Method for evaluating thyroid nodules by combining protein mass spectrometry with deep learning

Abstract

The invention provides a method for evaluating thyroid nodules by combining protein mass spectrometry with deep learning. According to the scheme, a genetic algorithm is adopted to obtain a protein combination capable of reflecting benign and malignant probabilities of thyroid nodules; based on a deep learning model, thyroid nodules are accurately classified by utilizing the selected protein combination; the possibility of malignant nodules is assessed, the surgical treatment cost and the treatment cost generated by subsequent medication can be greatly reduced, and meanwhile, the medical resource waste caused by excessive treatment can be reduced while the life quality of a patient is greatly improved.

Description

technical field [0001] The invention relates to the field of proteomics, in particular to a method for evaluating thyroid nodules by combining proteomics mass spectrometry with deep learning. Background technique [0002] Thyroid nodules refer to lumps in the thyroid gland, which is a common clinical disease and can be caused by a variety of causes. According to the survey, about 50% of the population suffer from thyroid nodules, while only 10% of all thyroid nodules Left and right are malignant nodules. [0003] The classification of thyroid nodules in clinical medicine mainly depends on the results of cytopathology. Cytopathology is to extract a small part of thyroid tissue through biopsy puncture for cell smear to observe the changes of cell morphology, but there are still some benign and malignant nodules that need to be evaluated. Observe whether the tissue invades the capsule to determine whether it is benign or malignant, that is, 30% of the nodules cannot be classif...

Images