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Endoscopic detection and differentiation of esophageal lesions using a deep neural network

Published:October 01, 2019DOI:https://doi.org/10.1016/j.gie.2019.09.034

      Background and Aims

      Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentiate esophageal SCC.

      Methods

      A total of 9591 nonmagnified endoscopy (non-ME) and 7844 ME images of pathologically confirmed superficial esophageal SCCs and 1692 non-ME and 3435 ME images from noncancerous lesions or normal esophagus were used as training image data. Validation was performed using 255 non-ME white-light images, 268 non-ME narrow-band images/blue-laser images, and 204 ME narrow-band images/blue-laser images from 135 patients. The same validation test data were diagnosed by 15 board-certified specialists (experienced endoscopists).

      Results

      Regarding diagnosis by non-ME with narrow-band imaging/blue-laser imaging, the sensitivity, specificity, and accuracy were 100%, 63%, and 77%, respectively, for the artificial intelligence (AI) system and 92%, 69%, and 78%, respectively, for the experienced endoscopists. Regarding diagnosis by non-ME with white-light imaging, the sensitivity, specificity, and accuracy were 90%, 76%, and 81%, respectively, for the AI system and 87%, 67%, and 75%, respectively, for the experienced endoscopists. Regarding diagnosis by ME, the sensitivity, specificity, and accuracy were 98%, 56%, and 77%, respectively, for the AI system and 83%, 70%, and 76%, respectively, for the experienced endoscopists. There was no significant difference in the diagnostic performance between the AI system and the experienced endoscopists.

      Conclusions

      Our AI system showed high sensitivity for detecting SCC by non-ME and high accuracy for differentiating SCC from noncancerous lesions by ME.

      Abbreviations:

      AI (artificial intelligence), BLI (blue-laser imaging), CNN (convolutional neural network), ME (magnified endoscopy), NBI (narrow-band imaging), SCC (squamous cell carcinoma), WLI (white-light imaging)
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      References

