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BackgroundImage recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.MethodsA CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.ResultsThe CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.ConclusionThe constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

BackgroundDetermining the depth of invasion of early stage colorectal cancer has been emphasized as a means of improving endoscopic diagnostic accuracy. Recent studies have focused on other pathological risk factors for lymph node metastasis (LNM). We investigated the significance of depth of invasion and predictive properties of other endoscopic findings.MethodsWe retrospectively investigated 846 patients with submucosal invasive (T1) colorectal cancer who received an accurate pathological diagnosis and were treated between January 2005 and December 2016. Pathological risk factors associated with LNM were reviewed. We divided patients into groups: low-risk T1 colorectal cancer (LRC; no risk factors) and high-risk T1 colorectal cancer (HRC; exhibiting lymphovascular invasion, tumor budding grade of 2/3, and/or poor differentiation) and studied predictive endoscopic factors for HRC.ResultsSignificant risk factors for LNM in multivariate analysis were lymphovascular invasion [odds ratio (OR) 8.09; 95% confidence interval (CI) 3.84–17.1], tumor budding (OR 1.89; 95% CI 1.09–3.29), and histological differentiation (OR 2.09; 95% CI 1.12–3.89). The LNM-positive rate with only deep submucosal invasion was 1.6%. Significant predictive factors for HRC in multivariate analysis identified rectal tumor location (OR 1.92; 95% CI 1.35 –2.72, depression (OR 2.73; 95% CI 1.96 –3.80), protuberance within the depression (OR 2.58; 95% CI 1.39– 4.78), expansiveness (OR 2.39; 95% CI 1.27– 4.50), and loss of mucosal patterns (OR 1.90; 95% CI 1.20 –3.01) as significant factors.ConclusionsRectal tumor location, depression, protuberance within the depression, expansiveness, and loss of mucosal patterns could be predictive factors for HRC.

Objectives In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as EP-SM1, whereas ESCC cases deeper than SM2 are treated by surgical operation or chemoradiotherapy. Therefore, it is crucial to determine the invasion depth of ESCC via preoperative endoscopic examination. Recently, rapid progress in the utilization of artificial intelligence (AI) with deep learning in medical fields has been achieved. In this study, we demonstrate the diagnostic ability of AI to measure ESCC invasion depth. Methods We retrospectively collected 1751 training images of ESCC at the Cancer Institute Hospital, Japan. We developed an AI-diagnostic system of convolutional neural networks using deep learning techniques with these images. Subsequently, 291 test images were prepared and reviewed by the AI-diagnostic system and 13 board-certified endoscopists to evaluate the diagnostic accuracy. Results The AI-diagnostic system detected 95.5% (279/291) of the ESCC in test images in 10 s, analyzed the 279 images and correctly estimated the invasion depth of ESCC with a sensitivity of 84.1% and accuracy of 80.9% in 6 s. The accuracy score of this system exceeded those of 12 out of 13 board-certified endoscopists, and its area under the curve (AUC) was greater than the AUCs of all endoscopists. Conclusions The AI-diagnostic system demonstrated a higher diagnostic accuracy for ESCC invasion depth than those of endoscopists and, therefore, can be potentially used in ESCC diagnostics.

BackgroundEarly detection of early gastric cancer (EGC) allows for less invasive cancer treatment. However, differentiating EGC from gastritis remains challenging. Although magnifying endoscopy with narrow band imaging (ME-NBI) is useful for differentiating EGC from gastritis, this skill takes substantial effort. Since the development of the ability to convolve the image while maintaining the characteristics of the input image (convolution neural network: CNN), allowing the classification of the input image (CNN system), the image recognition ability of CNN has dramatically improved.AimsTo explore the diagnostic ability of the CNN system with ME-NBI for differentiating between EGC and gastritis.MethodsA 22-layer CNN system was pre-trained using 1492 EGC and 1078 gastritis images from ME-NBI. A separate test data set (151 EGC and 107 gastritis images based on ME-NBI) was used to evaluate the diagnostic ability [accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV)] of the CNN system.ResultsThe accuracy of the CNN system with ME-NBI images was 85.3%, with 220 of the 258 images being correctly diagnosed. The method’s sensitivity, specificity, PPV, and NPV were 95.4%, 71.0%, 82.3%, and 91.7%, respectively. Seven of the 151 EGC images were recognized as gastritis, whereas 31 of the 107 gastritis images were recognized as EGC. The overall test speed was 51.83 images/s (0.02 s/image).ConclusionsThe CNN system with ME-NBI can differentiate between EGC and gastritis in a short time with high sensitivity and NPV. Thus, the CNN system may complement current clinical practice of diagnosis with ME-NBI.

