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Non-small cell lung cancer prognosis survival prediction method, medium and electronic equipment

A non-small cell lung cancer and survival prediction technology, applied in neural learning methods, biological neural network models, image data processing, etc., can solve problems such as time-consuming and labor-intensive, uncertain validity of hand-crafted features, and improve prediction accuracy, The effect of reducing the cost of medical treatment and improving the efficiency of prediction

Pending Publication Date: 2021-01-05
SHANGHAI UNIV OF MEDICINE & HEALTH SCI +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This type of method improves the prediction accuracy to a certain extent, but extracting features of interest from medical images requires experienced doctors to identify or describe them, which is time-consuming and laborious, and there are uncertainties in the effectiveness of hand-crafted features

Method used

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  • Non-small cell lung cancer prognosis survival prediction method, medium and electronic equipment
  • Non-small cell lung cancer prognosis survival prediction method, medium and electronic equipment
  • Non-small cell lung cancer prognosis survival prediction method, medium and electronic equipment

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Embodiment 1

[0038] In-depth learning is based on the deep superposition of artificial neural network (ANN) in machine learning, which is an expansion of traditional neural networks. It realizes classification and prediction by combining low-level features to form more abstract high-level features. Due to its unique advantages, deep learning is developing faster and faster in various fields of medicine, such as the prognosis analysis of cancer. Radiomics uses automated algorithms to mine a large amount of feature information from the Area of ​​Interest (ROI) of radiological images as the research object, and extracts effective key information through statistical methods for auxiliary diagnosis, classification or grading of diseases. Computed tomography (Computed tomography, CT) is one of the commonly used means of lung examination, and it is also one of the important modalities in radiomics. It is easy to collect and compare, and it is useful in distinguishing tumor lesions with different h...

Embodiment 2

[0060] This embodiment provides a computer-readable medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for predicting the prognosis and survival of non-small cell lung cancer as described in Embodiment 1 are implemented.

Embodiment 3

[0062] This embodiment provides an electronic device for predicting the prognosis and survival of non-small cell lung cancer, including a CT image acquisition module and a prediction module, wherein the CT image acquisition module is used to acquire a CT image to be predicted, and gray out the CT image to be predicted. degree normalization process, and extract the region of interest; the prediction module maintains a prognostic survival model based on deep learning, and based on the region of interest, use the trained prognostic survival model based on deep learning to predict and obtain the corresponding prognostic survival classification result. The prognostic survival model based on deep learning is a deep learning convolutional neural network model, including 5 convolutional blocks, 1 fully connected layer and 1 classification layer, abstract tumor features are extracted layer by layer, and the classification results of prognosis and survival are obtained , among the five ...

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Abstract

The invention relates to a non-small cell lung cancer prognosis survival prediction method, a medium and electronic equipment. The method comprises the steps: obtaining a to-be-predicted CT image, carrying out the gray normalization of the to-be-predicted CT image, and extracting a region of interest; based on the region of interest, adopting a trained prognosis survival model based on deep learning to predict and obtain a corresponding prognosis lifetime classification result, wherein the prognosis survival model based on deep learning is a deep learning convolutional neural network model, and comprises five convolution blocks, a full connection layer and a classification layer, tumor abstract features are extracted layer by layer, a prognosis lifetime classification result is obtained, aBottleneck architecture is introduced into three convolution blocks in the middle of the five convolution blocks, and a fusion layer is added to the last convolution block on the basis of the Bttleneck architecture. Compared with the prior art, the method has the advantages of high prediction precision, convenience in implementation and the like.

Description

technical field [0001] The invention relates to the field of computer-aided medicine, relates to a computer electronic device, in particular to a method, a medium and an electronic device for predicting the prognosis and survival of non-small cell lung cancer. Background technique [0002] According to the latest report of the International Agency for Research on Cancer in 2018, lung cancer is the cancer with the highest morbidity and mortality rate in the world. Among them, patients with non-small cell lung cancer (NSCLC) account for 80% to 85% of the total number of lung cancer patients. %, about 3 / 4 of the patients were found in the middle and advanced stages, and the 5-year survival rate was low. In addition, due to the heterogeneity of tumors, different individuals show different therapeutic effects and prognosis, and even tumor cells in the same individual have different characteristics and differences. Therefore, there is an urgent need for an accurate, objective and...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/084G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30096G06T2207/30061G06V10/25G06V2201/03G06N3/045G06F18/241G06F18/214Y02A90/10
Inventor 黄钢聂生东陈雯
Owner SHANGHAI UNIV OF MEDICINE & HEALTH SCI
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