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Whether antithrombotic drugs increase the risk of post-esophageal endoscopic resection bleeding is unknown. This study examined the effect of antithrombotic drugs, aspirin, thienopyridine, direct oral anticoagulants (DOAC), and warfarin, on post-esophageal endoscopic resection bleeding.We enrolled 957 patients (1202 esophageal tumors) treated with endoscopic resection and classified them based on antithrombotic drug use as no use, aspirin, thienopyridine, DOAC, and warfarin. Patients using antiplatelet drugs (i.e. aspirin and thienopyridine) were further sub-classified based on their continued or discontinued use before endoscopic resection. The bleeding rates were compared between these groups to assess the effects of antithrombotic drug use and interruption of antiplatelet therapy on post-esophageal endoscopic resection bleeding.The post-endoscopic resection bleeding rate was 0.3% (95% CI, 0.1-1) in the group without antithrombotic drug use, 4.5% (95% CI, 0.1-23) in the aspirin-continued group, 2.9% (95% CI, 0.1-15) in the aspirin-discontinued group, 0% (95% CI, 0-78) in the replaced thienopyridine with aspirin group, 0% (95% CI, 0-26) in the thienopyridine-discontinued group, 13% (95% CI, 1.6-38) in the DOAC group, and 0% (95% CI, 0-45) in the warfarin group. The post-endoscopic resection bleeding rate in the DOAC group was significantly higher than that in the group without antithrombotic drugs (P = 0.003). The post-endoscopic resection bleeding rates did not differ between the other groups.Our results suggest that discontinuing aspirin is not necessary for esophageal endoscopic resection while we must be careful regarding DOAC.

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

A 73-year-old woman was hospitalized with sudden chest pain and hematemesis. Chest computed tomography and upper gastrointestinal endoscopy revealed an idiopathic submucosal hematoma from the cervical esophagus to the esophagogastric mucosal junction. Idiopathic esophageal submucosal hematoma is often prone to a bleeding tendency of an underlying disorder. The patient had a history of essential thrombocythemia (ET) and was taking aspirin. She successfully recovered after aspirin discontinuation and conservative treatment; however, died of cardiopulmonary arrest in the ward on day 9 of hospitalization. The autopsy revealed that the cause of death was pulmonary thromboembolism. This is the first report of ET with submucosal hematoma of the esophagus. The possibility of an esophageal submucosal hematoma should be considered when patients with ET complain of chest pain since ET and treatment with aspirin are considered risk factors for bleeding. Additionally, close attention should be focused on the risk of developing thrombosis if a patient with myeloproliferative neoplasm is required to discontinue antithrombotic therapy due to a bleeding event.

Background and study aims Colorectal endoscopic submucosal dissection (ESD) is technically challenging owing to submucosal fibrosis and difficult endoscope manipulation. Therefore, various traction methods have been reported. We often use a simple looped nylon thread attached to a clip to assist with dissection. We assessed the feasibility of mentor-guided colorectal ESD using this traction device (TD). Patients and methods From December 2017 to March 2018, we retrospectively reviewed outcomes of 101 colorectal ESDs performed by two groups of endoscopists (A, 5 endoscopists with colorectal ESD experience of 300 cases). Group A was further divided into two subgroups that performed ESD with or without TD. Results No significant difference was observed in ESD completion rates (86.1 % [62/72] vs. 96.6 % [28/29]; odds ratio [OR], 0.22; 95 % confidence interval [CI], 0.005 – 1.71; P = 0.17) or procedure times (52.0 min vs. 40.0 min; P = 0.27) and adverse event rates between groups A and B. The rate of TD use was significantly higher in group A than in group B (44.4 % [32/72] vs. 20.7 % [6/29]; OR, 3.03; CI, 1.04 – 10.23; P = 0.03). The completion rate was not different between the two subgroups of group A (with vs. without TD) (81.2 % [26/32] vs. 90.0 % [36/40]; OR, 0.49; CI, 0.09 – 2.29; P = 0.32); however, the proportion of fibrosis cases was significantly higher in the TD-use group (46.8 % [15/32] vs. 22.5 % [9/40]; OR, 2.99; CI, 0.98 – 9.59; P = 0.03). Conclusion Mentor-guided colorectal ESD using TD was performed efficiently, safely, and in a manner comparable to that of experts.

