GANDA: A deep generative adversarial network conditionally generates intratumoral nanoparticles distribution pixels-to-pixels

Tang, Yuxia; Zhang, Jiulou; He, Doudou; Miao, Wenfang; Liu, Wei; Li, Yang; Lu, Guangming; Wu, Fann; Wang, Shouju

doi:10.1016/j.jconrel.2021.06.039

Cited by 18 publications

(17 citation statements)

References 28 publications

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“…Maybe, AI could be the solution to these bottleneck problems. For example, Tang et.al developed a AI model to analyse the distribution of intratumoral nanoparticles to optimize the cancer treatment [ 41 ]. It's worth noting that antioxidation therapy is an important mechanism of nanomaterials in treating with cancer [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy

Chen

Mao

et al. 2022

J Transl Med

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Background The tumor microenvironment (TME) plays an important role in the occurrence and development of gastric cancer (GC) and is widely used to assess the treatment outcomes of GC patients. Immunohistochemistry (IHC) and gene sequencing are the main analysis methods for the TME but are limited due to the subjectivity of observers, the high cost of equipment and the need for professional analysts. Methods The ImmunoScore (IS) was developed in the TCGA cohort and validated in GEO cohorts. The Radiomic ImmunoScore (RIS) was developed in the TCGA cohort and validated in the Nanfang cohort. A nomogram was developed and validated in the Nanfang cohort based on RIS and clinical features. Results For IS, the area under the curves (AUCs) were 0.798 for 2-year overall survival (OS) and 0.873 for 4-year overall survival. For RIS, in the TCGA cohort, the AUCs distinguishing High-IS or Low-IS and predicting prognosis were 0.85 and 0.81, respectively; in the Nanfang cohort, the AUC predicting prognosis was 0.72. The nomogram performed better than the TNM staging system according to the ROC curve (all P < 0.01). Patients with TNM stage II and III in the High-nomogram group were more likely to benefit from adjuvant chemotherapy than Low-nomogram group patients. Conclusions The RIS and the nomogram can be used to assess the TME, prognosis and adjuvant chemotherapy benefit of GC patients after radical gastrectomy and are valuable additions to the current TNM staging system. High-nomogram GC patients may benefit more from adjuvant chemotherapy than Low-nomogram GC patients.

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Section: Discussionmentioning

confidence: 99%

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy

Chen

Mao

et al. 2022

J Transl Med

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“…277,278 A recent study used a GAN model to map the intratumoral distribution of QDs after intravenous injection (Figure 11E). 279 These deep learning models are increasingly capable of deciphering critical phenomena from biological images and show great promise in providing scientific insights. In summary, end-to-end deep learning models can learn representations from original input data in an automated way, avoiding the complicated feature engineering process.…”

Section: Nanodescriptors From Periodic Table Properties Liquid Drop M...mentioning

confidence: 99%

“…Moreover, CNN was useful for detecting malformations in Daphnia magna exposed to SNPs and TiO 2 NPs from thousands of microscopic images (Figure D) . Another class of end-to-end deep learning frameworks widely used for biological images analysis is the GAN. , A recent study used a GAN model to map the intratumoral distribution of QDs after intravenous injection (Figure E) . These deep learning models are increasingly capable of deciphering critical phenomena from biological images and show great promise in providing scientific insights.…”

Section: Unraveling Quantitative Nanostructure–toxicity Relationships...mentioning

confidence: 99%

Section: Unraveling Quantitative Nanostructure–toxicity Relationships...mentioning

confidence: 99%

“…Various ML models have been developed for the in vivo behavior of NMs in mice or rats. These include modeling of reproductive and pulmonary toxicity, ,, genotoxicity, tissue-specific oxidative stress, metabolic pathways, delivery efficiency, and in vivo fate. ,,, For example, an RF model was developed to identify the most important factors determining reproductive toxicity of NPs, determined by the sperm count, percentage of motile sperm, and sperm abnormalities in rats . Feature importance analysis revealed that the reproductive toxicity was strongly related to the NP type and toxicity end points used.…”

Section: Unraveling Quantitative Nanostructure–toxicity Relationships...mentioning

confidence: 99%

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Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

et al. 2023

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Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)–toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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A High‐Resolution Prediction Network for Predicting Intratumoral Distribution of Nanoprobes by Tumor Vascular and Nuclear Feature

Xu,

Luo,

Wang

et al. 2023

Advanced Intelligent Systems

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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.

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GANDA: A deep generative adversarial network conditionally generates intratumoral nanoparticles distribution pixels-to-pixels

Cited by 18 publications

References 28 publications

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

A High‐Resolution Prediction Network for Predicting Intratumoral Distribution of Nanoprobes by Tumor Vascular and Nuclear Feature

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