        • Ferlay J.
        • Colombet M.
        • Soerjomataram I.
        • et al.
        Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods.
        Int J Cancer. 2019; 144: 1941-1953
        • Kodama M.
        • Kakegawa T.
        Treatment of superficial cancer of the esophagus: a summary of responses to a questionnaire on superficial cancer of the esophagus in Japan.
        Surgery. 1998; 123: 432-439
        • Yamashina T.
        • Ishihara R.
        • Nagai K.
        • et al.
        Long-term outcome and metastatic risk after endoscopic resection of superficial esophageal squamous cell carcinoma.
        Am J Gastroenterol. 2013; 108: 544-551
        • Katada C.
        • Muto M.
        • Momma K.
        • et al.
        Clinical outcome after endoscopic mucosal resection for esophageal squamous cell carcinoma invading the muscularis mucosae-a multicenter retrospective cohort study.
        Endoscopy. 2007; 39: 779-783
        • Shimizu Y.
        • Tsukagoshi H.
        • Fujita M.
        • et al.
        Long-term outcome after endoscopic mucosal resection in patients with esophageal squamous cell carcinoma invading the muscularis mucosae or deeper.
        Gastrointest Endosc. 2002; 56: 387-390
        • Igaki H.
        • Kato H.
        • Tachimori Y.
        • et al.
        Clinicopathologic characteristics and survival of patients with clinical stage I squamous cell carcinomas of the thoracic esophagus treated with three-field lymph node dissection.
        Eur J Cardiothorac Surg. 2001; 20: 1089-1094
        • Yamamoto S.
        • Ishihara R.
        • Motoori M.
        • et al.
        Comparison between definitive chemoradiotherapy and esophagectomy in patients with clinical stage I esophageal squamous cell carcinoma.
        Am J Gastroenterol. 2011; 106: 1048-1054
        • Hashimoto C.L.
        • Iriya K.
        • Baba E.R.
        • et al.
        Lugol's dye spray chromoendoscopy establishes early diagnosis of esophageal cancer in patients with primary head and neck cancer.
        Am J Gastroenterol. 2005; 100: 275-282
        • Dawsey S.M.
        • Fleischer D.E.
        • Wang G.Q.
        • et al.
        Mucosal iodine staining improves endoscopic visualization of squamous dysplasia and squamous cell carcinoma of the esophagus in Linxian, China.
        Cancer. 1998; 83: 220-231
        • Gono K.
        • Obi T.
        • Yamaguchi M.
        • et al.
        Appearance of enhanced tissue features in narrow-band endoscopic imaging.
        J Biomed Opt. 2004; 9: 568-577
        • Muto M.
        • Minashi K.
        • Yano T.
        • et al.
        Early detection of superficial squamous cell carcinoma in the head and neck region and esophagus by narrow band imaging: a multicenter randomized controlled trial.
        J Clin Oncol. 2010; 28: 1566-1572
        • Takenaka R.
        • Kawahara Y.
        • Okada H.
        • et al.
        Narrow-band imaging provides reliable screening for esophageal malignancy in patients with head and neck cancers.
        Am J Gastroenterol. 2009; 104: 2942-2948
        • Ishihara R.
        • Takeuchi Y.
        • Chatani R.
        • et al.
        Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists.
        Dis Esophagus. 2010; 23: 480-486
        • Kaneko K.
        • Oono Y.
        • Yano T.
        • et al.
        Effect of novel bright image enhanced endoscopy using blue laser imaging (BLI).
        Endosc Int Open. 2014; 2: E212-E219
        • Tomie A.
        • Dohi O.
        • Yagi N.
        • et al.
        Blue laser imaging-bright improves endoscopic recognition of superficial esophageal squamous cell carcinoma.
        Gastroenterol Res Pract. 2016; 2016: 6140854
        • Morita F.H.
        • Bernardo W.M.
        • Ide E.
        • et al.
        Narrow band imaging versus Lugol chromoendoscopy to diagnose squamous cell carcinoma of the esophagus: a systematic review and meta-analysis.
        BMC Cancer. 2017; 17: 54
        • Nagami Y.
        • Tominaga K.
        • Machida H.
        • et al.
        Usefulness of non-magnifying narrow-band imaging in screening of early esophageal squamous cell carcinoma: a prospective comparative study using propensity score matching.
        Am J Gastroenterol. 2014; 109: 845-854
        • Russakovsky O.
        • Deng J.
        • Su H.
        • et al.
        ImageNet large scale visual recognition challenge.
        Int J Computer Vision. 2015; 115: 211-252
        • Esteva A.
        • Kuprel B.
        • Novoa R.A.
        • et al.
        Dermatologist-level classification of skin cancer with deep neural networks.
        Nature. 2017; 542: 115-118
        • Gulshan V.
        • Peng L.
        • Coram M.
        • et al.
        Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
        JAMA. 2016; 316: 2402-2410
        • Hirasawa T.
        • Aoyama K.
        • Tanimoto T.
        • et al.
        Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.
        Gastric Cancer. 2018; 21: 653-660
        • Horie Y.
        • Yoshio T.
        • Aoyama K.
        • et al.
        Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.
        Gastrointest Endosc. 2019; 89: 25-32
        • Society J.E.
        Japanese classification of esophageal cancer, tenth edition: part I.
        Esophagus. 2009; 6: 1-25
        • Landis J.R.
        • Koch G.G.
        The measurement of observer agreement for categorical data.
        Biometrics. 1977; 33: 159-174
        • Kanda Y.
        Investigation of the freely available easy-to-use software “EZR” for medical statistics.
        Bone Marrow Transplant. 2013; 48: 452-458
        • Zhao Y.Y.
        • Xue D.X.
        • Wang Y.L.
        • et al.
        Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy.
        Endoscopy. 2019; 51: 333-341
        • Kumagai Y.
        • Takubo K.
        • Kawada K.
        • et al.
        Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus.
        Esophagus. 2019; 16: 180-187
        • Ishihara R.
        • Takeuchi Y.
        • Chatani R.
        • et al.
        Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists.
        Dis Esophagus. 2010; 23: 480-486