System and method for predicting tumor mutation load of patient with advanced lung cancer

Abstract

The invention provides a system and a method for predicting tumor mutation load of a patient with advanced lung cancer. The system comprises a data acquisition module for acquiring a first lung tomography image and a real tumor mutation load of an early lung cancer patient, and a second lung tomography image and clinical survival data of an advanced lung cancer patient; a first processing module used for labeling by adopting a preset labeling box to obtain a first lung labeling image and a second lung labeling image; adding each first lung labeling image and the real tumor mutation load into afirst data set; a model training module used for training according to the first data set to obtain a tumor mutation load prediction model; a second processing module used for inputting each second lung labeling image into the tumor mutation load prediction model to obtain a predicted tumor mutation load; and performing survival analysis according to the clinical survival data to obtain a survival analysis curve, and processing to obtain the similarity between the distribution curve for predicting the tumor mutation load and the survival analysis curve. The prediction accuracy is effectivelyimproved; and the cost is saved.

Description

technical field [0001] The present invention relates to the field of deep learning technology, in particular to a system and method for predicting tumor mutation load of patients with advanced lung cancer. Background technique [0002] Lung cancer is the leading cause of cancer-related death worldwide. In the treatment of lung cancer, novel immune checkpoint inhibitors can induce durable anti-tumor effects. However, for advanced lung cancer, only a small number of patients can respond to immune checkpoint inhibitors and obtain the expected anti-tumor effect. Therefore, before receiving immunotherapy, it is very important to accurately predict the clinical response of patients and to identify the population benefiting from immunotherapy for the treatment of advanced lung cancer. [0003] At present, many biomarkers have been confirmed to be correlated with the efficacy of immunotherapy, such as the expression of programmed death receptor-1 (PD-L1), tumor-infiltrating lympho...

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Problems solved by technology

However, no studies have explored its predictive significance in patients with advanced lung cancer treated with immune checkpoint inhibitors

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