Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer

Yumii Kohei

Ueda Tsutomu

Kawahara Daisuke

Chikuie Nobuyuki

Taruya Takayuki

Hamamoto Takao

Takeno Sachio

Auris Nasus Larynx

Objective
Transoral surgery (TOS) is a widely used treatment for laryngopharyngeal cancer. There are some difficult cases of setting the extent of resection in TOS, particularly in setting the vertical margins. However, positive vertical margins require additional treatment. Further, excessive resection should be avoided as it increases the risk of bleeding as a postoperative complication and may lead to decreased quality of life, such as dysphagia. Considering these issues, determining the extent of resection in TOS is an important consideration. In this study, we investigated the possibility of accurately diagnosing the depth of laryngopharyngeal cancer using radiomics, an image analysis method based on artificial intelligence (AI).
Methods
We included esophagogastroduodenoscopic images of 95 lesions that were pathologically diagnosed as squamous cell carcinoma (SCC) and treated with transoral surgery at our institution between August 2009 and April 2020. Of the 95 lesions, 54 were SCC in situ, and 41 were SCC. Radiomics analysis was performed on 95 upper gastrointestinal endoscopic NBI images of these lesions to evaluate their diagnostic performance for the presence of subepithelial invasion. The lesions in the endoscopic images were manually delineated, and the accuracy, sensitivity, specificity, and area under the curve (AUC) were evaluated from the features obtained using least absolute shrinkage and selection operator analysis. In addition, the results were compared with the depth predictions made by skilled endoscopists.
Results
In the Radiomics study, the average cross-validation was 0.833. The mean AUC for cross-validation calculated from the receiver operating characteristic curve was 0.868. These results were equivalent to those of the diagnosis made by a skilled endoscopist.
Conclusion
The diagnosis of laryngopharyngeal cancer depth using radiomics analysis has potential clinical applications. We plan to use it in actual surgery in the future and prospectively study whether it can be used for diagnosis.

Artificial intelligence (AI)

Head and neck cancer

Transoral surgery

Diagnosis of depth

Radiomics

Deep learning

英語

Elsevier

© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

This is not the published version. Please cite only the published version.

この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。

[DOI] https://doi.org/10.1016/j.anl.2023.09.001 ～の異版である

日本学術振興会

Japan Society for the Promotion of Science

[Crossref Funder] https://doi.org/10.13039/501100001691

頭頸部癌における人工知能を用いた内視鏡と経口超音波による超高精度診断モデルの開発

Development of an ultra-high-precision diagnostic model using endoscopy and oral ultrasound with artificial intelligence in head and neck cancer.

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