Value of computed tomography evaluation in pathologic classification and prognosis prediction of gastric neuroendocrine tumors

Synergizing traditional CT imaging with radiomics: a novel model for preoperative diagnosis of gastric neuroendocrine and mixed adenoneuroendocrine carcinoma

Objective

To develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features.

Methods

We retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong's test. The models' diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases.

Results

Tumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P < 0.05). The clinical model's AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC of 0.809 (training) and 0.802 (validation). The combined model's AUCs were 0.853 (training) and 0.812 (validation), significantly outperforming the clinical model (P < 0.05). Subgroup analysis revealed that the combined model exhibited acceptable performance in differentiating g-NEC from g-ADC and g-MANEC from g-ADC, with AUC of 0.887 and 0.823 in the training cohort and 0.852 and 0.762 in the validation cohort.

Conclusion

A combined model based on traditional CT imaging and radiomic features provides a non-invasive and effective preoperative diagnostic method for differentiating g-(MA)NEC from g-ADC.

Gastric mixed neuroendocrine non-neuroendocrine neoplasms

The uncommon tumour known as gastric mixed neuroendocrine-non-neuroendocrine neoplasms (G-MiNENs) is made up of parts of neuroendocrine carcinoma and adenocarcinoma. The biological and clinical features are different from those of gastric adenocarcinoma. Their pathophysiology, diagnostic standards, and clinical behaviour have all been the subject of lengthy debates, and their nomenclature has undergone multiple changes. Its emergence has created new challenges in the classification and diagnosis of gastric tumours. This review will update information on the topic, covering molecular aspects, diagnostic criteria, treatment, and prognostic factor discovery. It will also provide a historical context that will aid in understanding the evolution of the idea and nomenclature of mixed gastric tumours. Additionally, it will provide the reader a thorough understanding of this difficult topic of cancer that is applicable to real-world situations.

Deep learning radiomics analysis based on computed tomography for survival prediction in gastric neuroendocrine neoplasm: a multicenter study

Background

Survival prediction is crucial for patients with gastric neuroendocrine neoplasms (gNENs) to assess the treatment programs and may guide personalized medicine. This study aimed to develop and evaluate a deep learning (DL) radiomics model to predict the overall survival (OS) in patients with gNENs.

Methods

The retrospective analysis included 162 consecutive patients with gNENs from two hospitals, who were divided into a training cohort, internal validation cohort (The First Affiliated Hospital of Zhengzhou University; n=108), and an external validation cohort (The Henan Cancer Hospital; n=54). DL radiomics analysis was applied to computed tomography (CT) images of the arterial phase and venous phase, respectively. Based on pretreatment CT images, two DL radiomics signatures were developed to predict OS. The combined model incorporating the radiomics signatures and clinical factors was built through the multivariable Cox proportional hazards (CPH) method. The combined model was visualized into a radiomics nomogram for individualized OS estimation. Prediction performance was assessed with the concordance index (C-index) and the Kaplan-Meier (KM) estimator.

Results

The DL-based radiomics signatures based on two phases were significantly correlated with OS in the training (C-index: 0.79-0.92; P<0.01), internal validation (C-index: 0.61-0.86; P<0.01), and external validation (C-index: 0.56-0.75; P<0.01) cohorts. The combined model integrating radiomics signatures with clinical factors showed a significant improvement in predictive performance compared to the clinical model in the training (C-index: 0.86 vs. 0.80; P<0.01), internal validation (C-index: 0.77 vs. 0.71; P<0.01), and external validation (C-index: 0.71 vs. 0.66; P<0.01) cohorts. Moreover, the combined model classified patients into high-risk and low-risk groups, and the high-risk group had a shorter OS compared to the low-risk group in the training cohort [hazard ratio (HR) 3.12, 95% confidence interval (CI): 2.34-3.93; P<0.01], which was validated in the internal (HR 2.51, 95% CI: 1.57-3.99; P<0.01) and external validation cohort (HR 1.77, 95% CI: 1.21-2.59; P<0.01).

Conclusions

DL radiomics analysis could serve as a potential and noninvasive tool for prognostic prediction and risk stratification in patients with gNENs.

