Background and Aim:The morphological diagnosis of microvessels on the surface of superficial esophageal squamous cell carcinomas using magnifying endoscopy with narrow-band imaging is widely used in clinical practice. Nevertheless, inconsistency, even among experts, remains a problem. We constructed a convolutional neural network-based computer-aided diagnosis system to classify the microvessels of superficial esophageal squamous cell carcinomas and evaluated its diagnostic performance. Methods: In this retrospective study, a cropped magnifying endoscopy with narrow-band images from superficial esophageal squamous cell carcinoma lesions was used as the dataset. All images were assessed by three experts, and classified into three classes, Type B1, B2, and B3, based on the Japan Esophagus Society classification. The dataset was divided into training and validation datasets. A convolutional neural network model (ResNeXt-101) was trained and tuned with the training dataset. To evaluate diagnostic accuracy, the validation dataset was assessed by the computer-aided diagnosis system and eight endoscopists. Results: In total, 1777 and 747 cropped images (total, 393 lesions) were included in the training and validation datasets, respectively. The diagnosis system took 20.3 s to evaluate the 747 images in the validation dataset. The microvessel classification accuracy of the computer-aided diagnosis system was 84.2%, which was higher than the average of the eight endoscopists (77.8%, P < 0.001). The area under the receiver operating characteristic curves for diagnosing Type B1, B2, and B3 vessels were 0.969, 0.948, and 0.973, respectively. Conclusions:The computer-aided diagnosis system showed remarkable performance in the classification of microvessels on superficial esophageal squamous cell carcinomas.

Deficiency of p53 in cancer cells activates the transformation of normal tissue fibroblasts into carcinoma-associated fibroblasts; this promotes tumor progression through a variety of mechanisms in the tumor microenvironment. The role of autophagy in carcinoma-associated fibroblasts in tumor progression has not been elucidated. We aimed to clarify the significance of autophagy in fibroblasts, focusing on the TP53 status in co-cultured human colorectal cancer cell lines (TP53-wild-type colon cancer, HCT116; TP53-mutant colon cancer, HT29; fibroblast, CCD-18Co) in vitro. Autophagy in fibroblasts was significantly suppressed in association with ACTA2, CXCL12, TGFβ1, VEGFA, FGF2, and PDGFRA mRNA levels, when co-cultured with p53-deficient HCT116sh p53 cells. Exosomes isolated from the culture media of HCT116sh p53 cells significantly suppressed autophagy in fibroblasts via inhibition of ATG2B. Exosomes derived from TP53-mutant HT29 cells also suppressed autophagy in fibroblasts. miR-4534, extracted from the exosomes of HCT116sh p53 cells, suppressed ATG2B in fibroblasts. In conclusion, a loss of p53 function in colon cancer cells promotes the activation of surrounding fibroblasts through the suppression of autophagy. Exosomal miRNAs derived from cancer cells may play a pivotal role in the suppression of autophagy.

Background and Aim To investigate whether assessment by magnifying narrow‐band imaging (M‐NBI) based on the classification of the Japan Esophageal Society provides additional value to the estimation of the invasion depth of superficial esophageal squamous cell carcinoma (SCC) compared with assessment by white light endoscopy (WLE) alone. Methods Endoscopic images of 211 consecutive superficial esophageal SCCs resected by endoscopic submucosal dissection were separated into WLE and M‐NBI images. Depth estimation was performed independently by five evaluators using the numerical depth estimation scale (0 = epithelium (EP)/lamina propria (LPM), 1 = EP/LPM > muscularis mucosa (MM)/shallow submucosa (SM1), 2 = MM/SM1 > EP/LPM, 3 = MM/SM1, 4 = MM/SM1 > deep submucosa (SM2), 5 = SM2 > MM/SM1, 6 = SM2), using primarily WLE images (step 1), and subsequently both WLE and M‐NBI images (step 2). The discordance scores, determined by the average of the five evaluators' difference between the estimated score (from 0 to 6) and pathological score (0 for histologically proven EP/LPM, 3 for MM/SM1, and 6 for SM2), were analyzed in steps 1 and 2. Results The discordance scores significantly decreased in step 2 (0.53 ± 0.06) compared with those in step 1 (0.79 ± 0.07) (P < 0.001). When the discordance score < 1.5 was regarded as a clinically correct diagnosis, the rate of the clinically correct diagnosis significantly increased in step 2 compared with that in step 1 (81% to 91%, P < 0.001). Conclusion M‐NBI has an additive value for estimating the invasion depth of superficial esophageal SCCs.

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