  Intratumoral nanoparticles (NPs) distribution is critical for the success of
nanomedicine in imaging and treatment, but computational models to describe the
NPs distribution remain unavailable due to the complex tumor-nano interactions.
Here, we develop a Generative Adversarial Network for Distribution Analysis
(GANDA) to describe and conditionally generates the intratumoral quantum dots
(QDs) distribution after i.v. injection. This deep generative model is trained
automatically by 27 775 patches of tumor vessels and cell nuclei decomposed
from whole-slide images of 4T1 breast cancer sections. The GANDA model can
conditionally generate images of intratumoral QDs distribution under the
constraint of given tumor vessels and cell nuclei channels with the same
spatial resolution (pixels-to-pixels), minimal loss (mean squared error, MSE =
1.871) and excellent reliability (intraclass correlation, ICC = 0.94).
Quantitative analysis of QDs extravasation distance (ICC = 0.95) and subarea
distribution (ICC = 0.99) is allowed on the generated images without knowing
the real QDs distribution. We believe this deep generative model may provide
opportunities to investigate how influencing factors affect NPs distribution in
individual tumors and guide nanomedicine optimization for molecular imaging and
personalized treatment.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning