Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy
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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实时图像识别系统和窄带成像放大结肠镜计算机辅助诊断结直肠息肉组织学
DOI：
10.1016/j.gie.2015.08.004
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发表时间：
2016-03-01
影响因子：
7.7
通讯作者：
Chayama, Kazuaki
中科院分区：
文献类型：
--
作者：
Kominami, Yoko;Yoshida, Shigeto;Chayama, Kazuaki
背景与目的：考虑治疗风险和治疗后的监测间隔，有必要建立具有成本效益的小型结直肠肿瘤检查和治疗方法。美国胃肠内窥镜学会的保存和整合有价值的内窥镜创新（PIVI）委员会发表了一份声明，建议建立内窥镜技术，实践切除和丢弃策略。本研究的目的是评估我们新开发的实时图像识别系统是否可以预测窄频带成像所描述的结直肠病变的组织学诊断，并满足PIVI推荐的一些问题。方法：我们纳入41例经内镜切除118个结直肠病变的患者（45个非肿瘤性病变，73个肿瘤性病变）。我们将实时图像识别系统分析结果与窄带成像诊断结果进行比较，并评价图像分析与病理结果的相关性。结果：内镜诊断与支持向量机实时图像识别系统诊断的符合率为97.5%（115/118）。基于支持向量机输出值的实时图像识别系统对结直肠小病变（息肉）的组织学表现与诊断的准确率为93.2%（敏感性93.0%，特异性93.3%，阳性预测值93.0%，特异性93.3%）。阴性预测值为93.3%)。结论：虽然我们的计算机辅助诊断系统的建立还需要进一步的研究，但这种实时图像识别系统可以满足PIVI的建议，并有助于预测结直肠肿瘤的组织学。
Background and Aims: 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.