Assessing cancer response to therapeutic interventions has been realized as an important course to early predict curative efficacy and treatment outcomes due to tumor heterogeneity. Compared to the traditional invasive tissue biopsy method, molecular imaging techniques have fundamentally revolutionized the ability to evaluate cancer response in a spatiotemporal manner. The past few years has witnessed a paradigm shift on the efforts from manufacturing functional molecular imaging probes for seeing a tumor to a vantage stage of interpreting the tumor response during different treatments. This review is to stand by the current development of advanced imaging technologies aiming to predict the treatment response in cancer therapy. Special interest is placed on the systems that are able to provide rapid and noninvasive assessment of pharmacokinetic drug fates (e.g., drug distribution, release, and activation) and tumor microenvironment heterogeneity (e.g., tumor cells, macrophages, dendritic cells (DCs), T cells, and inflammatory cells). The current status, practical significance, and future challenges of the emerging artificial intelligence (AI) technology and machine learning in the applications of medical imaging fields is overviewed. Ultimately, the authors hope that this review is timely to spur research interest in molecular imaging and precision medicine.

中文翻译：

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超越视觉的成像：癌症治疗的早期预后

由于肿瘤异质性，评估癌症对治疗干预的反应已被认为是早期预测疗效和治疗结果的重要过程。与传统的侵入性组织活检方法相比，分子成像技术从根本上改变了以时空方式评估癌症反应的能力。过去几年见证了从制造用于观察肿瘤的功能性分子成像探针到解释不同治疗期间的肿瘤反应的有利阶段的努力范式转变。这篇综述是为了支持目前旨在预测癌症治疗中治疗反应的先进成像技术的发展。特别关注能够对药代动力学药物命运（例如，药物分布、释放和激活）和肿瘤微环境异质性（例如，肿瘤细胞、巨噬细胞、树突状细胞 (DC)、T细胞和炎症细胞）。概述了新兴人工智能（AI）技术和机器学习在医学影像领域应用中的现状、现实意义和未来挑战。最后，作者希望这篇综述能及时激发对分子成像和精准医学的研究兴趣。概述了新兴人工智能（AI）技术和机器学习在医学影像领域应用中的现实意义和未来挑战。最后，作者希望这篇综述能及时激发对分子成像和精准医学的研究兴趣。概述了新兴人工智能（AI）技术和机器学习在医学影像领域应用中的现实意义和未来挑战。最后，作者希望这篇综述能及时激发对分子成像和精准医学的研究兴趣。