Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy.
OBJECTIVE
It is necessary to establish cost-effective examinations and treatments for diminutive colorectal tumors that consider the treatment risk and surveillance interval after treatment. The Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) committee of the American Society for Gastrointestinal Endoscopy published a statement recommending the establishment of endoscopic techniques that practice the resect and discard strategy. The aims of this study were to evaluate whether our newly developed real-time image recognition system can predict histologic diagnoses of colorectal lesions depicted on narrow-band imaging and to satisfy some problems with the PIVI recommendations.


METHODS
We enrolled 41 patients who had undergone endoscopic resection of 118 colorectal lesions (45 nonneoplastic lesions and 73 neoplastic lesions). We compared the results of real-time image recognition system analysis with that of narrow-band imaging diagnosis and evaluated the correlation between image analysis and the pathological results.


RESULTS
Concordance between the endoscopic diagnosis and diagnosis by a real-time image recognition system with a support vector machine output value was 97.5% (115/118). Accuracy between the histologic findings of diminutive colorectal lesions (polyps) and diagnosis by a real-time image recognition system with a support vector machine output value was 93.2% (sensitivity, 93.0%; specificity, 93.3%; positive predictive value (PPV), 93.0%; and negative predictive value, 93.3%).


CONCLUSIONS
Although further investigation is necessary to establish our computer-aided diagnosis system, this real-time image recognition system may satisfy the PIVI recommendations and be useful for predicting the histology of colorectal tumors.
Department of Gastroenterology and Metabolism, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Yoko
Y
Kominami
Department of Endoscopy and Medicine, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Shigeto
S
Yoshida
Department of Endoscopy and Medicine, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Shinji
S
Tanaka
Department of Endoscopy and Medicine, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Yoji
Y
Sanomura
Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
Tsubasa
T
Hirakawa
Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
Bisser
B
Raytchev
Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
Toru
T
Tamaki
Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan.
Tetsusi
T
Koide
Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
Kazufumi
K
Kaneda
Department of Gastroenterology and Metabolism, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Kazuaki
K
Chayama
D000230:Adenocarcinoma / Q000000981:diagnostic imaging* / Q000473:pathology; D000236:Adenoma / Q000000981:diagnostic imaging* / Q000473:pathology; D000368:Aged; D003111:Colonic Polyps / Q000000981:diagnostic imaging* / Q000473:pathology; D003113:Colonoscopy; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D000069916:Endoscopic Mucosal Resection; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D062048:Narrow Band Imaging; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity