Molecular imaging of tumour-associated pathological biomarkers with smart nanoprobe: From “Seeing” to “Measuring”

This review summarizes the recent achievements in the designs of the smart quantitative molecular imaging nanoprobes, and highlights the principle and state of the art in the quantitative visualization of tumour-associated pathological molecules. The future prospects of nanoprobe-based quantitative molecular imaging of cancer and the challenges facing its clinical translation are also discussed.


Although the extraordinary progress has been made in molecular biology, the prevention of cancer remains arduous. Most solid tumours exhibit both spatial and temporal heterogeneity, which is difficult to be mimicked in vitro. Additionally, the complex biochemical and immune features of tumour microenvironment significantly affect the tumour development. Molecular imaging aims at the exploitation of tumour-associated molecules as specific targets of customized molecular probe, thereby generating image contrast of tumour markers, and offering opportunities to non-invasively evaluate the pathological characteristics of tumours in vivo. Particularly, there are no “standard markers” as control in clinical imaging diagnosis of individuals, so the tumour pathological characteristics-responsive nanoprobe-based quantitative molecular imaging, which is able to visualize and determine the accurate content values of heterogeneous distribution of pathological molecules in solid tumours, can provide criteria for cancer diagnosis. In this context, a variety of “smart” quantitative molecular imaging nanoprobes have been designed, in order to provide feasible approaches to quantitatively visualize the tumour-associated pathological molecules in vivo. This review summarizes the recent achievements in the designs of these nanoprobes, and highlights the state-of-the-art technologies in quantitative imaging of tumour-associated pathological molecules.

Author list:

Peisen Zhang, Wenyue Li, Chuang Liu, Feng Qin, Yijie Lu*, Meng Qin*, Yi Hou*

How to cite:

P. Zhang, W. Li, C. Liu, F. Qin, Y. Lu, M. Qin, Y. Hou, Exploration 2023, 3, 20230070.