Chemotherapy response prediction with diffuser elapser network

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. Author summaryThe disorganized structure and leakiness of tumor vessels induce the inadequate blood supply and high fluid pressure within tumors. These features of the tumor microenvironment, identified as delivery barriers, lead to an insufficient amount of drugs to reach the interior parts of tumors. It is observed that the use of anti-vascular drugs makes the structure and function of the tumor vascular system more normal. Moreover, the combination of these drugs with cytotoxic agents provides favorable results with increased treatment response. But, it is also important to adjust the treatment schedule properly. In this regard, we build a deep learning model, designed to examine the tumor response with the ongoing treatment schedule. Our study suggests that deep learning models can be used to predict tumor growth and drug response in the scheduling of cytotoxic drugs.