Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors

• Verfasst von: Ali, Nairveen [VerfasserIn]
• Verfasst von: Bolenz, Christian [VerfasserIn]
• Verfasst von: Todenhöfer, Tilman [VerfasserIn]
• Verfasst von: Stenzel, Arnulf [VerfasserIn]
• Verfasst von: Deetmar, Peer [VerfasserIn]
• Verfasst von: Kriegmair, Martin [VerfasserIn]
• Verfasst von: Knoll, Thomas [VerfasserIn]
• Verfasst von: Porubsky, Stefan [VerfasserIn]
• Verfasst von: Hartmann, Arndt [VerfasserIn]
• Verfasst von: Popp, Jürgen [VerfasserIn]
• Verfasst von: Kriegmair, Maximilian [VerfasserIn]
• Verfasst von: Bocklitz, Thomas [VerfasserIn]
• Titel: Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
• Verf.angabe: Nairveen Ali, Christian Bolenz, Tilman Todenhöfer, Arnulf Stenzel, Peer Deetmar, Martin Kriegmair, Thomas Knoll, Stefan Porubsky, Arndt Hartmann, Jürgen Popp, Maximilian C. Kriegmair and Thomas Bocklitz
• E-Jahr: 2021
• Jahr: 02 June 2021
• Umfang: 10 S.
• Fussnoten: Gesehen am 26.08.2021
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Springer Nature, 2011
• Jahr Quelle: 2021
• Band/Heft Quelle: 11(2021), Artikel-ID 11629, Seite 1-10
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-021-91081-x
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 176792321X

Abstract

Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.