The assessment of Ki-67 for prognosis of gastroenteropancreatic neuroendocrine neoplasm patients: a systematic review and meta-analysis

Background

Neuroendocrine neoplasm (NEN) is a group of rare tumors. Among which, gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) is the most common group. The World Health Organization (WHO) classified these tumors into three different grades (G1, G2, and G3) based on Ki-67 and mitotic rate, and updated the classification in 2019. Several previous studies proved that Ki-67 was related to tumor prognosis, but others still reported that Ki-67 had no predictive value for tumor prognosis. There are different conclusions between studies regarding the correlation between Ki-67 and tumor prognosis, and there is a lack of studies about this correlation of GEP-NENs. Further analysis is still needed to evaluate the prognostic value of Ki-67 in GEP-NENs, to provide reference for clinical decisions.

Methods

A total of 303 studies were retrieved that included Ki-67, GEP-NENs, prognosis, survival, and other subject terms and keywords. We excluded studies that did not show complete Ki-67 index, number of patients and 5-year survival data available for meta-analysis, non-cohort studies, articles published before 2000 or not published in English. Fifteen studies were finally included to assess the value of Ki-67 in the prognosis of patients with GEP-NENs using a random-effects model.

Results

The cumulative 5-year survival rate for GEP-NEN G1 (Ki-67 ≤2%), G2 (Ki-67 2-20%) and G3 (Ki-67 >20%) was 86%, 65%, 25% respectively. The 5-year survival rate of GEP-NEN G1 (Ki-67 <3%, first revised in WHO classification 2017, redefined WHO classification 2019) and G1 (Ki-67 ≤2%, WHO classification 2010) was 97% and 84% respectively.

Conclusions

The overall prognosis of GEP-NENs patients showed a decreasing trend with the increase of Ki-67, which confirmed the significance of Ki-67 index as a prognostic marker for the prognosis of GEP-NENs. Increasing the cut-off value of Ki-67 index for G1 grade from ≤2% to <3% according to WHO classification 2019 did not significantly decrease the 5-year survival rate.

Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm

Purpose

The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN).

Methods and Materials

A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated.

Results

The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice.

Conclusions

The R-signature could be used to stratify patients with GNEN into high and low risk groups. Furthermore, the combined radiomics-clinical nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis

OBJECTIVE

The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs).

METHODS

Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC).

RESULTS

In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621).

CONCLUSIONS

The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

Gastric neuroendocrine neoplasms: a primer for radiologists

Gastric neuroendocrine neoplasms are uncommon tumors with variable differentiation and malignant potential. Three main subtypes are recognized: type 1, related to autoimmune atrophic gastritis; type 2, associated with Zollinger-Ellison and MEN1 syndrome; and type 3, sporadic. Although endoscopy alone is often sufficient for diagnosis and management of small, indolent, multifocal type 1 tumors, imaging is essential for evaluation of larger, high-grade, and type 2 and 3 neoplasms. Hypervascular intraluminal gastric masses are typically seen on CT/MRI, with associated perigastric lymphadenopathy and liver metastases in advanced cases. Somatostatin receptor nuclear imaging (such as Ga-68-DOTATATE PET/CT) may also be used for staging and assessing candidacy for peptide receptor radionuclide therapy. Radiotracer uptake is more likely in well-differentiated, lower-grade tumors, and less likely in poorly differentiated tumors, for which F-18-FDG-PET/CT may have additional value. Understanding disease pathophysiology and evolving histologic classifications is particularly useful for radiologists, as these influence tumor behavior, preferred imaging, therapy options, and patient prognosis.

New Developments in Gastric Neuroendocrine Neoplasms

Purpose of Review

Gastric neuroendocrine neoplasms (g-NENs) are a rare type of stomach cancer. The three main subtypes have different pathogeneses, biological behaviours and clinical characteristics, so they require different management strategies. This article will provide an overview of g-NENs and highlight recent advances in the field.

Recent Findings

Molecular profiling has revealed differences between indolent and aggressive g-NENs, as well as a new somatic mutation responsible for some familial type I g-NENs. Novel biomarkers have been developed which will hopefully improve diagnosis, treatment, risk stratification and follow-up. Patient treatment is also changing, as evidence supports the use of less aggressive options (e.g. endoscopic surveillance or resection) in some patients with more indolent tumours.

Summary

g-NEN heterogeneity poses challenges in understanding and managing this rare disease. More basic science research is needed to investigate molecular pathogenesis, and future larger clinical studies will hopefully also further improve treatment and patient outcomes.