High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development
The aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV). Methods: Case-control study including 100 patients with unilateral vocal fold lesions in c...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/14/3716 |
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| author | Jakub Malinowski Wioletta Pietruszewska Konrad Stawiski Magdalena Kowalczyk Magda Barańska Aleksander Rycerz Ewa Niebudek-Bogusz |
| author_facet | Jakub Malinowski Wioletta Pietruszewska Konrad Stawiski Magdalena Kowalczyk Magda Barańska Aleksander Rycerz Ewa Niebudek-Bogusz |
| author_sort | Jakub Malinowski |
| collection | DOAJ |
| description | The aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV). Methods: Case-control study including 100 patients with unilateral vocal fold lesions in comparison to 38 normophonic subjects. Quantitative assessment with the determination of vocal fold oscillation parameters was performed based on HSV kymography. Machine-learning predictive models were developed and validated. Results: All calculated parameters differed significantly between healthy subjects and patients with organic lesions. The first predictive model distinguishing any organic lesion patients from healthy subjects reached an area under the curve (AUC) equal to 0.983 and presented with 89.3% accuracy, 97.0% sensitivity, and 71.4% specificity on the testing set. The second model identifying malignancy among organic lesions reached an AUC equal to 0.85 and presented with 80.6% accuracy, 100% sensitivity, and 71.1% specificity on the training set. Important predictive factors for the models were frequency perturbation measures. Conclusions: The standard protocol for distinguishing between benign and malignant lesions continues to be clinical evaluation by an experienced ENT specialist and confirmed by histopathological examination. Our findings did suggest that advanced machine learning models, which consider the complex interactions present in HSV data, could potentially indicate a heightened risk of malignancy. Therefore, this technology could prove pivotal in aiding in early cancer detection, thereby emphasizing the need for further investigation and validation. |
| first_indexed | 2024-03-11T01:12:33Z |
| format | Article |
| id | doaj.art-53a4e8e8f9ab465990ec1286e89f9684 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T01:12:33Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-53a4e8e8f9ab465990ec1286e89f96842023-11-18T18:43:08ZengMDPI AGCancers2072-66942023-07-011514371610.3390/cancers15143716High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model DevelopmentJakub Malinowski0Wioletta Pietruszewska1Konrad Stawiski2Magdalena Kowalczyk3Magda Barańska4Aleksander Rycerz5Ewa Niebudek-Bogusz6Department of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USADepartment of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Biostatistics and Translational Medicine, Medical University of Lodz, 90-419 Lodz, PolandDepartment of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-419 Lodz, PolandThe aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV). Methods: Case-control study including 100 patients with unilateral vocal fold lesions in comparison to 38 normophonic subjects. Quantitative assessment with the determination of vocal fold oscillation parameters was performed based on HSV kymography. Machine-learning predictive models were developed and validated. Results: All calculated parameters differed significantly between healthy subjects and patients with organic lesions. The first predictive model distinguishing any organic lesion patients from healthy subjects reached an area under the curve (AUC) equal to 0.983 and presented with 89.3% accuracy, 97.0% sensitivity, and 71.4% specificity on the testing set. The second model identifying malignancy among organic lesions reached an AUC equal to 0.85 and presented with 80.6% accuracy, 100% sensitivity, and 71.1% specificity on the training set. Important predictive factors for the models were frequency perturbation measures. Conclusions: The standard protocol for distinguishing between benign and malignant lesions continues to be clinical evaluation by an experienced ENT specialist and confirmed by histopathological examination. Our findings did suggest that advanced machine learning models, which consider the complex interactions present in HSV data, could potentially indicate a heightened risk of malignancy. Therefore, this technology could prove pivotal in aiding in early cancer detection, thereby emphasizing the need for further investigation and validation.https://www.mdpi.com/2072-6694/15/14/3716glottis organic pathologyglottic cancerhigh-speed videoendoscopykymographymachine learning |
| spellingShingle | Jakub Malinowski Wioletta Pietruszewska Konrad Stawiski Magdalena Kowalczyk Magda Barańska Aleksander Rycerz Ewa Niebudek-Bogusz High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development Cancers glottis organic pathology glottic cancer high-speed videoendoscopy kymography machine learning |
| title | High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development |
| title_full | High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development |
| title_fullStr | High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development |
| title_full_unstemmed | High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development |
| title_short | High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development |
| title_sort | high speed videoendoscopy enhances the objective assessment of glottic organic lesions a case control study with multivariable data mining model development |
| topic | glottis organic pathology glottic cancer high-speed videoendoscopy kymography machine learning |
| url | https://www.mdpi.com/2072-6694/15/14/3716 |
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