CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor

Zhao, Zengrui; Bian, Yun; Jiang, Hui; Fang, Xu; Li, Jing; Cao, Kai; C, Ma; Wang, Li; Zheng, Jianming; Yue, Xiaodong; Zhang, Huiran; Wang, Xiangxue; Madabhushi, Anant; Xu, Jun; Jin, Gang; Lu, Jianping

doi:10.1016/j.acra.2020.01.002

Cited by 29 publications

(25 citation statements)

References 36 publications

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“…It has been applied in modern medical care including diagnosis, risk stratification, virtual biopsy and radiogenomics (34). Several studies have investigated the utility of machine learning based-radiomics on the differentiation of pancreatic mucinous cystadenomas from pancreatic serous cystadenomas (35,36) and the prediction of PNETs grading (13,21,(37)(38)(39). However, no studies reported how to differentiate pancreatic cystadenomas from PNETs.…”

Section: Discussionmentioning

confidence: 99%

“…The management of patients is different due to the biological differences of PNETs and pancreatic cystadenomas. The endoscopic ultrasound fine-needle aspiration (EUS-FNA) is considered the best approach to diagnosis pancreatic tumors, but it is invasive and not completely accurate due to the small size of samples (13,14). Therefore, a preoperative differential diagnosis is vital to identify the most appropriate therapies and improve clinical management.…”

Section: Introductionmentioning

confidence: 99%

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Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

et al. 2021

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ObjectivesThe purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.MethodsIn this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.ResultsThe predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.ConclusionsRadiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

et al. 2021

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“…Recent studies have shown that CT and MRI images can have quantitative features that are associated with tumor aggressiveness [50][51][52][53][54][55][56][57] (Table 5). These studies utilized imaging modalities such as MRI and CT.…”

Section: Determination Of Tumor Gradementioning

confidence: 99%

Update on quantitative radiomics of pancreatic tumors

et al. 2021

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Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.

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“…In light of these uncertainties, there is interest in developing a system for preoperative risk stratification of pancreatic NETs, which will help guide therapeutic directions in support of endocrine oncologists and surgeons 70 , 71 . Studies have utilized both conventional machine learning and deep learning on preoperative CT and MRI to classify pancreatic NET grade with robust accuracy in pathology-confirmed tumours 4 , 72 – 75 . Importantly, the development of classification boundaries for future studies requires consensus in the partitioning of tumour grades.…”

Section: Diagnosticsmentioning

confidence: 99%