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Development and Validation of a Prognostic Nomogram to Guide Decision-Making for High-Grade Digestive Neuroendocrine Neoplasms.

ABSTRACT: BACKGROUND:The objective of this study was to develop and validate a nomogram to predict 1-year overall survival (OS) and 2-year OS in patients with high-grade digestive neuroendocrine neoplasms (NENs) as well as to guide selection of subgroups that could benefit from systemic chemotherapy. SUBJECTS, MATERIALS, AND METHODS:We performed a retrospective analysis of 223 patients with NENs of the gut and hepato-biliary-pancreatic system from four centers included in the development cohort. The nomogram was externally validated in a cohort of 90 patients from another one. RESULTS:The final model included lactate dehydrogenase, performance status, stage, Ki67, and site of primary tumor, all of which had a significant effect on OS. The uncorrected C-index was 0.761 for OS, and the bias-corrected C-index was 0.744. Predictions correlated well with observed 1-year and 2-year outcomes (judged by eye). The area under the time-dependent receiver operating characteristic curve at 12 months and 24 months was 0.876 and 0.838, respectively. The nomogram performed well in terms of both discrimination and calibration when applied to the validation cohort, and OS was significantly different between the two groups classified by nomogram score (log-rank p < .001). CONCLUSION:The validated nomogram provided useful prediction of OS, which can be offered for clinicians to improve their abilities to assess patient prognosis, to create clinical risk groups for informing treatment or for patient stratification by disease severity in clinical trials. IMPLICATIONS FOR PRACTICE:The high-grade neuroendocrine neoplasms of the digestive system are rare malignancies with great heterogeneity. An overall survival nomogram was developed and externally validated in this study. Two subgroups were classified by the nomogram score, and platinum-based chemotherapy may not bring clinical benefit for the low-risk patients.

SUBMITTER: Lin Z

PROVIDER: S-EPMC7160419 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Development and Validation of a Prognostic Nomogram to Guide Decision-Making for High-Grade Digestive Neuroendocrine Neoplasms.

Lin Zhenyu Z Wang Haihong H Zhang Yixuan Y Li Guiling G Pi Guoliang G Yu Xianjun X Chen Yaobing Y Jin Kaizhou K Chen Liangkai L Yang Shengli S Zhu Ying Y Wu Gang G Chen Jie J Zhang Tao T

The oncologist 20191129 4

<h4>Background</h4>The objective of this study was to develop and validate a nomogram to predict 1-year overall survival (OS) and 2-year OS in patients with high-grade digestive neuroendocrine neoplasms (NENs) as well as to guide selection of subgroups that could benefit from systemic chemotherapy.<h4>Subjects, materials, and methods</h4>We performed a retrospective analysis of 223 patients with NENs of the gut and hepato-biliary-pancreatic system from four centers included in the development co ...[more]

PMID: 32297441

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Project description:Objectives To develop and validate a contrast-enhanced CT-based radiomics nomogram for the diagnosis of neuroendocrine carcinoma of the digestive system. Methods The clinical data and contrast-enhanced CT images of 60 patients with pathologically confirmed neuroendocrine carcinoma of the digestive system and 60 patients with non-neuroendocrine carcinoma of the digestive system were retrospectively collected from August 2015 to December 2021 at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and randomly divided into a training cohort (n=84) and a validation cohort (n=36). Clinical characteristics were analyzed by logistic regression and a clinical diagnosis model was developed. Radiomics signature were established by extracting radiomic features from contrast-enhanced CT images. Based on the radiomic signature and clinical characteristics, radiomic nomogram was developed. ROC curves and Delong’s test were used to evaluate the diagnostic efficacy of the three models, calibration curves and application decision curves were used to analyze the accuracy and clinical application value of nomogram. Results Logistic regression results showed that TNM stage (stage IV) (OR 6.8, 95% CI 1.320-43.164, p=0. 028) was an independent factor affecting the diagnosis for NECs of the digestive system, and a clinical model was constructed based on TNM stage (stage IV). The AUCs of the clinical model, radiomics signature, and radiomics nomogram for the diagnosis of NECs of the digestive system in the training, validation cohorts and pooled patients were 0.643, 0.893, 0.913; 0.722, 0.867, 0.932 and 0.667, 0.887, 0.917 respectively. The AUCs of radiomics signature and radiomics nomogram were higher than clinical model, with statistically significant difference (Z=4.46, 6.85, both p < 0.001); the AUC difference between radiomics signature and radiomics nomogram was not statistically significant (Z=1.63, p = 0.104). The results of the calibration curve showed favorable agreement between the predicted values of the nomogram and the pathological results, and the decision curve analysis indicated that the nomogram had favorable application in clinical practice. Conclusions The nomogram constructed based on contrast-enhanced CT radiomics and clinical characteristics was able to effectively diagnose neuroendocrine carcinoma of the digestive system.

Project description:High-grade Gastroenteropancreatic Neuroendocrine neoplasms (H-NENs) comprehend well-differentiated tumors (NET G3) and poorly differentiated carcinomas (NEC) with proliferative activity indexes as mitotic count (MC) >20 mitoses/10 HPF and Ki-67 >20%. At present, no specific therapy for H-NENs exists and the several evidences of microenvironment involvement in their pathogenesis pave the way for tailored therapies. Forty-five consecutive cases, with available information about T-cell, immune, and non-immune markers, from surgical pathology and clinical databases of 2 Italian institutions were immunostained for Arginase, CD33, CD163 and CD66 myeloid markers. The association between features was assessed by Spearman's correlation coefficient. A unsupervised K-means algorithm was used to identify clusters of patients according to inputs of microenvironment features and the relationship between clusters and clinicopathological features, including cancer-specific survival (CSS), was analyzed. The H-NEN population was composed of 6 (13.3%) NET G3 and 39 (86.7%) NEC. Overall, significant positive associations were found between myeloid (CD33, CD163 and Arginase) and T/immune markers (CD3, CD4, CD8, PD-1 and HLA-I). Myeloid and T-cell markers CD3 and CD8 identified two clusters of patients from unsupervised K-means analysis. Cases grouped in cluster 1 with more myeloid infiltrates, T cell, HLA and expression of inhibitory receptors and ligands in the stroma (PD-1, PD-L1) had significantly better CSS than patients in cluster 2. Multivariable analysis showed that Ki-67 (>55 vs. <55, HR 8.60, CI 95% 2.61-28.33, p < 0.0001) and cluster (1 vs. 2, HR 0.43, CI 95% 0.20-0.93, p = 0.03) were significantly associated with survival. High grade gastroenteropancreatic neuroendocrine neoplasms can be further classified into two prognostic sub-populations of tumors driven by different tumor microenvironments and immune features able to generate the framework for evaluating new therapeutic strategies.

Dataset Information

Development and Validation of a Prognostic Nomogram to Guide Decision-Making for High-Grade Digestive Neuroendocrine Neoplasms.

Publications

Development and Validation of a Prognostic Nomogram to Guide Decision-Making for High-Grade Digestive Neuroendocrine Neoplasms.

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