Primary levofloxacin resistance was common in Japan and primarily related to gyrA mutations at Asn-87 and Asp-91.

Scan the quick response (QR) code to the left with your mobile device to watch this article's video abstract and others. Don't have a QR code reader? Get one by searching 'QR Scanner' in your mobile device's app store. R ecently, the American Society of GastrointestinalEndoscopy established the Preservation and Incorporation of Valuable Endoscopic Innovations 1 for diminutive colorectal polyps. Preservation and Incorporation of Valuable Endoscopic Innovations suggests that, if an endoscopist diagnoses an agreement of >90% in determining postpolypectomy surveillance intervals and a negative predictive value of >90% with adenomatous histology, pathologic diagnosis might not be necessary. Although magnifying chromoendoscopy, 2 narrow-band imaging (NBI), 3 endocytoscopy (EC), 4 and confocal laser endomicroscopy 5 are highly accurate, interpretation of these modalities is difficult for novices. Furthermore, achieving a negative predictive value of >90% for adenoma is not easy using these modalities 3 and requires comprehensive experiments. 6 To achieve a breakthrough on this issue, we developed a computer-aided diagnosis (CAD) system for EC. This system automatically provides highly accurate diagnosis as expert endoscopists concurrently take EC images (Video Clip 1). 7 Our previous system, based on glandular structural and cellular atypia, required endoscopists to use dye for staining. In contrast, the endocytoscopic vascular pattern can effectively evaluate microvessel findings using EC with NBI (EC-NBI) without using any dye. We reported that EC-NBI has a highly accurate diagnostic ability, similar to other modalities. 8 Because dye staining complicates the procedure, our CAD system for EC-NBI represents a powerful tool for novices and experts who do not use dyes on a routine basis. Therefore, we developed a tentative CAD system model for EC-NBI image. Description of TechnologyWe developed custom software (EndoBRAIN, Cybernet Systems Co., Ltd., Tokyo, Japan) to analyze EC images. We collected a consecutive series of 1079 EC-NBI images (431 nonneoplasms and 648 neoplasms) from 85 lesions to form an image database. To validate the CAD system, we randomly extracted 100 images (50 nonneoplasms, 50 neoplasms) from the database. The remaining 979 images (381 nonneoplasms, 598 neoplasms) were used for machine learning. The inclusion criteria were colorectal lesions that had been detected during colonoscopy using EC and subsequently resected between December 2014 and April 2015. The exclusion criteria were inflammatory bowel disease; lesions for which no clear EC-NBI were available; sessile serrated adenomas/polyps (SSA/Ps); and nonepithelial Figure 1. Output image. (1) Computer diagnosis. (2) Input endocytoscopy with narrow band imaging. (3) Extracted vessel image, in which the green area denotes the extracted vessels. The light-green vessel has the maximum diameter. (4) Probability of computer diagnosis is calculated by the support vector machine classifier.Abbreviations used in this paper: CAD, computer-aided diagnosis; ...

EC-CAD provides fully automated instant classification of colorectal polyps with excellent sensitivity, accuracy, and objectivity. Thus, it can be a powerful tool for facilitating decision making during routine colonoscopy.

Optical diagnosis of colorectal polyps is expected to improve the cost-effectiveness of colonoscopy, but achieving a high accuracy is difficult for trainees. Computer-aided diagnosis (CAD) is therefore receiving attention as an attractive tool. This study aimed to validate the efficacy of the latest CAD model for endocytoscopy (380-fold ultra-magnifying endoscopy). This international web-based trial was conducted between August and November 2015. A web-based test comprising one white-light and one endocytoscopic image of 205 small colorectal polyps (≤ 10 mm) from 123 patients was undertaken by both CAD and by endoscopists (three experts and ten non-experts from three countries). Outcome measures were accuracy in identifying neoplastic change in diminutive (≤ 5 mm) and small (≤ 10 mm) polyps, and accuracy in predicting post-polypectomy surveillance intervals according to current guidelines for high confidence optical diagnoses of diminutive polyps. Of the 205 small polyps (147 neoplastic and 58 non-neoplastic), 139 were diminutive. CAD was accurate for 89 % (95 % confidence interval [CI] 83 % - 94 %) of diminutive polyps and 89 % (84 % - 93 %) of small polyps, which was significantly greater than results for the non-experts (73 % [71 % - 76 %], < 0.001; and 76 % [74 % - 78 %], < 0.001, respectively) and comparable with the experts' results (90 % [87 % - 93 %], = 0.703; and 91 % [89 % - 93 %], = 0.106, respectively). The surveillance interval predicted by CAD provided 98 % (93 % - 100 %) and 96 % (91 % - 99 %) agreement with pathology-directed intervals of the European and American guidelines, respectively. The use of CAD in endocytoscopy can be effective in the management of diminutive/small colorectal polyps.UMIN Clinical Trial Registry: UMIN000018185.

Background and study aims Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery. Patients and methods Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 – 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines. Results Sensitivity was 100 % (95 % confidence interval [CI] 72 % to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66 % (95 %CI 56 % to 76 %), 44 % (95 %CI 34 % to 55 %), 0 % (95 %CI 0 % to 3 %), and 0 % (95 %CI 0 % to 3 %), respectively; and accuracy was 69 % (95 %CI 59 % to 78 %), 49 % (95 %CI 39 % to 59 %), 9 % (95 %CI 4 % to 16 %), and 9 % (95 %CI 4 % – 16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77 % (95 %CI 62 % to 89 %) for the artificial intelligence model, and 85 % (95 %CI 73 % to 93 %; P < 0.001), 91 % (95 %CI 84 % to 96 %; P < 0.001), and 91 % (95 %CI 84 % to 96 %; P < 0.001) for the American, European, and Japanese guidelines, respectively. Conclusions Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity.

Background and Aim: Recent advances in endoscopic technology have allowed many T1 colorectal carcinomas to be resected endoscopically with negative margins. However, the criteria for curative endoscopic resection remain unclear. We aimed to identify risk factors for nodal metastasis in T1 carcinoma patients and hence establish the indication for additional surgery with lymph node dissection. Methods: Initial or additional surgery with nodal dissection was performed in 653 T1 carcinoma cases. Clinicopathological factors were retrospectively analyzed with respect to nodal metastasis. The status of the muscularis mucosae (MM grade) was defined as grade 1 (maintenance) or grade 2 (fragmentation or disappearance). The lesions were then stratified based on the risk of nodal metastasis. Results: Muscularis mucosae grade was associated with nodal metastasis (P = 0.026), and no patients with MM grade 1 lesions had nodal metastasis. Significant risk factors for nodal metastasis in patients with MM grade 2 lesions were attribution of women (P = 0.006), lymphovascular infiltration (P < 0.001), tumor budding (P = 0.045), and poorly differentiated adenocarcinoma or mucinous carcinoma (P = 0.007). Nodal metastasis occurred in 1.06% of lesions without any of these pathological factors, but in 10.3% and 20.1% of lesions with at least one factor in male and female patients, respectively. There was good inter-observer agreement for MM grade evaluation, with a kappa value of 0.67. Conclusions: Stratification using MM grade, pathological factors, and patient sex provided more appropriate indication for additional surgery with lymph node dissection after endoscopic treatment for T1 colorectal carcinomas.

Endocytoscopy is noninferior to standard biopsy for the discrimination of neoplastic lesions. With its advantage of providing an on-site diagnosis, endocytoscopy could provide a novel alternative to standard biopsy in routine colonoscopy.

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