The clinical diagnostic value of target biopsy using narrow-band imaging endoscopy and accurate laryngeal carcinoma pathologic specimen acquisition

Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201's performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model.

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

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.

Use of narrowband imaging for the diagnosis and screening of laryngeal cancer: A systematic review and meta-analysis

We evaluated the diagnostic accuracy of narrowband imaging (NBI) in terms of detecting laryngeal cancer compared to that of white light endoscopy (WLE). Two reviewers individually searched the six databases for studies published between the first record date and December 31, 2019. We recorded the numbers of true positives, true negatives, false positives, and false negatives. Quality Assessment of Diagnostic Accuracy Studies ver. 2 software was used to assess the studies. The extent of the inter-rater agreement was also measured. The diagnostic odds ratio (OR) associated with NBI was 87.463 (95% confidence interval [CI]: 46.968, 160.873). The area under the summary receiver operating characteristic curve was 0.954. NBI was more diagnostically accurate than WLE, which was associated with a diagnostic OR of 13.750. NBI affords high diagnostic accuracy, thus supporting a role for NBI in the diagnostic work-up of laryngeal cancer.

Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images

OBJECTIVE

To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.

METHODS

A DCNN-based diagnostic system was constructed and trained using 13,721 laryngoscopic images of LCA, precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT) and normal tissues (NORM) from 2 tertiary hospitals in China, including 2293 from 206 LCA subjects, 1807 from 203 PRELCA subjects, 6448 from 774 BLT subjects and 3191 from 633 NORM subjects. An independent test set of 1176 laryngoscopic images from other 3 tertiary hospitals in China, including 132 from 44 LCA subjects, 129 from 43 PRELCA subjects, 504 from 168 BLT subjects and 411 from 137 NORM subjects, was applied to the constructed DCNN to evaluate its performance against experienced endoscopists.

RESULTS

The DCCN achieved a sensitivity of 0.731, a specificity of 0.922, an AUC of 0.922, and the overall accuracy of 0.867 for detecting LCA and PRELCA among all lesions and normal tissues. When compared to human experts in an independent test set, the DCCN' s performance on detection of LCA and PRELCA achieved a sensitivity of 0.720, a specificity of 0.948, an AUC of 0.953, and the overall accuracy of 0.897, which was comparable to that of an experienced human expert with 10-20 years of work experience. Moreover, the overall accuracy of DCNN for detection of LCA was 0.773, which was also comparable to that of an experienced human expert with 10-20 years of work experience and exceeded the experts with less than 10 years of work experience.

CONCLUSIONS

The DCNN has high sensitivity and specificity for automated detection of LCA and PRELCA from BLT and NORM in laryngoscopic images. This novel and effective approach facilitates earlier diagnosis of early LCA, resulting in improved clinical outcomes and reducing the burden of endoscopists.

Pseudoepitheliomatous Hyperplasia of the Larynx Requiring Total Laryngectomy

Patient: Male, 57

Final Diagnosis: Pseudoepitheliomatous hyperplasia

Symptoms: Dysphonia

Medication: —

Clinical Procedure: Progressive speudoepitheliomaous hyperplasia spreaded

Specialty: Oncology