Clinicopathological characteristics and prognosis of 232 patients with poorly differentiated gastric neuroendocrine neoplasms

Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma

BACKGROUND

This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).

PATIENTS AND METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) were analyzed and split into training and validation groups (7:3 ratio). CS profiles for patients with GNEC were examined in the full cohort. We utilized random survival forests (RSFs) and least absolute shrinkage and selection operator (LASSO) regression, alongside stepwise Cox regression, for variable selection. A CS-based nomogram was developed on the basis of key prognostic factors, followed by risk stratification and model validation.

RESULTS

We included 654 patients with GNEC in our study, with 457 assigned to the training set and 197 to the validation set. The CS analysis demonstrated that the probability of achieving 5-year CS improved from 48% immediately after diagnosis to 68%, 81%, 88%, and 94% after surviving an additional year (i.e., at 1, 2, 3, and 4 years, respectively). Through the use of RSFs and LASSO regression, combined with multivariable regression analysis, we identified the optimal combination of prognostic factors, which included age, tumor grade, tumor stage, surgery, and chemotherapy. Utilizing these prognostic indicators, we successfully developed a nomogram model that incorporated CS and effectively stratified these patients by risk. Subsequent performance analyses further validated the superior efficacy of the nomogram.

CONCLUSIONS

Our study highlights the value of CS in GNEC prognosis. The nomogram offers dynamic, individualized survival predictions, supporting personalized treatment strategies.

Construction and validation of a novel prognostic nomogram for patients with poorly differentiated gastric neuroendocrine neoplasms

BACKGROUND

The prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms (PDGNENs) is dismal and related research is limited.

AIM

To investigate the prognostic factors, and validate a novel prognostic nomogram for PDGNEN patients.

METHODS

We conducted a retrospective study using clinical and pathological data from PDGNEN patients treated at the First Medical Center of the Chinese PLA General Hospital from January 2000 to June 2023. Overall survival (OS) differences were assessed with the Log-rank test and Kaplan-Meier survival curves. Cox regression analysis identified independent risk factors for prognosis. Model performance was evaluated using Harrell’s concordance index, receiver operating characteristic analysis, area under the curve, calibration curves, and decision curve analysis (UDC), including the area under the UDC.

RESULTS

The study included 336 patients (227 with neuroendocrine carcinoma and 109 with mixed adenoneuroendocrine carcinoma). The average age was 62.7 years. The cohort comprised 80 (24.7%) patients in stage I, 146 (42.9%) in stage II, 62 (18.1%) in stage III, and 48 (14.3%) in stage IV. Significant differences in OS were observed across tumor-node-metastasis stages (P < 0.001). Multivariate analysis showed age, Ki-67 index, invasion depth, lymph node metastasis, distant metastasis, and platelet-to-lymphocyte ratio as independent risk factors. We developed a nomogram with a concordance index of 0.779 (95% confidence interval: 0.743-0.858). Receiver operating characteristic analysis showed area under the curves for 1-year, 3-year, and 5-year OS predictions of 0.865, 0.850, and 0.890, respectively. The calibration curve demonstrated good agreement with actual outcomes. The area under the UDC for the nomogram vs the 8^th American Joint Committee on Cancer tumor-node-metastasis staging system were 0.047 vs 0.027, 0.291 vs 0.179, and 0.376 vs 0.216 for 1-year, 3-year, and 5-year OS, respectively.

CONCLUSION

PDGNENs are predominantly found in older men, often in advanced stages at diagnosis, resulting in poor prognosis. The established nomogram demonstrates strong predictive capability and clinical utility.

Clinicopathologic characteristics and prognostic factors of pure gastric neuroendocrine carcinoma patients undergoing radical surgery

BACKGROUND

There is a low incidence of gastric neuroendocrine carcinoma (G-NEC), but it is associated with particularly aggressive biological behaviours and poor prognosis compared with other gastric neoplasms. Our study aimed to investigate the clinicopathologic traits and prognostic factors of patients with pure gastric neuroendocrine carcinoma treated with radical surgery.