Background and Aims The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma. Methods We retrospectively collected 8428 training images of esophageal cancer from 384 patients at the Cancer Institute Hospital, Japan. Using these, we developed deep learning through convolutional neural networks (CNNs). We also prepared 1118 test images for 47 patients with 49 esophageal cancers and 50 patients without esophageal cancer to evaluate the diagnostic accuracy. Results The CNN took 27 seconds to analyze 1118 test images and correctly detected esophageal cancer cases with a sensitivity of 98%. CNN could detect all 7 small cancer lesions less than 10 mm in size. Although the positive predictive value for each image was 40%, misdiagnosing shadows and normal structures led to a negative predictive value of 95%. The CNN could distinguish superficial esophageal cancer from advanced cancer with an accuracy of 98%. Conclusions The constructed CNN system for detecting esophageal cancer can analyze stored endoscopic images in a short time with high sensitivity. However, more training would lead to higher diagnostic accuracy. This system can facilitate early detection in practice, leading to a better prognosis in the near future.

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 ESCC 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, 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

A white light imaging (WLI) endoscopy showed a 2/3 circumference rough reddish depressed area with keratinization on the right wall of middle to lower thoracic esophagus. There was no granular change or nodule within the depressed area. Predicted depth of tumor invasion was lamina propria mucosa (LPM) (Fig. 2.1).

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. 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. 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. 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.

Image recognition using artificial intelligence(AI)has developed dramatically with innovative technologies such as machine learning and deep learning. Currently, it is considered that AI has exceeded human ability in image recognition. In the field of endoscopic diagnosis, development of computer-aided diagnosis(CAD)systems using AI is progressing. The CAD is expected to help endoscopists improve detection and characterization of polyp, cancer, and inflamation in all digestive area. Some CAD systemes showing ability better than endoscopists have been reported. It may be well applicable to daily clinical practice as real time endoscopic diagnosis in the near future.

An 83-year-old man had been prescribed clopidogrel for pontine infarction since 8 months previously, and had had a cough for the last 2 weeks of this period. Laboratory examinations on admission showed a marked increase in eosinophils and elevated serum immunoglobulin E levels. Chest radiography showed bilateral ground-glass opacities, mild reticulation, and interlobar pleural effusion in the minor fissure. After clopidogrel was discontinued his symptoms resolved, and his laboratory tests showed normal results. Bronchoalveolar lavage also showed an increase in eosinophils, and transbronchial biopsy revealed infiltration of eosinophils in the subepithelium of the bronchial mucosa. On the basis of these findings, we diagnosed eosinophilic pneumonia induced by clopidogrel. Reports on cases of lung diseases caused by anti-platelet drugs are rare. To the best of our knowledge, this case is the first report on clopidogrel-induced eosinophilic pneumonia.

A 68-year-old woman was brought by ambulance because of sudden choking sensation and dyspnea after coughing. A physical examination showed hoarseness and blood-stained sputum. A chest computed tomography (CT) showed bilateral diffusely-distributed ground-glass opacity. A bronchoscopy examination revealed a large nodule on the left larynx and bloody bronchoalveolar lavage fluid. We diagnosed negative pressure pulmonary hemorrhage (NPPH) based on suspected upper-airway obstruction, but it resolved without further treatment. We found no other causes. We report a rare case of NPPH due to a giant vocal cord nodule.

A 60-year-old woman had received adjuvant chemotherapy after abdominal hysterectomy for clear cell carcinoma of the endometrium. She had no history of allergy. She was admitted to our hospital because of massive pleural effusion. She underwent drainage of left-side malignant pleural effusion followed by chemical pleurodesis with OK-432 via a chest tube. Ten minutes after administration of OK-432 to the thoracic cavity, she complained of dyspnea and showed general flushing. Since the clinical course suggested drug-induced anaphylactic reaction, she was immediately treated with an adrenaline subcutaneous injection and methylprednisolone sodium succinate intravenous injection. However, the respiratory insufficiency progressively deteriorated and mechanical ventilation was needed for the next 3 days. This is the first published case of anaphylactic reaction to OK-432 pleurodesis. In Japan, OK-432 is a key drug used for pleurodesis and we should consider possible serious adverse reactions include anaphylactic reaction.

