文章：

机器学习揭示神经内分泌肿瘤中肽受体放射性核素治疗资格的新预测因子

Machine Learning Uncovers Novel Predictors of Peptide Receptor Radionuclide Therapy Eligibility in Neuroendocrine Neoplasms

原文发布日期：8 September 2025

DOI: 10.3390/cancers17172935

类型: Article

开放获取: 是

英文摘要：

Background:Neuroendocrine neoplasms (NENs) are a diverse group of malignancies in which somatostatin receptor expression can be crucial in guiding therapy. We aimed to evaluate the effectiveness of [99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT in differentiating neuroendocrine tumor histology, selecting candidates for radioligand therapy, and identifying correlations between somatostatin receptor expression and non-imaging parameters in metastatic NENs.Methods:This retrospective study included 65 patients (29 women, 36 men, mean age 61) with metastatic neuroendocrine neoplasms confirmed by histology, follow-up, or imaging, comprising 14 poorly differentiated carcinomas and 51 well-differentiated tumors. Somatostatin receptor SPECT/CT results were assessed visually and semiquantitatively, with mathematical models incorporating histological, oncological, immunohistochemical, and laboratory parameters, followed by biostatistical analysis.Results:Of 392 lesions evaluated, the majority were metastases in the liver, lymph nodes, and bones. Mathematical models estimated somatostatin receptor expression accurately (70–83%) based on clinical parameters alone. Key factors included tumor origin, oncological treatments, and the immunohistochemical marker CK7. Associations were found between age, grade, disease extent, and markers (CEA, CA19-9, AFP).Conclusions:Our findings suggest that [99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT effectively evaluates somatostatin receptor expression in NENs. Certain immunohistochemical and laboratory parameters, beyond recognized factors, show potential prognostic value, supporting individualized treatment strategies.

摘要翻译： 

背景：神经内分泌肿瘤是一组异质性恶性肿瘤，其中生长抑素受体表达对指导治疗至关重要。本研究旨在评估[99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT在鉴别神经内分泌肿瘤组织学类型、筛选放射性配体治疗候选者，以及探索转移性神经内分泌肿瘤中生长抑素受体表达与非影像学参数相关性的应用价值。 方法：这项回顾性研究纳入65例经组织学、随访或影像学确诊的转移性神经内分泌肿瘤患者（女性29例，男性36例，平均年龄61岁），包括14例低分化癌和51例高分化肿瘤。通过视觉评估和半定量分析评估生长抑素受体SPECT/CT结果，建立整合组织学、肿瘤学、免疫组化和实验室参数的数学模型，并进行生物统计学分析。 结果：在评估的392个病灶中，多数为肝脏、淋巴结和骨转移灶。数学模型仅基于临床参数即可准确（70-83%）预测生长抑素受体表达。关键影响因素包括肿瘤起源、肿瘤学治疗和免疫组化标志物CK7。研究发现年龄、分级、疾病范围与标志物（CEA、CA19-9、AFP）之间存在关联。 结论：研究结果表明[99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT能有效评估神经内分泌肿瘤的生长抑素受体表达。除已知因素外，某些免疫组化和实验室参数显示出潜在的预后价值，为个体化治疗策略提供了依据。

原文链接：

Machine Learning Uncovers Novel Predictors of Peptide Receptor Radionuclide Therapy Eligibility in Neuroendocrine Neoplasms