Clinical and pathological characteristics of and predictive model for colorectal neuroendocrine tumors
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PROVIDER: S-EPMC11336838 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
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Project description:Background/aimsGastric neuroendocrine tumors (GNETs), once rare, have become more prevalent due to the increased use of endoscopy and increased physician awareness. The clinical characteristics and long-term outcomes of GNET management were explored in this study.MethodsThe clinical data of 69 patients who treated at Seoul National University Hospital between January 2013 and October 2023 were retrospectively studied. Baseline characteristics, recurrence rates, associated factors, and overall survival rates were analyzed.ResultsOf the tumors, 71.0% were grade 1, 24.6% were grade 2, 1.4% were grade 3, and 2.9% were poorly differentiated. In terms of tumor type, 69.6% were type I, 1.4% were type II, and 29.0% were type III. A significant proportion of patients with grade 1 tumors received more endoscopic treatment, whereas a significant proportion of patients with grade 2 tumors underwent surgery or chemotherapy (p=0.015). The overall 5-year survival and recurrence rates were 93.8% and 7.25% (5/69), respectively. Among five patients who experienced recurrence, three had metachronous recurrence, all of which were type I; the remaining two patients exhibited distant hepatic metastasis, encompassing types I and III. The time to recurrence was 1 to 9.8 years. Margin positivity (p=0.002) and invasion deeper than the submucosal layer (p=0.007) were associated with higher recurrence rates. However, there was no significant association between recurrence and intestinal metaplasia, atrophic gastritis, or Helicobacter pylori infection.ConclusionsMost patients with GNETs in this study had grade I and type I tumors, and the overall prognosis was favorable. Patients with risk factors for recurrence warrant further investigation. Those presenting margin positivity or deep invasion after resection should be closely monitored and undergo follow-up examinations, as necessary.
| S-EPMC11907262 | biostudies-literature
Project description:ObjectivesTo develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner.MethodsPatients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model.ResultsA total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001).ConclusionsAn interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability.Clinical relevance statementThe radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy.Key points• A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy.
| S-EPMC10873440 | biostudies-literature
Project description:INTRODUCTION:Neuroendocrine tumors (NET) of the colon and sigmoid colon are uncommon compared to colorectal adenocarcinoma. Few reports have been made of NET of the colon and sigmoid colon that presents with peritonitis and large bowel obstruction. CASE PRESENTATION:Here, we report two cases of NET of the colon and sigmoid colon, which were diagnosed and treated at our institution. In our first case, a 66-year-old man with a history of abdominal distension was diagnosed with NET via histopathology of the sigmoid colon. The second case involved a 45-year-old woman with the chief complaints of abdominal distention and inability to defecate; specimen histopathology of the descending colon showed neuroendocrine carcinoma features. Clinical outcome was very poor in our patients: eight months after the resection, the second patient demonstrated a sign of metastasis on the liver. CONCLUSION:An uncommon case of colon and sigmoid colon carcinoma with neuroendocrine and diagnostic difficulties precludes an exact description of the initial diagnostic criteria and management. Thus, our case series offers an overview of initial symptoms, radiological and histopathological features for early diagnosis, and proper management of NET.
| S-EPMC7306531 | biostudies-literature
Project description:BackgroundCurrently, the tumor, node, and metastasis (TNM) staging system has a limited value in prognostic stratification for neuroendocrine tumors of the lung (NETL). A specific pathological staging system was therefore explored.MethodsTwo cohorts were assessed: the training cohort was composed of surgically treated patients from the Surveillance, Epidemiologic, and End Results (SEER) database [2004-2015]; the Shanghai cohort included Shanghai resident patients treated at Shanghai Pulmonary Hospital [2009-2018]. Multivariable Cox regression analysis was performed to identify factors associated with overall survival. A new staging system was proposed based on survival tree, and was further compared with the 8th edition of the TNM staging system.ResultsIn the training set (n=3,204), multivariate Cox analysis showed that tumor histotype and nodal status were independently associated with survival, but not T stage. Therefore, by incorporating NETL histotype (G1, low-grade typical pulmonary carcinoids; G2, intermediate-grade atypical pulmonary carcinoids; G3, high-grade large-cell neuroendocrine carcinomas) and N stage, a new staging system was developed: IA, G1N0; IB, G1N1 or G2N0; II, G1N2, G2N1-2, or G3N0; III, G3N1-2. Five-year survival rates were 91.2%, 81.3%, 50.2% and 27.6% for the new stages IA to III in the validation set (n=3,204), respectively (P<0.001). Additionally, the new staging system had significantly better predictive ability than the TNM staging system, in both the SEER [C-index, 0.75 vs. 0.62; net reclassification improvement (NRI), 0.62; integrated discrimination improvement (IDI), 20%] and Shanghai (IDI, 8%) cohorts. Based on the new staging system, adjuvant chemotherapy conferred a significantly better survival in stage-III NETL cases (HR =0.34, 95% CI, 0.25-0.45).ConclusionsThe new pathological staging system can better predict NETL prognosis than the 8th edition of the TNM staging system, with the potential to guide postoperative treatment.
