论文标题
甘达：深层生成的对抗网络预测肿瘤像素中纳米颗粒的空间分布
GANDA: A deep generative adversarial network predicts the spatial distribution of nanoparticles in tumor pixelly
论文作者
论文摘要
肿瘤内纳米颗粒（NP）分布对于纳米医学在成像和处理中的成功至关重要，但是由于复杂的肿瘤纳米相互作用，描述NPS分布的计算模型仍然无法使用。在这里，我们开发了一个生成的对抗网络，用于分布分析（GANDA），以描述并有条件地生成静脉内肿瘤内量子点（QDS）分布。注射。这种深层生成模型由277775个肿瘤血管和细胞核自动训练，从4T1乳腺癌切片的全裂片图像分解。 GANDA模型可以在给定的肿瘤血管和细胞核通道的约束下有条件地生成具有相同空间分辨率（像素到像素），最小损失（平均平均误差，MSE = 1.871）和出色的可靠性（Intac Correation，Intac Correation，ICC = 0.94）的肿瘤血管和细胞核通道的图像。在生成的图像上允许对QDS外出距离（ICC = 0.95）和亚区分布分布（ICC = 0.99）进行定量分析，而不知道实际QDS分布。我们认为，这种深层生成模型可能会提供机会来研究影响因素如何影响单个肿瘤中NPS分布，并指导纳米医学对分子成像和个性化治疗的优化。
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.
