroboscopy predict early glottic cancer? A systematic review and meta-analysis

Mehlum, Camilla Slot; Rosenberg, Tine; Groentved, Aagot Moeller; Dyrvig, Anne-Kirstine; Godballe, Christian

doi:10.1002/lary.25745

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…19 Additionally, a recent meta-analysis showed that only 65% of the tested population was correctly identified as not having cancer by ENT doctors, even with videostroboscopy. 8 Generally speaking, an automated system could help physicians make more confident determinations to exclude the malignant lesion, reducing the burden of endoscopists and patients' waiting time, while it could raise red flags when a patient tests positive, reminding doctors to complete further clinical investigation to determine if the patient is truly positive. However, two recent studies using the CNN-based technique obtained over 90% accuracy for malignancy lesions detection, which outperformed our classifier.…”

Section: Discussionmentioning

confidence: 99%

“…High levels of diagnostic inconsistency have been observed, even among experts. 8 Today, artificial intelligence (AI) has achieved outstanding performance within medical imaging interpretation and triage tasks and has been successfully used to diagnose skin cancer, 9 lung cancer, 10 glioma, 11 and breast histopathology. 12 As a popular technique of deep-learning algorithms, region-based convolutional neural networks (R-CNNs) proposed an efficient method in object detection that utilizes a feature map from a convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

Yan

Zhou

et al. 2021

Preprint

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OBJECTIVE Little is known about the efficacy of using artificial intelligence to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicenter study was aimed to establish an artificial intelligence system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study Design: Multicentre case-control study Setting: Six tertiary care centers Participants: The laryngoscopy images were collected from 2179 patients with vocal lesions. Outcome Measures: An automatic detection system of laryngeal carcinoma was established based on Faster R-CNN, which was used to distinguish vocal malignant and benign lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathology was the gold standard to identify malignant and benign vocal lesions. Results: Among 89 cases of the malignant group, the classifier was able to evaluate the laryngeal carcinoma in 66 patients (74.16%, sensitivity), while the classifier was able to assess the benign laryngeal lesion in 503 cases among 640 cases of the benign group (78.59%, specificity). Furthermore, the CNN-based classifier achieved an overall accuracy of 78.05% with a 95.63% negative prediction for the testing dataset. Conclusion: This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis, which may improve and standardize the diagnostic capacity of endoscopists using different laryngoscopes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

Yan

Zhou

et al. 2021

Preprint

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“…Vocal fold carcinoma often develops through a multistage process where normal mucosa transforms into premalignant lesions with increasing malignant potential (laryngeal intraepithelial neoplasia; LIN) finally resulting in invasive malignant carcinoma. 1,2 Current presurgical non-invasive diagnostic techniques provide valuable information of the lesions, but is not always able to distinguish LIN or early glottic cancer from non-neoplastic lesions (NNL), 3,4 eg hyperkeratosis without dysplasia or chronic inflammation [5][6][7] and no malignant potential. 2 Various treatment strategies have been, 3 and still are, 8 applied for the treatment of LIN and early glottic cancer.…”

Section: Introductionmentioning

confidence: 99%

Vocal Outcome After Cordectomy by Transoral CO2 Laser Microsurgery in Patients With Laryngeal Intraepithelial Neoplasia and Non-neoplastic Lesions

et al. 2024

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“…High levels of diagnostic inconsistency have been observed, even among experts. 8 Today, artificial intelligence (AI) has achieved outstanding performance in medical imaging interpretation and triage tasks. It has been successfully used to diagnose skin cancer, 9 lung cancer, 10 glioma 11 and breast histopathology.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, misdiagnoses and missed diagnoses are not rare when only using a laryngoscope. High levels of diagnostic inconsistency have been observed, even among experts 8 …”

Section: Introductionmentioning

confidence: 99%

Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network

Yan

Zhou

et al. 2023

Clinical Otolaryngology

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Objective Little is known about the efficacy of using artificial intelligence (AI) to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicentre study aimed to establish an AI system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study design Multicentre case–control study. Setting Six tertiary care centres. Participants Laryngoscopy images were collected from 2179 patients with vocal fold lesions. Outcome measures An automatic detection system of laryngeal carcinoma was established and used to distinguish malignant and benign vocal lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathological examination was the gold standard for identifying malignant and benign vocal lesions. Results Out of 89 cases in the malignant group, the classifier was able to correctly identify laryngeal carcinoma in 66 patients (74.16%, sensitivity). Out of 640 cases in the benign group, the classifier was able to accurately assess the laryngeal lesion in 503 cases (78.59%, specificity). Furthermore, the region‐based convolutional neural network (R‐CNN) classifier achieved an overall accuracy of 78.05%, with a 95.63% negative predictive value and a 32.51% positive predictive value for the testing data set. Conclusion This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis which may improve and standardise the diagnostic capacity of laryngologists using different laryngoscopes.

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Can videostroboscopy predict early glottic cancer? A systematic review and meta-analysis

Abstract: NA Laryngoscope, 126:2079-2084, 2016.

Cited by 14 publications

References 19 publications

Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

Vocal Outcome After Cordectomy by Transoral CO2 Laser Microsurgery in Patients With Laryngeal Intraepithelial Neoplasia and Non-neoplastic Lesions

Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network

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