| S-EPMC8039668 | biostudies-literature
Project description:Neuroendocrine tumors (NET) of the lung constitute a rare entity of primary lung malignancies that often exhibit an indolent clinical course. Epigenetics-related differences have been described previously for lung NET, but the clinical significance remains unclear. In this study, we performed genome-wide methylation analysis using the Infinium MethylationEPIC BeadChip technology on FFPE tissues from lung NET treated at two academic centers. We aimed to investigate the methylation profiles of known prognostic subgroups. In total, 54 tissue samples from primary lung NET were analyzed, of which 37 were typical carcinoids (TC) and 17 atypical carcinoids (AC). Overall, 25/53 patients (47.2%) developed metastases throughout the disease course, 14/26 (53.8%) had a positive somatostatin receptor (SSTR) scan, and 7/28 patients (25.0%) had documented endocrine activity. Analysis of the DNA methylation data showed substantial differences between TC and AC samples and revealed three distinct clusters (C1-C3): C3 (n = 29) with 100% TC and 89.7% non-metastasized, C2 (n = 22) with 63.6% AC and 95.5% metastasized, and C1 with three AC samples (2/3 metastasized). In subgroup analyses, distinct methylation patterns were observed based on histology, metastases, SSTR status, and endocrine activity. In the functional gene classification, the genes affected by differential methylation were mainly involved in cell signaling. DNA methylation could potentially aid in the diagnostic process of lung NET. The differences in methylation observed with respect to clinical features like SSTR expression and endocrine activity could translate into improved management of lung NET.
| S-EPMC11903570 | biostudies-literature
Project description:Pituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs are difficult to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories. In this study, we analyzed 118 PitNETs to develop a clinico-molecular approach to classifying PitNETs. Comparison of clinical, immunohistochemical, and DNA methylation showed that PitNETs can be classified into distinct clinical and molecular subgroups. Unsupervised DNA methylation separated PitNETs into two major clusters. The first major cluster was composed of tumors currently labeled as gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silence, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of Corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that correspond to clinically active and silent tumors and consistent with degree of differentiation. Analysis of promoter methylation patterns correlates with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes do not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation. These findings suggest that future classification of PitNETs may need to include DNA methylation for clinicopathological stratification.
2025-04-29 | GSE283928 | GEO
Project description:ObjectivesThe objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).MethodsEighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively.ResultsOne hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility.ConclusionsThe integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning.Trial registrationChiCTR2400091906.
| S-EPMC11743008 | biostudies-literature
Project description:BackgroundIncidence of locoregional neuroendocrine tumors (NETs) is rising. However, after curative resection, the patterns and risk factors associated with recurrence remain unknown. Consensus guidelines recommend surveillance every 6-12 months for up to 10 years after surgery for resected, well-differentiated NETs irrespective of patient demographics, site, grade or stage of tumor with few exceptions.Patients and methodsFrom the Surveillance, Epidemiology, and End Results (SEER)-Medicare database, we identified localized and regional stage NET patients who underwent surgical resection between January 2002 and December 2011. Development of recurrence was identified by capturing at least two claims indicative of metastatic disease until 31 December 2013.ResultsOf the 2366 identified patients (median age 73 years), 369 (16%) developed metastatic disease within 5 years and only an additional 1% developed metastases between years 5 and 10 with the majority dying due to unrelated causes. The 5-year risk of developing metastases (hazard ratio, HR) varied significantly (log-rank P < 0.001) by grade: 9.9% versus 25.9% (2.2) versus 48.1% (4.4) for grades 1, 2, and ≥ 3, respectively; stage: 10.3% versus 31.1% (2.8) for localized versus regional; primary tumor size: 7.6% versus 15% (1.3) versus 26.6% (1.5) for <1, 1-2, and > 2 cm, respectively; and site: ranging from 11.3% for colon to 23.9% for pancreas.ConclusionsContrary to current guidelines, our study suggests that surveillance recommendations should be tailored according to patient and tumor characteristics. Surveillance past 5 years may be avoided in elderly patients with competing morbidities or low risk of recurrence. Pancreatic, lung, higher grade, and regional NETs have a higher risk of recurrence and may be considered for future adjuvant trials.
| S-EPMC6246350 | biostudies-literature
Project description:This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast-enhanced MRI (DCE-MRI)-derived perfusion parameters. The clinical data and preoperative DCE-MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning-based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.
| S-EPMC9026802 | biostudies-literature
Project description: Not available
| S-EPMC10783998 | biostudies-literature