The patient was a 95-year-old-man admitted to the urological section of our hospital because of hematuria. Transurethral resection of the bladder tumor (TUR-Bt) was performed. This tumor was diagnosed pathologically as bladder cancer (transitional cell carcinoma Grade 3 pT1). After 8 months, local reccurence was seen and TUR-Bt was performed. (pTa). After 1 month, his chest radiograph and computed tomogram showed a mass shadow in the left upper lung field. Cystoscopy did not reveal a local reccurence. The patient's condition worsened, and he died of respiratory failure after about 1 month. At autopsy, pathologic studies of the lung tumor revealed transitional cell carcinoma, and local recurrence was not seen. We report a rare case of pulmonary metastases from superficial bladder cancer without local reccurence.

Objective: An alteration of high energy phosphate metabolism in muscles may contribute to exercise intolerance. The objective of this study was to clarify the changes in high energy phosphate metabolites in muscles during exercise in patients with non-hypoxaemic chronic obstructive pulmonary disease (COPD), which influences the impairment of muscle metabolism. Methodology: Calf muscle energy metabolism was studied in eight stable non-hypoxaemic COPD patients and eight control subjects, using 31P-magnetic resonance spectroscopy (MRS). MRS spectra were acquired at rest, during exercise at two levels of intensity, and during recovery. The control subjects exercised under both normoxic and hypoxic conditions. The intensity of exercise was standardized by the maximal isometric voluntary contraction (MVC) of the calf muscle and the cross-sectional area (CSA) of calf muscle. Results: MVC and CSA were lower in COPD patients. No significant differences in intracellular pH, inorganic phosphate/phosphocreatine ratio or percentage recovery in inorganic phosphate/phosphocreatine ratio were observed between the two groups in muscles at rest, during exercise or during recovery. Conclusions: Muscle metabolites, during exercise standardized by muscle CSA and MVC, did not differ between non-hypoxaemic COPD patients and control subjects. MVC, CSA or both, are assumed to be closely related to muscle metabolism, as no difference in high energy phosphate metabolites was observed for COPD patients compared to control subjects when the load was standardized for MVC and CSA. This suggests that high energy metabolites are consumed to a similar extent in the same muscle volume in non-hypoxaemic COPD patients and control subjects.

A 60-year-old man was admitted for investigation of an abnormality detected in chest radiography: a giant tumor with calcification in the right middle lung field. A computed tomography (CT) scan revealed multiple tumors with calcification on the posterior chest wall. Histological analysis of the tumor specimen obtained by surgical biopsy demonstrated an increasing number of plasma cells accompanied with the deposition of amyloid. A bone marrow biopsy contained over 50% of plasma cells. We therefore diagnosed these tumors as multiple myeloma. It has been reported that multiple myeloma is usually characterized by osteolytic lesions; osteosclerotic changes are rare. Multiple myeloma should be taken into account as one of the causes of a chest wall tumor even if it is diffusely calcified.

A 44-year-old man visited our hospital because of right chest pain. Pleural effusion in the right lung was detected on a chest radiograph. A chest CT scan demonstrated no abnormal lesions in either lung field, but passive atelectasis due to the pleural effusion was present. Since many eosinophils were found in the exudative pleural effusion, a parasitic infection was suspected. An enzyme-linked immunosorbent assay test led to a diagnosis of eosinophilic pleural effusion by dirofilariasis. Pleural effusion disappeared spontaneously and the level of anti-Dirofilaria immitis antibody decreased. Continued careful observation is necessary in such cases.

Langmuir–Blodgett (LB) films of 10,12-tricosadiynoic acid molecules were deposited onto a basal plane of highly oriented pyrolytic graphite (HOPG) by Schaefer's method and then observed by scanning tunneling microscopy (STM). For the purpose of achieving a parallel molecular arrangement, the deposition was deliberately conducted at a surface pressure lower than that of saturated Langmuir film. Using this approach, 10,12-tricosadiynoic acid molecules could form an ordered structure with their molecular chains lying parallel to the substrates. A similar result was also attained with behenic acid molecules.