METHODS

We retrospectively analysed 60 patients with pure G-NEC who underwent radical gastrectomy between March 2010 and May 2019. 68 patient who underwent curative surgery for mixed gastric adenoneuroendocrine carcinoma (G-ANEC) from August 2012 to June 2022. The relationships between the clinicopathologic characteristics of pure G-NEC and overall survival (OS) and disease-free survival (DFS), as well as the comparison of pure-NEC with G-ANEC in terms of prognosis and treatment regimens, were evaluated using the Kaplan-Meier method and (or) Cox regression.

RESULTS

The gastroesophageal junction (GEJ) was the predilection site for G-NEC. Tumor location, histology, and lymph node metastasis status were independent prognostic factors for OS (P < 0.05). Pathological T stage and the presence or absence of lymph node metastasis were independently associated variables with DFS (P = 0.019 and P = 0.041). Large cell neuroendocrine carcinoma (LCGNEC) did not differ statistically from the small cell neuroendocrine carcinoma (SCGNEC) (P = 0.314) for OS, while mixed type (MGNEC) vs. LCGNEC did differ significantly (P = 0.031). There were no significant differences in OS and DFS between etoposide and cisplatin (EP) and S-1 + oxaliplatin (SOX) / oxaliplatin + capecitabine (XELOX). The study of 106 patients found no significant impact of NEC proportion on OS (P = 0.438) or DFS (P = 0.079). Neoadjuvant/adjuvant chemotherapy targeting NEC versus adenocarcinoma showed no statistical difference in OS (P = 0.415, P = 0.350), but there was a trend toward longer survival with NEC-targeted regimen.

CONCLUSIONS

The LCGNEC did not differ statistically from the SCGNEC for OS, while the MGNEC vs. LCGNEC were different. The prognosis of G-NEC was related to the tumor location, histology, postoperative T stage, and lymph node metastasis. For gastric neuroendocrine carcinoma, prognosis does not differ statistically by NEC proportion. Chemotherapy regimens targeting lymph node metastases with an NEC component maybe better prognosis than those focusing on the adenocarcinoma component.

Survival analysis of gastric malignancy patients: identifying key prognostic factors

Background

Gastric malignancies are common worldwide, with a high incidence rate and mortality. Relevant studies are needed to further demonstrate the harmfulness of gastric malignancies to promote awareness of its prevention. The aim of the study was to analyze the clinicopathological features and prognosis of gastric malignancies.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was used to obtain the clinical data of 122,793 patients diagnosed with gastric malignancies from 2000 to 2019. Along with performing univariate and multivariate analyses, the associated survival rates of each variable were analyzed using SPSS software. Columnar line graph prediction models were developed and validated using R software.

Results

In total, 122,793 gastric malignancy patients were included in this study, including 74,303 males (60.5%) and 48,490 females (39.5%). The predominant age group among patients was 60-74 years, comprising a total of 45,603 individuals (37.1%). The follow-up time was 0-239 months, the median follow-up time was 11.7 months, and 91,869 patients (74.8%) died. Gastric adenocarcinoma was the main pathological type, accounting for 96,259 patients (82.7%). The main disease site was the cardia of the stomach, accounting for 34,019 patients (34.0%); most (109,706; 89.3%) patients lived in cities. Univariate and multivariate analyses showed that gender, age, tumor size, tumor location, American Joint Committee on Cancer (AJCC) stage, pathological type, rural/urban, and treatment were independent risk factors for prognosis (P<0.001). The Concordance index (C-index) of the nomogram prognostic model was 0.763±0.002, and the areas under the receiver operating characteristic (ROC) curve (AUC) of the 1-, 3-, and 5-year survival rates were 0.76, 0.79, and 0.79, respectively. The calibration curve showed that the predicted survival rate of the nomogram was in good agreement with the observed survival rate.

Conclusions

The prognosis for tumors located in the greater curvature of the stomach is relatively favorable. The level of care available in a patient's city is directly correlated with their prognosis. Notably, the outcomes for gastric stromal tumors (GSTs) and gastric neuroendocrine neoplasms (G-NENs) are significantly more favorable compared to other pathological types. Patients who meet surgical criteria should undergo surgery as early as possible to enhance survival duration.

