Background In personalized medicine, clinicians and health policy makers must choose the most appropriate clinical trial and make predictions for the right patient during decisionmaking [1, 2]. This approach is used to individualize medical practice. At present, clinicians can predict diseases by many methods like diagnostic imaging technique [3-7] but with fewer predictive models. In recent years, predictive modeling has been successfully applied in the medical scenarios, including the identification of risk factors [8, 9] and early detection of disease onset [10, 11]. In addition, advances have been made in using predictive modeling to predict patient outcomes [2]. The traditional predictive modeling approach involves building a global predictive model using all available training data. However, this may not be the most suitable approach for personalized

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The purpose of this study is to explore the value of CT (computed tomography) continuous scanning by MIMICS (Materiaise's interactive medical image control system) in segmentation modeling of lung cancer images and to analyze the process of building a three-dimensional finite element model of lung cancer quickly and accurately. In this study, MIMICS software is used to conduct data collection of routine CT continuous image for 12 patients diagnosed with lung cancer, and a database is established. The image is divided according to a specific rule, such as grayscale, space, texture, geometric shape and other characteristics, to form a series of meaningful regions. Software MIMICS17.0 is used to perform visualization segmentation, extraction, and 3d rendering to images, followed by data transformation. Because the 3d images constructed by MIMICS software have the advantages of strong editability and wide subsequent application, the lung cancer model images constructed in this study are lifelike in shape, with high geometric similarity and strong sense of solidity, truly reflecting the morphological characteristics of lung cancer and the spatial relationship between adjacent structures. Therefore, by transparently treating the lungs, the internal tumor, bronchial system and vascular system can be observed directly, thereby better presenting the spatial location of the lung tumor. However, due to the limitations of the software itself, the XYZ data of lung of space-occupying lesions could not be output by the program, which needs to be further studied and verified.