Molecular and functional imaging in cancer-targeted therapy: current applications and future directions

分子成像 靶向治疗 医学 磁共振成像 癌症治疗 实体瘤疗效评价标准 癌症 模式 正电子发射断层摄影术 医学物理学 病理 临床试验 放射科 内科学 生物 社会科学 社会学 生物技术 体内 临床研究阶段
作者
Junwei Bai,Siqi Qiu,Guojun Zhang
出处
期刊:Signal Transduction and Targeted Therapy [Springer Nature]
卷期号:8 (1) 被引量:21
标识
DOI:10.1038/s41392-023-01366-y
摘要

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.
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