Tohoku Univ. Technology : Cancer prognosis prediction method : T20-2962
Development of prognostic prediction method with the novel radiomic feature based on graph theory
In recent years, the research field to decode the biological characteristics of lesion (radiomics) by analyzing in global a large number of images extracted from medical image database has attracted attention. One potential application of radiomics is to predict the patient prognosis with tumor disease. However, it is difficult to represent phenotypic information of tumor and local information inside tumor with accuracy, so further progress of this research is required. In this invention, inventors have developed a new radiomic feature based on graph theory. Medical image processed by graph theory feature, and tumor image with relatively good prognosis have a smaller line density than those with poor prognosis. Moreover, the rad score calculated by graph theory feature can clearly indicate the tumor status change compared to the value calculated by conventional feature. Therefore, this invention is able to predict the cancer treatment prognosis with higher accuracy than conventional method.
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The revenue generated from technology transfer is reinvested as new research funding for universities and researchers, and is utilized to create further research outcomes. To ensure the smooth operation of this cycle, known as the "Intellectual Creation Cycle," we will vigorously promote technology transfer. The types of seeds we handle include patents, know-how, databases, and programs. We have established a collaborative framework by signing basic technology transfer agreements with the following universities (as of June 1, 2025): Tohoku University, Hirosaki University, Iwate University, Akita University, Fukushima University, Yamagata University, Tohoku Gakuin University, Iwate Medical University, Fukushima Medical University, Aizu University, Miyagi University, Hokkaido University, Muroran Institute of Technology, and Showa Medical University.
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