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PANEN列线图:为接受肽受体放射性核素治疗的转移性胰腺神经内分泌肿瘤患者提供临床决策支持。

The PANEN nomogram: clinical decision support for patients with metastatic pancreatic neuroendocrine neoplasm referred for peptide receptor radionuclide therapy.

作者信息

Singh Aviral, Sanduleanu Sebastian, Kulkarni Harshad R, Langbein Thomas, Lambin Philippe, Baum Richard P

机构信息

Theranostics (Oncology), GenesisCare Pty Ltd, Murdoch, WA, Australia.

Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.

出版信息

Front Endocrinol (Lausanne). 2025 Jun 24;16:1514792. doi: 10.3389/fendo.2025.1514792. eCollection 2025.

Abstract

INTRODUCTION

Patients with pancreatic neuroendocrine neoplasms (P-NEN) may benefit from peptide receptor radionuclide therapy (PRRT). Prediction of overall survival (OS) using statistical models has the potential to guide treatment decisions. In this study, we have generated a clinicopathological and imaging parameter-based internally validated nomogram of patients who received PRRT for metastatic P-NEN to facilitate treatment decision support for the clinical management of such patients.

PATIENTS AND METHODS

We reviewed 447 pancreatic NEN patients treated with PRRT. Clinical variables for the prediction of overall survival (OS) included age, gender, Karnofsky performance score (KPS), weight loss, hepatomegaly, time from diagnosis to first PRRT (days), tumor functionality, presence of Hedinger syndrome, presence of liver metastases, presence of bone metastases, presence of lung metastases, alkaline phosphatase, 2-deoxy-2-[18F]fluoro-D-glucose ([F]FDG) positron emission tomography (PET) scan positivity, erythrocytes, platelets, creatinine clearance, leucocytes, and histologic grade of tumor differentiation based on KI-67 staining. A random survival forests (RSF) method was used to construct a model with an optimal number of clinical variables. The model was developed on 80% of the data and tested on the remaining 20% of the data. Performance of prediction was calculated using the c-index, a generalization of the area under the ROC curve (AUC) for survival models.

RESULTS

Median follow up time was 2045 days (min 136 days, max 10329 days). Time from diagnosis to 1 PRRT, alkaline phosphatase, KPS, hepatomegaly, weight loss, [F]FDG-PET scan positivity, Ki-67% derived histologic grade, lung metastases, age, presence of bone metastases, platelet count, erythrocyte count, creatinine clearance, hemoglobin, presence of functioning tumor, creatinine, and gender, were in order of importance, all independent predictors for overall survival. The development set c-index was 0.86, while the test set c-index was 0.82. A nomogram was constructed based on the optimal number of clinical parameters selected in the RSF model.

CONCLUSION

This study proposes an internally validated nomogram (PANEN-N) to accurately predict overall survival for P-NEN patients following PRRT, which could be used for patient counseling to facilitate informed and shared decision support in daily clinical practice as well as for generating new hypotheses.

摘要

引言

胰腺神经内分泌肿瘤(P-NEN)患者可能从肽受体放射性核素治疗(PRRT)中获益。使用统计模型预测总生存期(OS)有可能指导治疗决策。在本研究中,我们基于临床病理和影像参数生成了一个内部验证的列线图,用于接受PRRT治疗的转移性P-NEN患者,以促进对此类患者临床管理的治疗决策支持。

患者与方法

我们回顾了447例接受PRRT治疗的胰腺神经内分泌肿瘤患者。预测总生存期(OS)的临床变量包括年龄、性别、卡氏功能状态评分(KPS)、体重减轻、肝肿大、从诊断到首次PRRT的时间(天)、肿瘤功能、是否存在黑丁格综合征、是否存在肝转移、是否存在骨转移、是否存在肺转移、碱性磷酸酶、2-脱氧-2-[18F]氟-D-葡萄糖([F]FDG)正电子发射断层扫描(PET)扫描阳性、红细胞、血小板、肌酐清除率、白细胞以及基于KI-67染色的肿瘤分化组织学分级。采用随机生存森林(RSF)方法构建一个包含最佳数量临床变量的模型。该模型在80%的数据上开发,并在其余20%的数据上进行测试。使用c指数计算预测性能,c指数是生存模型中ROC曲线下面积(AUC)的推广。

结果

中位随访时间为2045天(最小值136天,最大值10329天)。从诊断到首次PRRT的时间、碱性磷酸酶、KPS、肝肿大、体重减轻、[F]FDG-PET扫描阳性、Ki-67%衍生的组织学分级、肺转移、年龄、骨转移的存在、血小板计数、红细胞计数、肌酐清除率、血红蛋白、功能性肿瘤的存在、肌酐和性别,按重要性排序,均为总生存期的独立预测因素。开发集的c指数为0.86,而测试集的c指数为0.82。基于RSF模型中选择的最佳数量临床参数构建了列线图。

结论

本研究提出了一个内部验证的列线图(PANEN-N),以准确预测PRRT后P-NEN患者的总生存期,可用于患者咨询,以促进日常临床实践中的知情和共同决策支持,以及生成新的假设。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5825/12234284/39ea40b877b1/fendo-16-1514792-g001.jpg

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