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.

[Characteristics of gastric neuroendocrine tumors and the PDX-1 transcription factor expression]

OBJECTIVE

To study the features of gastric neuroendocrine tumors (NETs) and the diagnostic and prognostic significance of PDX-1 expression in them.

MATERIAL AND METHODS

207 NETs identified in 56 men and 115 women (59 had multiple NETs), and 94 cases of gastric cancer (comparison group) were studied morphologically and immunohistochemically.

RESULTS

In more than half of the cases (54.93%), NETs were localized in the body of the stomach; the cardiac and antral parts of the stomach accounted for 8.64% and 11.73%, respectively. NETs of the cardiac region predominated in men, and of the body and antrum - in women. NETs of the cardiac region predominated in men, and of the body and antrum - in women. The vast majority of NETs were highly differentiated (89.20%), of which Grade 1, 2 and 3 were 55.41%, 40.76% and 3.82%, respectively. Neuroendocrine carcinomas (NEC) accounted for 10.80% of all NET cases. NECs were more often localized in the cardiac part of the stomach and accounted for 35.71% of all NETs in the cardiac part. The share of NEC among all NETs of the antrum was 15.79%, of the body of the stomach - only 3.37%. Metastases were found in 17.90% of NETs. Expression of PDX-1 was detected in 44.73% of NETs, 70% of NECs and 74.50% of gastric cancers.

CONCLUSION

PDX-1 is involved in the mechanisms of precancerous and cancerous lesions of the stomach and its overexpression is detected in the majority of the most malignant NETs and gastric cancers.

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.

Endoscopic features of gastric neuroendocrine carcinoma

BACKGROUND AND AIM

The endoscopic features of gastric neuroendocrine carcinoma (G-NEC) have not been clarified; therefore, they were investigated in relation to clinicopathological findings.

METHODS

Consecutive patients with G-NECs who had undergone endoscopic or surgical resection at our institution between January 2005 and March 2022 were included in this retrospective study. The endoscopic and clinicopathological findings of the lesions were analyzed to provide information of diagnostic value. In addition, cases of gastric neuroendocrine tumor (G-NET) and common-type gastric adenocarcinoma treated in the same study period were identified to compare the endoscopic findings between each G-NEC versus G-NET, and G-NEC versus common-type gastric adenocarcinoma. Patients with common-type gastric adenocarcinoma were matched for age, sex, tumor size, and depth of tumor invasion in 1:3 ratio.

RESULTS

Among 15 patients with 15 G-NECs, submucosal tumor-like marginal elevation (87%), adherent white coat (67%), and ulceration with a distinct border (60%) were characteristic endoscopic findings in white-light images. Magnifying narrow-band imaging endoscopy revealed an absent microsurface (MS) pattern plus disrupted irregular microvessel (MV) in five (71%) of seven cases with evaluable MS and MV patterns. The area with an absent MS pattern plus disrupted irregular MV corresponded to the histological finding of NEC component in all five cases. These endoscopic features were all significantly more frequent in G-NECs than G-NETs (n = 22) or common-type gastric adenocarcinomas (n = 45).

CONCLUSIONS

These endoscopic features should be taken into consideration to increase the index of suspicion and to improve the accuracy of target biopsies for G-NEC.

Differences in clinicopathology and prognosis between gastroesophageal junctional and gastric non-cardiac neuroendocrine carcinomas: a retrospective comparison study of consecutive 56 cases from a single institution in China

