Method for constructing pulmonary nodule computer-aided detection model

A computer-aided and model-testing technology, applied in the computer field, can solve problems such as large trauma, and achieve the effect of good adaptability and easy realization

A computer-aided and model-testing technology, applied in the computer field, can solve problems such as large trauma, and achieve the effect of good adaptability and easy realization

CN110819700APending Publication Date: 2020-02-21上海朗曳医疗科技有限公司

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  • Method for constructing pulmonary nodule computer-aided detection model
  • Method for constructing pulmonary nodule computer-aided detection model

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Embodiment Construction

[0038] In order to illustrate the present invention more clearly, the present invention will be further described below in conjunction with preferred embodiments and accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not limit the protection scope of the present invention.

[0039] The method for constructing a computer-aided detection model of small pulmonary nodules provided in this example uses machine learning methods to analyze the RNAseq data of bronchial epithelial cells of patients with suspected pulmonary nodules, and combines clinical information and imaging to establish A model for distinguishing malignant / non-malignant small nodules.

[0040] Such as figure 1 As shown, the method for building a small pulmonary nodule computer-aided detection model provided by this embodiment inclu...

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Abstract

The present invention discloses a method for constructing a pulmonary nodule computer-aided detection model. The method comprises the following steps: obtaining bronchial epithelial cells of a subjectand extracting RNA; constructing a double-stranded cDNA library according to the RNA and conducting sequencing; comparing a sequencing result with a reference genome and selecting a differential significant gene and a differential significant variation of the subject; using a vector formed by combining the differential significant gene and the differential significant variation, and benign and malignant pulmonary nodules as sample data; randomly dividing the sample data of the subject into a training set and a prediction set and carrying out multiple times of training on the training set andthe prediction set on the basis of a support vector machine model adopting a radial basis kernel function to obtain a model output value of the prediction set; and carrying out non-dimensionalizationon clinical data and CT image data of the subject in the prediction set, deducing a Roc fitting curve by combining a non-dimensionalized data comprehensive value and the model output value, calculating a lower area, and adjusting covariance and penalty factors to be the value corresponding to the maximum value of the lower area to obtain the pulmonary nodule computer-aided detection model.

Description

technical field [0001] The present invention relates to the field of computer technology. More specifically, it relates to a method for constructing a computer-aided detection model of small pulmonary nodules. Background technique [0002] Pulmonary nodules are defined as round or irregular lesions with a diameter of ≤2 cm in the lung, and imaging shows shadows with increased density, which can be single or multiple, with clear or unclear borders. Most diseases of the human lungs can lead to the formation of nodules, and pulmonary nodules mainly include benign lesions and malignant lesions. Among them, benign lesions mainly include hamartoma, hemangioma, inflammatory pseudotumor, and tuberculoma, while malignant lesions mainly refer to primary lung cancer diseases such as bronchioloalveolar carcinoma and lung adenocarcinoma, or malignant tumors in other parts of the body. transfer phenomenon. Relevant clinical studies have shown that 80% to 90% of nodules are benign accor...

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Application Information

Patent Timeline
21 Feb 2020
Publication
CN110819700A
IPC
C12Q1/6858
CPC
C12Q1/6858; C12Q2537/165; C12Q2531/113; C12Q2535/122
Inventors
刘小军; 尹潼