Luo Yuxi, Cheng Kai, Liu Jingru, Pei Jinli, Xu Shengnan, Zhao Xinzhi, Wang Shijie, Zhao Kunlong, Li Wanhu, Liu Jie, Yu Jinming
Department of Oncology, The Affiliated Hospital of Southwest Medical University, College of Clinical Medicine, Southwest Medical University, Luzhou, China.
Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Korean J Radiol. 2025 Jun;26(6):593-603. doi: 10.3348/kjr.2024.1266.
Molecular subtyping of small-cell lung cancer (SCLC) has major implications for prognostic relevance and treatment guidance. This study aimed to explore the feasibility of a novel tracer targeting C-X-C-chemokine-receptor-type-4 (CXCR4) for distinguishing different SCLC subtypes.
Thirty-five patients with pathologically confirmed SCLC were enrolled in this prospective study. Immunohistochemical staining was performed to classify the molecular subtypes into SCLC-A, SCLC-N, SCLC-P, and SCLC-I. [¹⁸F]AlF-NOTA-QHY-04 PET/CT parameters were obtained, including the maximum, mean, and peak standard uptake values (SUV, SUV, and SUV, respectively) and the ratios of tumors (T) and normal tissues (NT) based on the SUV (T/NT). These parameters were compared among the molecular subtypes. A receiver operating characteristic (ROC) curve was used to analyze the performance of the parameters for distinguishing SCLC-N from other subtypes and neuroendocrine (NE) subtypes (SCLC-A and SCLC-N) from non-NE subtypes (SCLC-P and SCLC-I).
The molecular subtypes were SCLC-A (n = 17), SCLC-N (n = 6), SCLC-P (n = 7), and SCLC-I (n = 5). The SCLC-N subtype exhibited significantly higher uptake in both primary tumors and lymph node metastases than the other three subtypes ( < 0.05). When SCLC-N was compared with the other three subtypes combined (referred to as "other SCLCs"), all parameters were significantly higher in the SCLC-N group ( < 0.05). ROC analysis showed that these parameters had high accuracy in distinguishing SCLC-N from other SCLCs (area under ROC curve: 0.868-0.948 for primary tumors and 0.783-0.888 for lymph node metastases). Compared with the non-NE group, the SUV, SUV, and T/NT were significantly higher in the NE group for primary tumors. ROC analysis showed moderate accuracy in distinguishing between the NE and non-NE groups (ROC area: 0.692-0.786 for primary tumors and 0.692-0.815 for lymph node metastases).
Our preliminary findings indicate that CXCR4-directed PET/CT imaging using [¹⁸F]AlF-NOTA-QHY-04 may differentiate between SCLC-N and other molecular subtypes and between NE and non-NE subtypes of SCLC.
小细胞肺癌（SCLC）的分子亚型对预后相关性和治疗指导具有重要意义。本研究旨在探索一种靶向C-X-C趋化因子受体4型（CXCR4）的新型示踪剂用于区分不同SCLC亚型的可行性。
35例经病理确诊的SCLC患者纳入本前瞻性研究。进行免疫组织化学染色以将分子亚型分为SCLC-A、SCLC-N、SCLC-P和SCLC-I。获取[¹⁸F]AlF-NOTA-QHY-04 PET/CT参数，包括最大、平均和峰值标准摄取值（分别为SUVmax、SUVmean和SUVpeak）以及基于SUV的肿瘤（T）与正常组织（NT）的比值（T/NT）。比较这些参数在分子亚型之间的差异。采用受试者操作特征（ROC）曲线分析这些参数区分SCLC-N与其他亚型以及神经内分泌（NE）亚型（SCLC-A和SCLC-N）与非NE亚型（SCLC-P和SCLC-I）的性能。
分子亚型为SCLC-A（n = 17）、SCLC-N（n = 6）、SCLC-P（n = 7）和SCLC-I（n = 5）。SCLC-N亚型在原发肿瘤和淋巴结转移灶中的摄取均显著高于其他三种亚型（P < 0.05）。当将SCLC-N与其他三种亚型合并（称为“其他SCLC”）进行比较时，SCLC-N组的所有参数均显著更高（P < 0.05）。ROC分析表明，这些参数在区分SCLC-N与其他SCLC方面具有较高准确性（原发肿瘤的ROC曲线下面积：0.868 - 0.948，淋巴结转移灶的ROC曲线下面积：0.783 - 0.888）。与非NE组相比，NE组原发肿瘤的SUVmax、SUVmean和T/NT显著更高。ROC分析表明，在区分NE组与非NE组方面具有中等准确性（原发肿瘤的ROC面积：0.692 - 0.786，淋巴结转移灶的ROC面积：0.692 - 0.815）。
我们的初步研究结果表明，使用[¹⁸F]AlF-NOTA-QHY-04进行CXCR4导向的PET/CT成像可能区分SCLC-N与其他分子亚型以及SCLC的NE与非NE亚型。
