Clinicopathological Characteristics of Nonfunctional Pancreatic Neuroendocrine Neoplasms and the Effect of Surgical Treatment on the Prognosis of Patients with Liver Metastases: A Study Based on the SEER Database

Role of sex in liver tumor occurrence and clinical outcomes: A comprehensive review

Clinical research on sex-based differences in the manifestations, pathophysiology, and prevalence of several diseases, including those affecting the liver, has expanded considerably in recent years. Increasing evidence suggests that liver diseases develop, progress, and respond to treatment differently depending on the sex. These observations support the concept that the liver is a sexually dimorphic organ in which estrogen and androgen receptors are present, which results in disparities between men and women in liver gene expression patterns, immune responses, and the progression of liver damage, including the propensity to develop liver malignancies. Sex hormones play protective or deleterious roles depending on the patient's sex, the severity of the underlying disease, and the nature of precipitating factors. Moreover, obesity, alcohol consumption, and active smoking, as well as social determinants of liver diseases leading to sex-related inequalities, may interact strongly with hormone-related mechanisms of liver damage. Drug-induced liver injury, viral hepatitis, and metabolic liver diseases are influenced by the status of sex hormones. Available data on the roles of sex hormones and gender differences in liver tumor occurrence and clinical outcomes are conflicting. Here, we critically review the main gender-based differences in the molecular mechanisms associated with liver carcinogenesis and the prevalence, prognosis, and treatment of primary and metastatic liver tumors.

Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

Background

Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.

Method

A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.

Results

A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.

Conclusion

AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

Retracted: Clinicopathological Characteristics of Nonfunctional Pancreatic Neuroendocrine Neoplasms and the Effect of Surgical Treatment on the Prognosis of Patients with Liver Metastases: A Study Based on the SEER Database

[This retracts the article DOI: 10.1155/2022/3689895.].

Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database

BACKGROUND

The study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH) model in predicting the overall survival (OS) of patients with pancreatic neuroendocrine neoplasms (PNENs).

METHOD

The clinicopathological dataset obtained from the Surveillance, Epidemiology, and End Results database was randomly assigned to the training set and testing set at a ratio of 7:3. The concordance index (C-index) and integrated Brier score (IBS) were used to compare the predictive performance of the models. The accuracy of the model in predicting the 5-year and 10-year survival rates was compared using the receiver operating characteristic curve, decision curve analysis (DCA) and calibration curve.

RESULTS

This study included 3239 patients with PNENs in total. The DeepSurv model had the highest C-index of 0.7882 in the testing set and training set and the lowest IBS of 0.1278 in the testing set compared with the CoxPH, neural multitask logistic and random survival forest models (C-index = 0.7501, 0.7616, and 0.7612, respectively; IBS = 0.1397, 0.1418, and 0.1432, respectively). Moreover, the DeepSurv model had the highest accuracy in predicting 5- and 10-year OS rates (area under the curve: 0.87 and 0.90). DCA showed that the DeepSurv model had high potential for clinical decisions in 5- and 10-year OS models. Finally, we developed an online application based on the DeepSurv model for clinical use (https://whuh-ml-neuroendocrinetumor-app-predict-oyw5km.streamlit.app/).

CONCLUSIONS

All four models analyzed above can predict the prognosis of PNENs well, among which the DeepSurv model has the best prediction performance.

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

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.

Gender differences in pancreatic neuroendocrine neoplasms: A retrospective study based on the population of Hubei Province, China

OBJECTIVE

The aims of the present study were to investigate gender differences in the clinicopathological features, distant metastasis and prognosis of pancreatic neuroendocrine neoplasms (pNENs) in a Chinese population, and to identify any important gaps in the classification and management of pNENs relative to gender.

METHODS

Retrospective collection of the clinicopathological data of 193 patients with pathologically confirmed pNENs were analyzed and follow up was extended to observe the prognosis of the disease. Differences between genders in basic characteristics, clinical symptoms, comorbidities, and tumor parameters were analyzed.

RESULTS

There was no significant difference in females and males, however, moderately higher for females (52.8% vs. 47.2%), with the largest subgroup being 40~60 years of age (54.9%). Age at onset (P=0.002) and age at diagnosis (P=0.005) were both younger in females compared to males. Males lived more in urban areas and females lived more in rural areas (P=0.047). The proportion of smokers and alcohol drinkers was significantly higher in males than in females (P < 0.001). Non-functional pNENs were more frequent in males and functional pNENs in females (P=0.032). In women, functional status of the tumor was significantly associated with metastatic outcome (P=0.007) and functional tumors proved to be a protective factor compared to non-functional tumors (OR=0.090,95% CI: 0.011~ 0.752). There were no gender differences in tumor size, location, grade, stage or prognosis.

CONCLUSIONS

Gender differences in some clinicopathological features, and distant metastasis in patients with pNENs were identified, which suggested certain management details that justified emphasis based on gender.