ArticleFrontiers in oncology2020
Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GEP-NET radiomics: a systematic review and radiomics quality score assessment.European radiology · 2022Pooled it
- The Evolving Landscape of GEP-NENs in the Era of Precision Oncology: Molecular Insights into Tumor Heterogeneity.Cancers · 2025Review
- Article
- Construction of a prognostic survival model for colorectal cancer patients using CT image texture analysis: a prospective cohort study.Frontiers in oncology · 2025Article
- Radiomics model based on dual-energy CT venous phase parameters to predict Ki-67 levels in gastrointestinal stromal tumors.Frontiers in oncology · 2025Article
- Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma.BMC cancer · 2024Article
- A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors.European radiology · 2024Article
- A computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.Cancer medicine · 2024Article
- Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application.Abdominal radiology (New York) · 2024Review
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.Cancer medicine · 2023Article
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms-A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy.Frontiers in oncology · 2022Article
- Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.Frontiers in oncology · 2022Article
- Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study.Frontiers in surgery · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. MATERIALS AND
methodsA retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.
resultOur analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.
conclusionIn conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.
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