Machine learning-based model for prediction and feature analysis of recurrence in pancreatic neuroendocrine tumors G1/G2.

BACKGROUND: Pancreatic neuroendocrine neoplasms (PanNENs) are a heterogeneous group of tumors. Although the prognosis of resected PanNENs is generally considered to be good, a relatively high recurrence rate has been reported. Given the scarcity of large-scale reports about PanNEN recurrence due to their rarity, we aimed to identify the predictors for recurrence in patients with resected PanNENs to improve prognosis. METHODS: We established a multicenter database of 573 patients with PanNENs, who underwent resection between January 1987 and July 2020 at 22 Japanese centers, mainly in the Kyushu region. We evaluated the clinical characteristics of 371 patients with localized non-functioning pancreatic neuroendocrine tumors (G1/G2). We also constructed a machine learning-based prediction model to analyze the important features to determine recurrence. RESULTS: Fifty-two patients experienced recurrence (14.0%) during the follow-up period, with the median time of recurrence being 33.7 months. The random survival forest (RSF) model showed better predictive performance than the Cox proportional hazards regression model in terms of the Harrell's C-index (0.841 vs. 0.820). The Ki-67 index, residual tumor, WHO grade, tumor size, and lymph node metastasis were the top five predictors in the RSF model; tumor size above 20 mm was the watershed with increased recurrence probability, whereas the 5-year disease-free survival rate decreased linearly as the Ki-67 index increased. CONCLUSIONS: Our study revealed the characteristics of resected PanNENs in real-world clinical practice. Machine learning techniques can be powerful analytical tools that provide new insights into the relationship between the Ki-67 index or tumor size and recurrence.

A case of poorly differentiated mucinous carcinoma of the gallbladder that exhibited a peculiar morphology.

A 65-year-old female presented with an elevated lesion that was identified in the gallbladder fundus via abdominal ultrasound during a medical checkup. The tumor was a pedunculated lesion, measuring 30mm in diameter, that exhibited a blood flow pattern with gradual dense staining throughout the equilibrium phase on the abdominal contrast computed tomography and a high signal intensity on T2-weighted magnetic resonance imaging. Histopathological findings revealed the proliferation of poorly differentiated adenocarcinoma, including signet ring cells, throughout the tumor along with the formation of a mucous lake. The patient was consequently diagnosed with poorly differentiated mucinous carcinoma of the gallbladder.