Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Tao Zhang,Xuelei Ma,Hanyue Xu,Xinglong Liu,Xuan Zhou,Yichun Liu,Yuehua Zhang,Chaoyue Chen

doi:10.3389/fonc.2020.521831

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.

Highlights

Pancreatic neuroendocrine tumors (PNETs) are tumors that originate from the neuroendocrine system of the pancreas, accounting for 2%–10% of pancreatic tumors [1, 2]
We retrospectively reviewed a computer database of PNETs patients treated at our hospital from March 2011 to November 2019, yielded 201 patients who had treated for PNETs with their clinical records and Computed Tomography (CT) images
We found that the model constructed by the combination of DC + AdaBoost, DC + Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (Xgboost)+Random Forest (RF) was very valuable for the differential diagnosis of three pathological grades of PNET, and these models did not show over-fitting and underfitting

Summary

Introduction

Pancreatic neuroendocrine tumors (PNETs) are tumors that originate from the neuroendocrine system of the pancreas, accounting for 2%–10% of pancreatic tumors [1, 2]. Researches on pancreatic neuroendocrine tumors have received more and more attention. According to the World Health Organization’s classification system, PNETs are classified into three pathological grades according to mitotic count and Ki-67 index: G1, G2, G3. Different treatment options for different grades of tumors are different. The more accepted treatment is surgical resection. For the unresectable or metastatic PNETs, local treatment, chemotherapy and targeted therapy can be used as treatment options [5,6,7]. Tumors with different malignant grades will have an impact on the development of treatment options

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