Gastric cancer in young adults has been pointed out to comprise a subgroup associated with distinctive clinicopathological features, including an equal gender distribution, advanced disease, and diffuse‐type histology. Comprehensive molecular analyses of gastric cancers have led to molecular‐based classifications and to specific and effective treatment options. The molecular traits of gastric cancers in young adults await investigations, which should provide a clue to explore therapeutic strategies. Here, we studied 146 gastric cancer patients diagnosed at the age of 40 years or younger at the Cancer Institute Hospital (Tokyo, Japan). Tumor specimens were examined for Helicobacter pylori infection, Epstein‐Barr virus positivity, and for the expression of mismatch repair genes to indicate microsatellite instability. Overexpression, gene amplifications, and rearrangements of 18 candidate driver genes were examined by immunohistochemistry and FISH. Although only a small number of cases were positive for Epstein‐Barr virus and microsatellite instability (n = 2 each), we repeatedly found tumors with gene fusion between a tight‐junction protein claudin, CLDN18, and a regulator of small G proteins, ARHGAP, in as many as 22 cases (15.1%), and RNA sequencing identified 2 novel types of the fusion. Notably, patients with the CLDN18‐ARHGAP fusion revealed associations between aggressive disease and poor prognosis, even when grouped by their clinical stage. These observations indicate that a fusion gene between CLDN18 and ARHGAP is enriched in younger age‐onset gastric cancers, and its presence could contribute to their aggressive characteristics. In our gastric cancer in young adult cohort, we found enrichment of fusion genes between CLDN18 and ARHGAP. The positivity of the CLDN18‐ARHGAP fusion relates to advanced disease and poor prognosis, indicating its clinical relevance.

BackgroundManagement strategies for primary non-ampullary duodenal adenocarcinoma (NADAC) in early stage are not well established given its low incidence. This study aimed to elucidate clinicopathological features of early NADAC, including risk for lymph nodal metastasis (LNM).MethodsIn total, 166 patients with early NADAC underwent initial treatment at our institution between 2006 and 2019, of whom 153 had intramucosal (M-) and 13 had submucosal (SM-) NADAC. These endoscopic and pathological features were retrospectively analyzed. Risk factors for LNM were evaluated in 46 early NADAC patients who underwent surgery with lymph node dissection.ResultsCompared with M-NADAC, SM-NADAC was significantly more frequently located at the proximal side of the papilla, with mixed elevated and depressed macroscopic type, histologically poorly differentiated tumor and lymphovascular invasion (LVI) (85% vs. 47%, P = 0.009; 54% vs. 5%, P < 0.001; 23% vs. 0%, P < 0.001; and 46% vs. 0%, P < 0.001, respectively). The frequency of LNM was significantly higher in SM-NADAC than in M-NADAC (5/12, 42% vs. 0/34, 0%; P < 0.001). In SM-NADAC, the frequency of LNM was higher in poorly differentiated than in well to moderately differentiated tumors (3/3, 100% vs. 2/9, 22%) and higher in tumors with LVI than in those without LVI (3/5, 60% vs. 2/7, 29%). Regarding invasion depth, 2 of 4 patients with SM invasion (400 ≤ × < 500 µm) showed LNM. However, in this study, no patients developed very shallow SM invasion (0 < × < 400 µm).ConclusionsSM-NADAC showed high LNM risk. Surgical treatment with regional lymph node dissection is recommended as a treatment strategy for SM-NADAC.

