Non-small cell lung cancer risk prediction method based on machine learning

A non-small cell lung cancer and risk prediction technology, applied in the medical field, can solve problems such as differences in prediction results and inaccurate predictions, and achieve the effect of improving diagnostic accuracy and efficiency

Pending Publication Date: 2021-03-19
QINGDAO UNIV
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  • Abstract
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Problems solved by technology

[0003] The present invention provides a method for predicting the risk of non-small cell lung cancer based on machine learning, which solves the problem that the risk of non-sm

Method used

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  • Non-small cell lung cancer risk prediction method based on machine learning
  • Non-small cell lung cancer risk prediction method based on machine learning
  • Non-small cell lung cancer risk prediction method based on machine learning

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Embodiment

[0040] Refer figure 1 In this embodiment, the risk prediction method of non-small cell lung cancer based on machine learning is mainly used to extract typical characteristics of more common chest CT images at all levels, combined with clinical pathological characteristics, and construct non-small cells using joint NOMOGRAM machine learning algorithms. Risk prediction model of lung cancer, automatic risk prediction, and generate a column chart to assist diagnosis. The method sequentially includes the following steps:

[0041] S1, typical feature extraction of the patient's chest CT image, remember to CT image characteristics,

[0042] S2, obtain CT imaging features of all patients in the sample sample, combined with clinical pathological characteristics of all patients in the sample sample, form sample data sets, and preprocessing the CT image features in the sample data set,

[0043] S3, the pre-processed sample data set is divided into test sets and training sets,

[0044]S4, fo...

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Abstract

The invention provides a non-small cell lung cancer risk prediction method based on machine learning, and the method comprises the steps of carrying out the typical feature extraction of a chest CT image of a patient, and recording the typical feature as a CT image feature; obtaining CT image features of all patients in the sampling sample, forming a sample data set by combining clinical pathologyfeatures of all patients in the sampling sample, and preprocessing the CT image features in the sample data set; dividing the preprocessed sample data set into a test set and a training set; and performing feature screening on CT image features and clinical pathology features of patients in the divided test set and training set, and based on the training set subjected to feature screening, performing prediction training on high and low risks of non-small cell lung cancer by adopting a joint Nomogram model to obtain a non-small cell lung cancer risk prediction result. The invention can accumulate the experience of excellent doctors and experts, so as to copy the experience to other small cities and hospitals for popularization and application, improve the risk prediction accuracy, and improve the treatment effect of patients.

Description

Technical field [0001] The present invention belongs to the field of medical technology, and more particularly to a risk prediction method of non-small cell lung cancer based on machine learning. Background technique [0002] Lung cancer is one of the most common malignant tumors in the world, and has become the first place in the cause of malignant tumors in urban population in my country. About 75% of patients with non-small cell lung cancer were already in the middle and late stage, and the 5-year survival rate was very low. If the patient does not get timely diagnosis and treatment, it will miss the best treatment opportunity to face life. By combining CT image characteristics and clinical pathological characteristics, non-small cell lung cancer risk prediction, prediction results are divided into high risks and low risks, and doctors can arrange reasonable treatment programs according to the forecast results, and improve the treatment effect. However, according to CT imaging...

Claims

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Application Information

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IPC IPC(8): G16H50/30G16H30/40G06T7/00G06T7/11G06T7/62G06N20/00
CPCG16H50/30G16H30/40G06T7/0012G06T7/11G06T7/62G06N20/00G06T2207/10081G06T2207/30096
Inventor 宋瑞杰杨海强
Owner QINGDAO UNIV
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