Liver cancer image omics data processing method, system and device and storage medium

Abstract

The invention discloses a liver cancer image omics data processing method, system and device and a storage medium. The method comprises the steps of obtaining an upper abdomen CT image of a liver cancer patient, calculating image omics data corresponding to the upper abdomen CT image, calculating a linear combination of the plurality of image omics characteristic values, and dividing the liver cancer patient into a hepatic artery chemoembolization reaction group or a hepatic artery chemoembolization reaction-free group according to a magnitude relation between the predicted value and a presetthreshold value. The used imaging omics technology can extract the tumor feature information contained in the CT image from the three-dimensional perspective in an omnibearing mode, and the defect that tumor heterogeneity is not considered in an existing method is overcome; and patients are divided into a reaction group and a non-reaction group according to scores of the imaging characteristics ofpatients, so that the clinical guiding significance is good, and a better decision reference is provided for clinical workers. The method is widely applied to the technical field of data processing.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a data processing method, system, device and storage medium of liver cancer radiomics. Background technique [0002] Primary liver cancer is currently the sixth most common malignant tumor in the world, and its fatality rate also ranks third in the world. Since the symptoms of early liver cancer are not typical, and most patients have no obvious discomfort, most patients with liver cancer are already in the advanced stage of tumor when they are diagnosed, and even have vascular invasion or distant metastasis, and have lost radical curative effects such as surgical resection or liver transplantation. treatment approach. [0003] According to the pathological type, primary liver cancer can be divided into three types, namely, hepatocellular carcinoma, intrahepatic cholangiocarcinoma and mixed liver cancer, among which hepatocellular carcinoma accounts for 80% of al...

AI Technical Summary

Problems solved by technology

The prior art has the following deficiencies: the differences in age, gender and physiological factors between individual patients make some biochemical indicators measured clinically different from the actual values, which affects the prediction accuracy; the measurement methods of biochemical indicators such as blood biochemical values ​​have not Standardization makes the prediction results in different environments unstable; the characteristics reflected by the existing technical means are relatively single, and cannot fully reflect the characteristics of tumor heterogeneity; during the measurement process using electronic counters, there may be multiple variables that affect biochemical Result authenticity; tumor heterogeneity difficult to quantify

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