A multilayer network-enabled ultrasonic image series analysis approach for online cancer drug delivery monitoring. (January 2022) - Li, Yuxuan; VanOsdol, Joshua; Ranjan, Ashish; Liu, Chenang
Highlights: A multilayer network-enabled image-guided drug delivery (MNE-IGDD) monitoring approach using ultrasonic images is developed to monitor the progress of cancer drug delivery after treatment. In this method, a multilayer network representation approach with a proposed spatial-regularized similarity metric is developed to represent consecutive ultrasonic images from a network perspective. By incorporating a multilayer network community detection approach for ultrasonic image feature extraction, the drug delivery progress could be accurately identified and monitored. Both simulation and real-world case studies demonstrate that the proposed MNE-IGDD method is much more effective for cancer drug delivery monitoring than the benchmark methods. Abstract: The objective of this study is to develop an effective data-driven methodology for the online monitoring of cancer drug delivery guided by the ultrasonic images. To achieve this goal, effective image quantification and accurate feature extraction play a critical role on image-guided drug delivery (IGDD) monitoring. However, the existing image-guided approaches in such area are mainly focused on the analysis for individual images rather than the image series. In fact, the temporal patterns between consecutive images may contain critical information and it is necessary to be considered in the monitoring analysis. In addition, the conventional approaches, such as the pure intensity-based method, also do not sufficiently consider the effects of noise in the ultrasonic images, which also limits the monitoring sensitivity and accuracy. To address the challenges, this paper proposed a novel multilayer network-enabled IGDD (MNE-IGDD) monitoring approach. The contributions of the proposed method can be summarized into three aspects: (1) formulate the sequential ultrasound images to a multilayer network by the proposed spatial-regularized distance; (2) detect drug delivery area based on community detection algorithm of multilayer network; and (3) quantify the drug delivery progress by incorporating the image intensity-based features with the detected community. Both the detected communities and feature increment percentages are applied as the evaluation metric for validation. A simulation study was conducted and this method was also applied to a real-world mouse colon tumor treatment case study under three temperature conditions. Both simulation and the real-world case studies demonstrated that the proposed method is promising to achieve satisfactory monitoring performance in clinical trials.