  Intratumoral nanoparticles (NPs) distribution is critical for the diagnostic
and therapeutic effect, but methods to predict the distribution remain
unavailable due to the complex bio-nano interactions. Here, we developed a
Generative Adversarial Network for Distribution Analysis (GANDA) to make
pixels-to-pixels prediction of the NPs distribution across tumors. This
predictive model used deep learning approaches to automatically learn the
features of tumor vessels and cell nuclei from whole-slide images of tumor
sections. We showed that the GANDA could generate images of NPs distribution
with the same spatial resolution as original images of tumor vessels and
nuclei. The GANDA enabled quantitative analysis of NPs distribution (R2=0.93)
and extravasation without knowing their real distribution. This model provides
opportunities to investigate how influencing factors affect NPs distribution in
individual tumors and may guide nanomedicine optimization for personalized
treatments.
