One of the principal signs of disease progression in brain tumor patients is an increase in tumor size between time-separated medical image acquisitions. The current diagnosis of tumor progression is based on visual appraisal or manual measurement of largest diameters, neither of which is a fully quantitative measure. The RANO criteria dictate that more than 25% growth in the product of two largest diameters indicates tumor progression. A more accurate assessment can be done if the tumor is fully segmented and a measurement of tumor volume is obtained. Tumor volume measurements have been shown to reduce variation due to inter- and intra-rater variability, patient position in the scanner and subjective determination of the largest diameter. Both tumor segmentation and size computation must currently be done manually and can be very time-consuming. A number of automated algorithms have been developed for lesion segmentation, but none have yet made it into clinical practice. This project focuses on the gap in currently available tools, which is the lack of volumetric analysis, particularly as it pertains to longitudinal data. We propose a tool that offers intuitive, flexible, and easy to evaluate quantitative analysis of uploaded tumor segmentation data. The tool computes volume metrics, such as tumor volume and dimensions, and visualizes them in a statistical analysis. It is based on R and utilizes modern packages for data analysis, and is deployed as a web interface using the Shiny R package. It enables the user to upload their own lesion segmentation data in NIfTI format. For easy demonstration and proof of concept, it makes use of default data from BraTS, a publicly shared repository of brain lesion data. This tool can be used for segmentation exploration of groups of patients or study participants, and it scales to any cohort size. The tool can be accessed via shinyapps.io [1] [2] and the code is available at my GitHub page [3].