Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers
Author:
Publisher
Baishideng Publishing Group Inc.
Subject
General Medicine
Reference23 articles.
1. The Pathologic Classification of Neuroendocrine Tumors
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4. Localized high-grade gastroenteropancreatic neuroendocrine tumors: Defining prognostic and therapeutic factors for a disease of increasing clinical significance
5. A Practical Approach to the Classification of WHO Grade 3 (G3) Well-differentiated Neuroendocrine Tumor (WD-NET) and Poorly Differentiated Neuroendocrine Carcinoma (PD-NEC) of the Pancreas
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1. Recent advances and future challenges in the diagnosis of neuroendocrine neoplasms;Minerva Endocrinology;2024-07
2. Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis;Frontiers in Oncology;2024-04-23
3. Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors;Academic Radiology;2023-12
4. Prediction of local tumor progression after microwave ablation for early-stage hepatocellular carcinoma with machine learning;Journal of Cancer Research and Therapeutics;2023-08
5. Machine learning-based model for prediction and feature analysis of recurrence in pancreatic neuroendocrine tumors G1/G2;Journal of Gastroenterology;2023-04-26
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