Ossifying fibrous epulis as an IgG4-related disease of the oral cavity: a case report and literature review

Background Fibrous sclerosing tumours and hypertrophic lesions in IgG4-related disease (IgG4-RD) are formed in various organs throughout the body, but disease in the oral region is not included among individual organ manifestations. We report a case of ossifying fibrous epulis that developed from the gingiva, as an instance of IgG4-RD. Case presentation A 60-year-old Japanese man visited the Department of Oral and Maxillofacial Surgery, Gunma University Hospital, with a chief complaint of swelling of the left mandibular gingiva. A 65 mm × 45 mm pedunculated tumour was observed. The bilateral submandibular lymph nodes were enlarged. The intraoperative pathological diagnosis of the enlarged cervical lymph nodes was inflammation. Based on this diagnosis, surgical excision was limited to the intraoral tumour, which was subsequently pathologically diagnosed as ossifying fibrous epulis. Histopathologically, the ossifying fibrous epulis exhibited increased levels of fibroblasts and collagen fibres, as well as infiltration by numerous plasma cells. The IgG4/IgG cell ratio was > 40%. Serologic analysis revealed hyper-IgG4-emia (> 135 mg/dL). The patient met the comprehensive clinical diagnosis criteria and the American College of Rheumatology and European League Against Rheumatism classification criteria for IgG4-RD. Based on these criteria, we diagnosed the ossifying fibrous epulis in our patient as an IgG4-related disease. A pathological diagnosis of IgG4-related lymphadenopathy was established for the cervical lymph nodes. Concomitant clinical findings were consistent with type II IgG4-related lymphadenopathy. Conclusions A routine serological test may be needed in cases with marked fibrous changes (such as epulis) in the oral cavity and plasma cells, accompanied by tumour formation, to determine the possibility of individual-organ manifestations of IgG4-related disease.

Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation

The research is aimed at investigating computed tomography (CT) image based on deep learning algorithm and the application value of ceramide glycosylation in diagnosing bladder cancer. The images of ordinary CT detection were improved. In this study, 60 bladder cancer patients were selected and performed with ordinary CT detection, and the detection results were processed by CT based on deep learning algorithms and compared with pathological diagnosis. In addition, Western Blot technology was used to detect the expression of glucose ceramide synthase (GCS) in the cell membrane of tumor tissues and normal tissues of bladder. The comparison results found that, in simple CT clinical staging, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the clinical staging of pathological diagnosis ( P < 0.05 ). In the clinical staging of algorithm-based CT test results, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b stage, T3 stage, and T4 stage were 100.00%; and the total coincidence rate was 96.69%, which was not obviously different from the clinical staging of pathological diagnosis ( P > 0.05 ). Therefore, it could be concluded that the algorithm-based CT detection results were more accurate, and the use of CT scans based on deep learning algorithms in the preoperative staging and clinical treatment of bladder cancer showed reliable guiding significance and clinical value. In addition, it was found that the expression level of GCS in normal bladder tissues was much lower than that in bladder cancer tissues. This indicated that the changes in GCS were closely related to the development and prognosis of bladder cancer. Therefore, it was believed that GCS may be an effective target for the treatment of bladder cancer in the future, and further research was needed for specific conditions.

Deep Learning-Based Magnetic Resonance Imaging in the Evaluation of Tumor, Node, and Metastasis Staging of Renal Cancer