Gastric neuroendocrine carcinoma (NEC), including mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN), is uncommon and differences in clinicopathological features and outcomes of NEC arising in various gastric regions remain elusive. We investigated 56 consecutive NECs identified among 3961 gastrectomies performed at our center between 2005 and 2021. We then compared clinicopathological characteristics and prognosis between gastroesophageal junctional (GEJ) NECs (N=39) and gastric non-cardiac NECs (N=17). No significant difference was found between the two groups in age, gender, tumor size, mixed non-neuroendocrine carcinoma component, MiNEN, NEC type, metastatic NEC component in lymph nodes, tumor infiltrating lymphocyte, lymph node metastasis, lymphovascular or perineural invasion, intestinal metaplasia in adjacent non-neoplastic mucosa, and expression of P53, PD-L1, TTF-1, HER2, and Ki-67. However, compared to gastric non-cardiac NECs, GEJ NECs displayed a significantly higher frequency of prevalence (2.79% versus 0.66%), pT3-T4 (92.3% versus 64.7%), advanced pathological stage (IIb-IV) (76.9% versus 47.1%), and a significantly lower 5-year overall survival rate (46.1% versus 73.1%) (P<0.05). The GEJ location was the only independent risk factor for overall survival. In stage-stratified comparisons, patients with stage II GEJ NEC demonstrated a significantly lower 5-year survival rate than those with gastric non-cardiac NEC at the same stage. Compared to non-NECs matched for age, gender, tumor location, and pathological summary stage, GEJ NEC was associated with significantly worse prognosis. In conclusion, GEJ NEC showed deeper invasion, more advanced pathological stages, and worse prognosis than gastric non-cardiac NEC. The findings provide pathologic evidence for individualized management strategies for patients with GEJ NEC. Future studies with larger samples are needed.

Prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms: a multi-center study in China

Background: The incidence of poorly differentiated gastric neuroendocrine neoplasms (G-NENs) has been increasing during the past decades. Methods: A total of 183 patients diagnosed with poorly differentiated G-NENs were enrolled from eight hospitals during 2010-2019 in China. All cases included have accepted abdominal surgery in tertiary hospitals. Result: T3 (HR: 2.66, p = 0.019), T4 (HR: 3.62, p = 0.005), stage IV (HR: 5.67, p < 0.001), vascular invasion (HR: 1.59, p = 0.048) were independent risk factors for poor prognosis of poorly differentiated G-NENs. In stratified analysis, for patients with stage III tumors, those treated with chemotherapy had significantly longer survival than those accepting surgery alone. Conclusion: T3/T4 stage, TNM stage IV and vascular invasion were independent negative prognostic factors for patients with poorly differentiated G-NENs. Patients with stage III tumors can benefit from chemotherapy. Highly selected patients with stage IV tumors may also benefit from surgery.

The Clinicopathological Features and Overall Survival of Patients With Gastric Neuroendocrine Carcinoma

OBJECTIVES

Gastric neuroendocrine carcinoma (GNEC) is a class of rare histological subtypes in gastric cancer (GC). This retrospective case-control study aimed to explore the clinicopathological features and overall survival (OS) of patients with GNEC.

METHODS

A large population of GNEC and intestinal-type GC (IGC) patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The 1:1 propensity score matching (PSM) analysis was initiated to adjust the confounders between GNEC and IGC cohorts. Kaplan-Meier (KM) plots with log-rank tests were used to compare the survival differences in GNEC versus IGC. Additionally, Cox proportional hazard regression models were adopted to characterize the prognostic factors relevant to OS of the GNEC patients.

RESULTS

An entity of 4596 patients were collected, including 3943 (85.8%) IGC patients and 653 (14.2%) GNEC patients. The PSM analysis well-balanced all confounders in GNEC versus IGC (all P > .05). The KM plots showed that GNEC had significantly superior OS to IGC both before and after PSM analysis. Before PSM, the median OS was 52 (33.6-70.4) months in GNEC versus 32 (29.3-34.7) months in IGC (P  =  .0015). After PSM, the median OS was 26 (18.3-33.7) months in GNEC versus 21 (17.7-24.3) months in IGC (P  =  .0039). Stratified analysis indicated that GNEC had superior survivals to IGC in early stage patients and those who received surgery. In Cox regression analysis, age ≥ 60, tumor size > 50 mm, stage II-IV, T2, and N3 were independent risk factors for the GNEC patients (hazard ratio [HR]>1, P < .05). By contrast, year 2010 to 2015, female, and surgery were independent protective factors for these patients (HR < 1, P < .05).

CONCLUSIONS

GNEC has unique clinicopathological features quite different from IGC and may have a superior survival to IGC in early stage patients. The prognostic factors identified here may assist the clinicians to more individually treat these patients.