Xu, Jiaqi;
Luo, Yafei;
Wang, Chuanbing;
Chen, Haiyan;
Tang, Yuxia;
Xu, Ziqing;
Li, Yang;
... Wang, Shouju; + view all
(2023)
A High‐Resolution Prediction Network for Predicting Intratumoral Distribution of Nanoprobes by Tumor Vascular and Nuclear Feature.
Advanced Intelligent Systems
10.1002/aisy.202300592.
(In press).
Preview |
PDF
A High Resolution Prediction Network for Predicting Intratumoral Distribution.pdf - Published Version Download (5MB) | Preview |
Abstract
In this study, the critical need for precise and accurate prediction of intra‐tumor heterogeneity related to the enhanced permeability and retention effect and spatial distribution of nanoprobes is addressed for the development of effective nanodrug delivery strategies. Current predictive models are limited in terms of resolution and accuracy, prompting the construction of a high‐resolution prediction network (HRPN) that estimates the microdistribution of quantum dots, factoring in tumor vascular and nuclear features. The HRPN algorithm is trained using 27 780 patches and validated on 4920 patches derived from 4T1 breast cancer whole‐slide images, demonstrating its reliability. The HRPN model exhibits minimal error (mean square error = 1.434, root mean square error = 1.198), satisfactory goodness of fit (R2 = 0.891), and superior image quality (peak signal‐to‐noise ratio = 44.548) when compared to a generative‐adversarial‐network‐structured model. Furthermore, the HRPN model offers improved prediction accuracy, broader prediction intervals, and reduced computational resource requirements. Consequently, the proposed model yields high‐resolution predictions that more closely resemble actual tumor microdistributions, potentially serving as a powerful analytical tool for investigating the spatial relationship between the tumor microenvironment and nanoprobes.
| Type: | Article |
|---|---|
| Title: | A High‐Resolution Prediction Network for Predicting Intratumoral Distribution of Nanoprobes by Tumor Vascular and Nuclear Feature |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/aisy.202300592 |
| Publisher version: | https://doi.org/10.1002/aisy.202300592 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Artificial intelligence, deep learning, high-resolution networks, intratumoral distributions, nanoparticles |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10183028 |
Archive Staff Only
 |
View Item |