This study was aimed to investigate the diagnostic accuracy of magnetic resonance imaging (MRI) based on deep dictionary learning in TNM (tumor, node, and metastasis) staging of renal cell carcinoma. In this study, 82 patients with renal cancer were selected as the research object. The results were diagnosed by deep dictionary learning MRI, and TNM staging was performed by professional imaging personnel. MRI image will be reconstructed after deep dictionary learning to improve its image recognition ability. The pathological diagnosis will be handed over to the physiological pathology laboratory of the hospital for diagnosis. The staging results were compared with the pathological diagnostic staging results, and the results were analyzed by consistency statistics to evaluate the diagnostic value. The results showed that T staging was significantly consistent with the pathological diagnosis. 2 cases were misdiagnosed, and the accuracy rate was 97.56%. Compared with the pathological diagnosis, N staging had less obvious consistency. 10 cases were misdiagnosed, and the accuracy rate was 87.80%. M staging was significantly consistent with the pathological diagnosis. 4 cases were misdiagnosed. The accuracy rate was 95.12%. After laparotomy, it was found that 37 patients had emboli and 45 patients had no emboli, while 40 patients had emboli and 42 patients had no emboli by MRI. The accuracy rate was 96.34%. The results showed that in the evaluation of TNM staging by MRI imaging based on deep dictionary learning in patients with renal cell carcinoma, the diagnostic results of N staging and M staging were highly consistent with the pathological diagnosis, while the diagnostic results of T staging were slightly less accurate, and the diagnostic consistency was good. The results can provide effective support for the clinical application of MRI imaging based on deep dictionary learning as the clinical diagnosis of TNM staging of renal cell carcinoma.

Severe reflux esophagitis after total gastrectomy successfully treated by transposition of the jejunojejunal anastomosis: a report of two cases

Background Reflux esophagitis after total gastrectomy is often difficult to treat. In this report, we describe two cases of reflux esophagitis that were refractory to medical therapy and successfully treated by transposition of the jejunojejunal anastomosis. Case presentation Case 1: A 66-year-old man underwent total gastrectomy and cholecystectomy for gastric cancer, and Roux-en-Y (RY) reconstruction was performed. The pathological diagnosis was T4aN3aM0 stage IIIC. Five months later, esophagogastroduodenoscopy identified reflux esophagitis. Although he was treated with various oral medications and was hospitalized six times, he lost 19 kg of weight. Finally, the patient was reoperated 3 years postoperatively. Intraoperative findings showed that there was no evidence of recurrence or severe adhesions that could have caused obstruction, and the anastomotic distance between the esophagojejunostomy and the jejunojejunostomy was approximately 40 cm. The jejunojejunostomy was re-anastomosed to increase the distance to 100 cm. Two years and 6 months after the reoperation, there was no recurrence of reflux esophagitis, and the patient’s weight increased by 14 kg. Case 2: A 68-year-old woman underwent total gastrectomy and cholecystectomy for gastric cancer, and RY reconstruction was performed. The pathological diagnosis was T4aN0M0 stage IIB. Similar to Case 1, the patient was diagnosed with reflux esophagitis 5 months later. She lost 23 kg of weight and was reoperated at 6 months postoperatively. Intraoperative findings showed that there was no evidence of recurrence or severe adhesions, and transposition of the jejunojejunostomy was performed to increase the distance between anastomoses from 40 to 100 cm. Two years and 8 months after the reoperation, there was no recurrence of reflux esophagitis, and her weight increased by 15 kg. Conclusions Transposition of the jejunojejunostomy was an effective treatment for medication-resistant severe reflux esophagitis after total gastrectomy.

Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer. In recent years, both morbidity and mortality rates, particularly in our country, are getting higher and higher. At present, diagnosis of colorectal cancer, specifically in the emergency department of a hospital, is based on the doctor's pathological diagnosis, and it is heavily dependent on the doctor's clinical experience. The doctor's workload is heavy, and misdiagnosis events occur from time to time. Therefore, computer-aided diagnosis technology is desperately needed for colorectal pathological images to assist pathologists in reducing their workload, improve the efficiency of diagnosis, and eliminate misdiagnosis. To address these issues, a gland segmentation of emergency colorectal pathology images and diagnosis of benign and malignant pathology is presented in this paper. Initially, a multifeatured auxiliary diagnosis is designed to enable diagnosis of benign and malignant diagnosis of emergency colorectal pathology. The proposed algorithm constructs an SVM-enabled pathological diagnosis model which is based on contour, color, and texture features. Additionally, their combination is used for pathological benign and malignant pathological diagnosis of two types of data sets D1 (original pathological image dataset) and D2 (dataset that has undergone glandular segmentation) diagnosis. Experimental results show that the proposed pathological diagnosis model has higher diagnostic accuracy on D2. Among these datasets, SVM based on the multifeature fusion of contour and texture achieved the highest diagnostic accuracy rate, i.e., 83.75%, which confirms that traditional image processing methods have limitations. Diagnosing benign and malignant colorectal pathology in an emergency is more difficult and must be treated on a priority basis. Finally, an emergency colorectal pathology diagnosis method, which is based on deep convolutional neural networks such as CIFAR and VGG, is proposed. After configuring and training process of the two networks, trained CIFAR and VGG network models are applied to the diagnosis of both datasets, i.e., D1 and D2, respectively.

