Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy

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• 作者：Yoko Kominami ; MD¹ ; Shigeto Yoshida ; MD ; PhD² ; ; Shinji Tanaka ; MD ; PhD² ; Yoji Sanomura ; MD ; PhD² ; Tsubasa Hirakawa ; ME³ ; Bisser Raytchev ; PhD³ ; Toru Tamaki ; PhD³ ; Tetsusi Koide ; PhD⁴ ; Kazufumi Kaneda ; PhD³ ; Kazuaki Chayama ; MD ; PhD¹
• 关键词：NBI ; narrow-band imaging ; NPV ; negative predictive value ; PIVI ; Preservation and Incorporation of Valuable Endoscopic Innovations ; PPV ; positive predictive value ; ROI ; region of interest ; SIFT ; scale-invariant feature transform ; SVM ; support vector machine
• 刊名：Gastrointestinal Endoscopy
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：83
• 期：3
• 页码：643-649
• 全文大小：811 K

文摘

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.