TAFRO syndrome is a systemic inflammatory disorder characterized by thrombocytopenia, anasarca including pleural effusion and ascites, fever, renal insufficiency, and organomegaly including hepatosplenomegaly and lymphadenopathy. Its onset may be acute or sub-acute, but its etiology is undetermined. Although several clinical and pathological characteristics of TAFRO syndrome resemble those of multicentric Castleman disease (MCD), other specific features can differentiate between them. Some TAFRO syndrome patients have been successfully treated with glucocorticoids and/or immunosuppressants, including cyclosporin A, tocilizumab and rituximab, whereas others are refractory to treatment, and eventually succumb to the disease. Early and reliable diagnoses and early treatments with appropriate agents are essential to enhancing patient survival. The present article reports the 2015 updated diagnostic criteria, disease severity classification and treatment strategy for TAFRO syndrome, as formulated by Japanese research teams. These criteria and classification have been applied and retrospectively validated on clinicopathologic data of 28 patients with this and similar conditions (e.g. MCD with serositis and thrombocytopenia).

Background/Aims. The usefulness of macroscopic on-site evaluation (MOSE) during endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) has been reported, but a specific method for MOSE has not been established. We aimed to investigate the usefulness of MOSE using a stereomicroscope (S-MOSE) for the diagnosis of solid pancreatic lesions (SPLs). Methods. We reviewed a total of 60 consecutive patients who underwent both S-MOSE and rapid on-site cytopathological evaluation (ROSE) during EUS-FNB between July 2019 and October 2020, and the usefulness of S-MOSE in comparison with histology was evaluated. A 22-gauge Franseen needle was used to perform EUS-FNB in all patients, and only the specimens obtained by the first pass were evaluated. The final diagnosis was based on the surgical specimen or the clinical course consistent with the EUS-FNB results. Results. The final diagnoses of the 60 patients included 45 patients with pancreatic ductal adenocarcinoma, 6 with autoimmune pancreatitis, 4 with mass-forming pancreatitis, 1 with pancreatic metastasis, 2 with pancreatic neuroendocrine tumor, and 2 with intraductal papillary mucinous carcinoma. The histological diagnostic accuracy of the first pass of EUS-FNB was 83.3% (50/60). The agreement between the S-MOSE and the histological diagnosis was 90% (54/60). The positive predictive value of S-MOSE for histological diagnosis was 90.7%, which can be an indicator of when to stop the EUS-FNB procedure. There were no immediate or delayed adverse events reported after the FNB based on the chart and medical visit history review. Conclusion. In the EUS-FNB of SPLs, S-MOSE can be an alternative to ROSE for specimen evaluation and has the potential to shorten the procedure time.

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Rationale:Tumors with multiple histological features, such as adenocarcinomas and neuroendocrine carcinomas, were included as a new category of neuroendocrine carcinomas in the 2010 World Health Organization classification. We recently experienced a rare case of a pancreatic carcinoma with both adenocarcinoma and neuroendocrine carcinoma components, a so-called mixed adenoneuroendocrine carcinoma.Patient concerns and diagnosis:A 66-year-old man was referred to our hospital with obstructive jaundice. Contrast-enhanced computed tomography images indicated a tumor located at the pancreatic head measuring 3.0 × 2.5 cm in diameter and invading the common bile duct. Cytological examination of the bile juice obtained by endoscopic retrograde cholangiopancreatography revealed adenocarcinoma cells. Pancreaticoduodenectomy was performed safely as radical resection.Interventions:Microscopically, the resected tumor consisted of tightly intermingled adenocarcinoma and neuroendocrine carcinoma components. On the immunohistochemical examination, p53 was ubiquitously positive in both components, whereas chromogranin A, synaptophysin and neuron-specific enolase, neuroendocrine markers, were limited to the neuroendocrine carcinoma component.Outcomes:Thus, such features of both adenocarcinoma and neuroendocrine carcinoma observed microscopically and immunohistochemically seemed to indicate a composite tumor.Lessons:The findings of this case suggest that adenocarcinoma and neuroendocrine carcinoma may be derived from a single cancer stem cell.

The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is difficult. We developed a tool to diagnose the depth of submucosal invasion in early colorectal cancer using artificial intelligence. We reviewed data from 196 patients who had undergone a preoperative colonoscopy at the Osaka University Hospital and Osaka International Cancer Institute between 2011 and 2018 and were diagnosed pathologically as having shallow submucosal invasion or deep submucosal invasion colorectal cancer. A convolutional neural network for predicting invasion depth was constructed using 706 images from 91 patients between 2011 and 2015 as the training dataset. The diagnostic accuracy of the constructed convolutional neural network was evaluated using 394 images from 49 patients between 2016 and 2017 as the validation dataset. We also prospectively tested the tool from 56 patients in 2018 with suspected early-stage colorectal cancer. The sensitivity, specificity, accuracy, and area under the curve of the convolutional neural network for diagnosing deep submucosal invasion colorectal cancer were 87.2% (258/296), 35.7% (35/98), 74.4% (293/394), and 0.758, respectively. The positive predictive value was 84.4% (356/422) and the sensitivity was 75.7% (356/470) in the test set. The diagnostic accuracy of the constructed convolutional neural network seemed to be as high as that of a skilled endoscopist. Thus, endoscopic image recognition by deep learning may be able to predict the submucosal invasion depth in early-stage colorectal cancer in clinical practice.