Clinical Efficacy of Liver Tumor Biopsy With Radiofrequency Ablation of the Puncture Route Using a Co-access Needle

Background/Aim: Tumor biopsy are needed frequency for accurate diagnosis. However, percutaneous liver tumor biopsy presents a risk of complications such as bleeding and tumor seeding. We investigated the feasibility of liver tumor biopsy, followed by cauterization with expandable radiofrequency ablation. Patients and Methods: Tumor biopsies using a co-access needle were performed in 102 patients. Expandable radiofrequency ablation was used to ensure cauterization and hemostasis of the puncture route. We evaluated the clinical background and complications. Results: The average (±standard deviation) tumor diameter was 56.87±39.45 mm. Pathological diagnosis was possible in all cases. In 20 patients, the postoperative pathological diagnosis differed from the preoperative diagnosis. No significant anemia progression was observed in any patients after biopsy, and no peritoneal seeding was observed during a mean follow-up observation period of 18.5 months. Conclusion: Liver tumor biopsy, followed by cauterization with expandable radiofrequency ablation via a co-access needle, is safe and useful for obtaining reliable diagnoses.

PATH-19. TERT PROMOTER MUTATION, NOT H3K27M MUTATION IS A PROGNOSTIC FACTOR FOR ADULT THALAMIC GLIOMAS

Purpose Thalamic diffuse glioma is classified as WHO grade 4 as Diffuse midline glioma, H3K27M mutation if H3K27M mutation was found regardless of its histological findings, but the significance of H3K27M mutation is not clear compared with pediatric cases. We aimed to find genetic prognostic factors in adult thalamic diffuse gliomas. METHODS Pathological diagnosis, genetic abnormalities, and clinical course of adult newly diagnosed thalamic gliomas diagnosed and treated at our institution from July 2007 to March 2020 were retrospectively analyzed. RESULTS The number of cases was 41 (24 males, 17 females), median age was 47 years (20-75 years). Tumor localization was 20 cases on the left, 14 cases on the right, and 7 cases on both sides. The pathological diagnosis was GBM 15 cases, DMG 15 cases, AA-IDH WT 7 cases, DA-IDH WT 4 cases, all of which were IDH wild type, and none of them had IDH mutation and 1p/19q co-deletion. H3K27M mutations were found in 15 cases and TERT promoter mutations were found in 12 cases, both of which were completely mutually exclusive. Tumor resection and biopsy was performed in 33 and 8 cases, respectively, and the median removal rate was 95% for those who underwent tumor resection. The median PFS and OS of all cases were 14.3 months and 38 months, respectively, and the median OS by pathological diagnosis was GBM 12.4 months, DMG 47.4 months, AA-IDH WT 37.3 months, DA-IDH WT not reached. The median OS in the H3K27M mutant group (47.4 months) was significantly better (p=0.02) than that in the TERT promoter mutation group (13.5 months). CONCLUSION There was no IDH mutation in adult thalamic gliomas, the H3K27M mutation and the TERT promoter mutation were mutually exclusive. The H3K27M mutation was not a prognostic factor, but the TERT promoter mutation was the strongest prognostic factor.