Method for predicting liver cancer feature type, electronic equipment and computer storage medium

Abstract

The invention relates to a method for predicting liver cancer feature types, computing equipment and a storage medium. The method comprises the following steps: based on comparison result data about a tumor sample of a to-be-detected object, generating genome variation data about multiple predetermined genes of the tumor sample of the to-be-detected object; acquiring clinical data about the to-be-detected object; determining tumor mutation load information about the to-be-detected object; obtaining immune checkpoint molecular expression data of the to-be-detected object; generating input data of a prediction model at least based on the genome variation data, the clinical data, the tumor mutation load information and the immune checkpoint molecular expression data; and based on a prediction model trained by multiple samples, the prediction model is constructed based on a neural network model. According to the method, the reliability of predicting the liver cancer feature type can be improved, and good generalization of clinical application is achieved.

Description

technical field [0001] The present disclosure relates generally to biological information processing, and in particular, to methods, electronic devices, and computer storage media for predicting liver cancer feature types. Background technique [0002] Studies have shown that the annual recurrence rate of hepatocellular carcinoma (HCC) patients after surgery is as high as 50%, which is the main factor affecting the long-term survival of early-stage HCC patients. Therefore, it is of great significance to accurately predict the characteristic types of liver cancer to assist in judging the risk of liver cancer recurrence. [0003] Traditional methods for predicting the risk of HCC recurrence include, for example, constructing a cytosine-guanine dinucleotide (CpG) methylation signature to predict the risk of postoperative recurrence of early HCC, or based on radiomics, visual analysis, clinical Pathological and other multi-dimensional information to build early HCC recurrence p...

AI Technical Summary

Problems solved by technology

Although the above-mentioned traditional prediction methods have shown certain predictive ability, because the methylation level detection of the whole genome has not been routinely used in clinical work, there is a lack of clinical evidence support, and the interpretation of image results such as radiomics needs to be enriched. Expert experience and time-consuming and labor-intensive, resulting in limited practical application value for clinical translation

[0004] In summary, the traditional method for predicting the risk of liver cancer recurrence has the disadvantages of: either need to be supplemented by expert experience, or lack of clinical evidence support, therefore, It is difficult to balance the generalization of clinical application and the reliability of prediction results at the same time

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