4.7 Article

Chemotherapy response prediction with diffuser elapser network

期刊

SCIENTIFIC REPORTS
卷 12, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41598-022-05460-z

关键词

-

资金

  1. TUBITAK [117F047]
  2. Republic of Turkey Ministry of Development [2009K120520]
  3. Council of Higher Education
  4. TUBITAK 2211/A National Ph.D. Scholarship Program

向作者/读者索取更多资源

Angiogenesis plays a crucial role in the inefficiency of drug delivery in solid tumors. Antiangiogenic treatment and the combination of antiangiogenic agents and chemotherapy drugs have shown promising effects in enhancing drug delivery. However, finding the optimal scheduling and dosages for the combination therapy remains challenging. This study proposes a deep learning model to predict tumor microenvironment maps and evaluate the effects of ongoing treatment, providing insights for determining the optimal starting time and dosages of cytotoxic drug treatment.
In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof of concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The model predicts tumor microenvironment maps seven days ahead with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of 2.292 +/- 1.820.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据