Inflammatory myofibroblastic tumor of the pancreatic neck misdiagnosed as neuroendocrine tumor: A case report.

BACKGROUND: Inflammatory myofibroblastic tumor (IMT) is a relatively rare tumor. The global incidence of IMT is less than 1%. There is no specific clinical manifestation. It usually occurs in the lungs, but the pancreas is not the predilection site. CASE SUMMARY: We present a case of a male patient, 51 years old, who was diagnosed with a pancreatic neck small mass on ultrasound one year ago during a physical examination. As he had no clinical symptoms and the mass was relatively small, he did not undergo treatment. However, the mass was found to be larger on review, and he was referred to our hospital. Since the primal clinical diagnosis was pancreatic neuroendocrine tumor, the patient underwent surgical treatment. However, the case was confirmed as pancreatic IMT by postoperative pathology. CONCLUSION: Pancreatic IMT is relatively rare and easily misdiagnosed. We can better under-stand and correctly diagnose this disease by this case report.

Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics.

BACKGROUND: Liver cancer is one of the most common malignant tumors, and ranks as the fourth leading cause of cancer death worldwide. Microvascular invasion (MVI) is considered one of the most important factors for recurrence and poor prognosis of liver cancer. Thus, accurately identifying MVI before surgery is of great importance in making treatment strategies and predicting the prognosis of patients with hepatocellular carcinoma (HCC). Radiomics as an emerging field, aims to utilize artificial intelligence software to develop methods that may contribute to cancer diagnosis, treatment improvement and evaluation, and better prediction. AIM: To investigate the predictive value of computed tomography radiomics for MVI in solitary HCC ≤ 5 cm. METHODS: A total of 185 HCC patients, including 122 MVI negative and 63 MVI positive patients, were retrospectively analyzed. All patients were randomly assigned to the training group (n = 124) and validation group (n = 61). A total of 1351 radiomic features were extracted based on three-dimensional images. The diagnostic performance of the radiomics model was verified in the validation group, and the Delong test was applied to compare the radiomics and MVI-related imaging features (two-trait predictor of venous invasion and radiogenomic invasion). RESULTS: A total of ten radiomics features were finally obtained after screening 1531 features. According to the weighting coefficient that corresponded to the features, the radiomics score (RS) calculation formula was obtained, and the RS score of each patient was calculated. The radiomics model exhibited a better correction and identification ability in the training and validation groups [area under the curve: 0.72 (95% confidence interval: 0.58-0.86) and 0.74 (95% confidence interval: 0.66-0.83), respectively]. Its prediction performance was significantly higher than that of the image features (P < 0.05). CONCLUSION: Computed tomography radiomics has certain predictive value for MVI in solitary HCC ≤ 5 cm, and the predictive ability is higher than that of image features.