Primary Hepatic Mixed Neuroendocrine-Non-Neuroendocrine Neoplasm

Zou, Rui-Qi; Hu, Hai‐Jie; Li, Fuyu

doi:10.15403/jgld-3732

Cited by 1 publication

(2 citation statements)

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“…As reported, the Ki-67 index has been proven to be a marker of other various tumor recurrences; nevertheless, it works effectively for GP-NENs as well. (3) The TNM stage of the tumor is regarded as a classic tumor prognostic indicator. For patients with a high value in TMN stage and the primary tumor cannot be completely resected, bad prognosis was expected.…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

“…Gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs) are a rare malignancy derived from gastrointestinal neuroendocrine cells [ 1 , 2 ]. The incidence of GP-NENs has been rising in recent years due to factors such as poor dietary habits, heredity, excessive stress, and environmental pollution in China [ 3 , 4 ]. According to the degree of differentiation, the World Health Organization (WHO) classifies GP-NENs into two categories, well-differentiated gastrointestinal pancreatic neuroendocrine tumor (GP-NET) and poorly differentiated gastrointestinal pancreatic neuroendocrine carcinoma (GP-NEC).…”

Section: Introductionmentioning

confidence: 99%

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Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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Purpose. Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). Methods. In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People’s Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group ( n = 108 ) and the recurrent group ( n = 117 ), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data ( P < 0.05 ) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set. Results. Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05 ). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set. Conclusion. The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients.

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Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%