Affiliation:
1. Department of Radiology The First Affiliated Hospital of Nanjing Medical University Nanjing Jiangsu 210000 China
2. Department of Mathematics University College London London WC1H 0AY UK
3. Department of General Surgery Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing 210000 China
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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Double First Class University Plan
Science and Technology Support Program of Jiangsu Province
Science and Engineering Research Council
Engineering and Physical Sciences Research Council