BackgroundColorectal endoscopic submucosal dissection (ESD) requires advanced endoscopic skill. For safer and more reliable ESD implementation, various traction devices have been developed in recent years. The purpose of this research was to evaluate whether an ESD training program using a traction device (TD) would contribute to the improvement of trainees’ skill acquisition.MethodsThe differences in treatment outcomes and learning curves by the training program were compared before and after the introduction of TD (control group: January 2014 to March 2016; TD group: April 2016 to June 2018).ResultsA total of 316 patients were included in the analysis (TD group: 202 cases; control group: 114 cases). The number of cases required to achieve proficiency in ESD techniques was 10 in the TD group and 21 in the control group. Compared to the control group, the TD group had a significant advantage in ESD self-completion rate (73.8% vs. 58.8%), dissection speed (19.5 mm2/min vs. 15.9 mm2/min), en bloc resection rate (100% vs. 90%), and R0 resection rate (96% vs. 83%).ConclusionsThe rate of colorectal ESD self-completion by trainees improved immediately after the start of the training program using a traction device compared to the conventional method, and the dissection speed tended to increase linearly with ESD experience. We believe that ESD training using a traction device will help ESD techniques to be performed safely and reliably among trainees.

Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25–70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support.

Background Endoscopic resection (ER) has been accepted as minimally invasive treatment in patients with early gastric cancer (EGC) who have a negligible risk of lymph node metastasis. It has already been determined which lesions in differentiated-type EGC present a negligible risk of lymph node metastasis, and ER is being performed for these lesions. In contrast, no consensus has been reached on which lesions in undifferentiated-type (UD-type) EGC present a negligible risk for lymph node metastasis, nor have indications for ER for UD-type EGC been established. Methods We investigated 3843 patients who had undergone gastrectomy with lymph node dissection for solitary UD-type EGC at the Cancer Institute Hospital, Tokyo, and the National Cancer Center Hospital, Tokyo. Seven clinicopathological factors were assessed for their possible association with lymph node metastasis. Results Of the 3843 patients, 2163 (56.3%) had intramucosal cancers and 1680 (43.7%) had submucosal invasive cancers. Only 105 (4.9%) intramucosal cancers compared with 399 (23.8%) submucosal invasive cancers were associated with lymph node metastases. By multivariate analysis, tumor size 21 mm or more, lymphatic-vascular capillary involvement, and submucosal penetration were independent risk factors for lymph node metastasis (P < 0.001, respectively). None of the 310 intramucosal cancers 20 mm or less in size without lymphatic- vascular capillary involvement and ulcerative findings was associated with lymph node metastases (95% confidence interval, 0-0.96%). Conclusion UD-type intramucosal EGC 20 mm or less in size without lymphatic-vascular capillary involvement and ulcerative findings presents a negligible risk of lymph node metastasis. We propose that in this circumstance ER could be considered.

In Japan, preoperatively diagnosed T1a-muscularis mucosae or T1b-submucosa 1 (MM/SM1) esophageal squamous cell carcinoma (ESCC) is a relative indication for endoscopic resection (ER). We evaluated long-term outcomes in patients after ER for non-circumferential ESCC with a preoperative diagnosis of MM/SM1 invasion. We retrospectively reviewed 66 patients with a preoperative diagnosis of non-circumferential MM/SM1 ESCC endoscopically resected between 2010 and 2015. Patients were divided into low- (adequate follow-up) and high-risk (requiring additional treatment) groups for lymph node metastasis according to risk factors (submucosal invasion, lymphovascular invasion, or droplet infiltration) and long-term outcomes were analyzed. Pathological invasion to T1a-lamina propria mucosa, MM/SM1, and T1b-SM2 was seen in 22, 38, and 6 lesions, respectively. Overall, 71.2% patients were classified into the “adequate follow-up” group. Of these, only one patient had a lymph node recurrence, which was successfully treated by additional therapy. The remaining 28.8% patients were classified into the “requiring additional treatment” group, where no recurrences were observed after additional treatments. After a median follow-up of 58.6 months, no deaths happened due to ESCC. The 3- and 5-year overall survival rates were 93.6% and 88.7%, respectively. ER is a valid initial treatment for non-circumferential ESCC with preoperatively diagnosed MM/SM1 invasion.