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

Abstract

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.

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

“…In terms of disease recurrence appraisal, clinical data may significantly impact when added to radiomic features, as shown by one study [ 121 ] on 225 GP-NETs. Considering radiomics scores and clinical pathological factors (age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage), An et al [ 121 ] were able to properly assess the non-recurrent group and the recurrent group more accurately than the individual models, which analyzed independently (combined model with an AUC of 0.824; clinical data model with an AUC of 0.786, and radiomics model with an AUC of 0.712).…”

“…First-order statistics were also the best predictors for distinguishing PNETs from PDACs [ 63 , 64 ]. Ultimately, the best performance was achieved when radiomics features were combined with clinical features [ 69 , 110 , 111 , 121 ]. All this evidence still needs to be reproduced in a larger cohort of patients and in a prospective manner to ensure reliability.…”

Development and validation of a contrast-enhanced CT-based radiomics nomogram for preoperative diagnosis in neuroendocrine carcinoma of digestive system

“…In recent years, radiomics has been gradually and widely used in the diagnosis of cancers (10), identification of molecular typing of tumors (11), prediction of survival status of patients (12), and the use of imaging genomics to analyze the relationship between imaging features and genomic features to dissect tumor heterogeneity (13). Radiomics studies targeting NETs are also increasing, and radiomics can be applied in the diagnosis of pancreatic NETs (14), predicting the grading of pancreatic NETs (15), determining the prognosis of NETs (16), and assessing the effects of drug therapy for NETs (17). However, there are few radiomics studies for NECs, Wang et al (18) identified gastric NECs from gastric adenocarcinoma with CT radiomics.…”