Impact of Combined Chemotherapy and Targeted Therapy on Pancreatic Neuroendocrine Carcinoma with Liver Metastasis: A Single-Center Modified Nomogram Analysis.

IF 2.7 4区 医学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY OncoTargets and therapy Pub Date : 2024-06-22 eCollection Date: 2024-01-01 DOI:10.2147/OTT.S466213
Wenhao Luo, Hao Chen, Taiping Zhang
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引用次数: 0

Abstract

Objective: To establish a modified nomogram model for pancreatic neuroendocrine carcinoma (pNEC) patients with liver metastasis via single-center clinical data, and to provide guidelines for improving the diagnosis and treatment of patients.

Methods: A retrospective analysis of clinical data from pNEC patients with liver metastasis at Peking Union Medical College Hospital (January 2000 to November 2023) was conducted. Univariate and multivariate Cox regression analyses were employed to identify prognostic factors for overall survival (OS). Kaplan-Meier curves were generated, and a modified nomogram predictive model was developed to illustrate the prognosis of pNEC patients with liver metastasis. Calibration plots and C-index were used to validate the model's feasibility, accuracy, and reliability.

Results: Forty-five participants with the rare cancer type pNEC and liver metastasis were included in the study. Kaplan-Meier curves revealed that primary tumor resection (PTR), chemotherapy or targeted therapy, and tumor size equal to or less than 5cm significantly improved OS compared to those without PTR, chemotherapy or targeted therapy, and tumor size larger than 5cm. Multivariate Cox regression analysis identified PTR, a combination of chemotherapy and targeted therapy, and tumor size as independent prognostic factors for OS. The predictive nomogram model exhibited acceptable performance with a C-index of 0.744 (0.639-0.805) through bootstrapping.

Conclusion: Combining chemotherapy with targeted therapy enhances the survival of pNEC patients with liver metastasis. The modified nomogram model and predictive score table offer valuable references and insights for both clinicians and patients.

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联合化疗和靶向治疗对肝转移胰腺神经内分泌癌的影响:单中心改良提名图分析
目的通过单中心临床数据建立胰腺神经内分泌癌(pNEC)肝转移患者的改良提名图模型,为改善患者的诊断和治疗提供指导:方法:对北京协和医院(2000 年 1 月至 2023 年 11 月)肝转移 pNEC 患者的临床数据进行回顾性分析。采用单变量和多变量 Cox 回归分析确定总生存期(OS)的预后因素。生成了 Kaplan-Meier 曲线,并建立了改良的提名图预测模型,以说明有肝转移的 pNEC 患者的预后情况。校准图和C指数用于验证模型的可行性、准确性和可靠性:研究共纳入了 45 名患有罕见癌症类型 pNEC 并有肝转移的患者。Kaplan-Meier曲线显示,与未进行原发肿瘤切除术(PTR)、化疗或靶向治疗以及肿瘤大小等于或小于5厘米的患者相比,肿瘤大小大于或等于5厘米的患者的OS明显改善。多变量考克斯回归分析发现,PTR、化疗和靶向治疗的组合以及肿瘤大小是OS的独立预后因素。通过引导分析,预测提名图模型表现出可接受的性能,C指数为0.744(0.639-0.805):结论:化疗与靶向治疗相结合可提高肝转移 pNEC 患者的生存率。修改后的提名图模型和预测评分表为临床医生和患者提供了有价值的参考和启示。
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来源期刊
OncoTargets and therapy
OncoTargets and therapy BIOTECHNOLOGY & APPLIED MICROBIOLOGY-ONCOLOGY
CiteScore
9.70
自引率
0.00%
发文量
221
审稿时长
1 months
期刊介绍: OncoTargets and Therapy is an international, peer-reviewed journal focusing on molecular aspects of cancer research, that is, the molecular diagnosis of and targeted molecular or precision therapy for all types of cancer. The journal is characterized by the rapid reporting of high-quality original research, basic science, reviews and evaluations, expert opinion and commentary that shed novel insight on a cancer or cancer subtype. Specific topics covered by the journal include: -Novel therapeutic targets and innovative agents -Novel therapeutic regimens for improved benefit and/or decreased side effects -Early stage clinical trials Further considerations when submitting to OncoTargets and Therapy: -Studies containing in vivo animal model data will be considered favorably. -Tissue microarray analyses will not be considered except in cases where they are supported by comprehensive biological studies involving multiple cell lines. -Biomarker association studies will be considered only when validated by comprehensive in vitro data and analysis of human tissue samples. -Studies utilizing publicly available data (e.g. GWAS/TCGA/GEO etc.) should add to the body of knowledge about a specific disease or relevant phenotype and must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Bioinformatics studies must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Single nucleotide polymorphism (SNP) studies will not be considered.
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