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Prediction method and system for latent N2 lymph node metastasis of surrounding NSCLC

A technology of lymph node metastasis and prediction method, applied in neural learning methods, biological neural network models, image data processing, etc., can solve the problems of small area, unsatisfactory effect of excluding occult N2 lymph node metastasis, low sensitivity, etc., to achieve The effect of improving accuracy and reliability

Pending Publication Date: 2020-10-30
徐州市肿瘤医院
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  • Claims
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AI Technical Summary

Problems solved by technology

[0002] PET-CT plays an important role in the cTNM staging of NSCLC (Non-small cell lung cancer) patients and guides clinical treatment, but there are still false negatives in the diagnosis of cN staging
The 2020 edition of the National Comprehensive Cancer Network (NCCN) pointed out that for NSCLC patients with definite N2 metastases in the cTNM stage, concurrent radical chemoradiotherapy + Durvalumab drug therapy can be selected (class I evidence). However, in practice, about 25 Occult N2 lymph node metastasis occurs in 100% of cN1 (clinicalN1) patients. Although NCCN recommends endoscopic ultrasound and mediastinoscopy for NSCLC patients with no metastatic lymph nodes detected by PET-CT, Dooms’ research confirmed that endoscopic examination Sensitivity to detect occult N2 nodal metastases in cN1 is low
Moreover, Meta-analysis showed that the value of mediastinoscopy in detecting occult N2 lymph node metastasis is still controversial
Therefore, the current conventional mediastinal staging method is not ideal for excluding occult N2 lymph node metastasis in cN1 patients
Diagnosis of N2 lymph node metastasis by conventional parameters of PET-CT, although it has certain value, but the area under the curve is not high

Method used

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  • Prediction method and system for latent N2 lymph node metastasis of surrounding NSCLC
  • Prediction method and system for latent N2 lymph node metastasis of surrounding NSCLC
  • Prediction method and system for latent N2 lymph node metastasis of surrounding NSCLC

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Embodiment 1

[0033] refer to Figure 1 ~ Figure 4 , which is the first embodiment of the present invention, the first embodiment of the present invention provides a method for predicting occult N2 lymph node metastasis of peripheral NSCLC, including:

[0034] S1: The radiomics and clinicopathological features of the cN1 (clinical N1) stage primary tumor and ipsilateral hilar lymph nodes in PET-CT were collected. What needs to be explained is:

[0035] (1) Collect high-quality and standardized PET and CT for judgment and evaluation;

[0036] (2) Utilize automatic, semi-automatic segmentation method to delineate ROI from the acquired image;

[0037] (3) Convolutional neural networks are used to extract radiomics features in ROI, which include the description of intensity distribution, the spatial relationship between different intensity levels, the shape and heterogeneity of texture patterns, and the analysis of primary tumor or lymph nodes and surrounding tissues. description of the rela...

Embodiment 2

[0079] refer to Figure 5 ~ Figure 6 , which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a prediction system for occult N2 lymph node metastasis of peripheral NSCLC, including:

[0080] The collection module 100 is used to collect radiomics features and clinicopathological features of the cN1 stage primary tumor and ipsilateral hilar lymph nodes in PET-CT;

[0081] The modeling module 200 is connected with the acquisition module 100, and it is used to call the omics feature and clinicopathological feature to construct the Nomogram model. Generally speaking, when drawing the Nomogram, the assignment range of each factor is required to be between 0-100;

[0082] The prediction module 300 is connected to the modeling module 200, which is used to score patients in the clinic through the Nomogram model. Each score has a corresponding risk probability coefficient on the Nomogram model, and the risk pro...

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Abstract

The invention discloses a prediction method and system for latent N2 lymph node metastasis of surrounding NSCLC, and the method comprises the steps: collecting the imaging omics features and clinicalpathological features of a primary lesion in a clinical staging N1 stage and a pulmonary portal lymph node at the same side in PET-CT; establishing a Nomogram model by utilizing the imaging omics characteristics and the clinical pathological characteristics; scoring a patient in clinic based on the Nomogram model to obtain a corresponding risk probability coefficient; and utilizing the risk probability coefficient to predict and evaluate the probability of occurrence and transfer of the latent N2 lymph node. According to the invention, the image omics features are extracted through the convolutional neural network, so the accuracy and reliability of the classification or prediction of the imaging omics are further improved; the Nomogram based on the imaging omics can provide personalized information about whether lymph node metastasis exists or not according to the actual situation of each non-small cell lung cancer patient more visually and more accurately, so the unnecessary medicalexamination and operation of the patient are avoided.

Description

technical field [0001] The present invention relates to the technical field of radiomics and deep learning, in particular to a method and system for predicting occult N2 lymph node metastasis of peripheral NSCLC. Background technique [0002] PET-CT plays an important role in the cTNM staging of NSCLC (Non-small cell lung cancer) patients and guides clinical treatment, but there are still false negative problems in the diagnosis of cN staging. The 2020 edition of the National Comprehensive Cancer Network (NCCN) pointed out that for NSCLC patients with definite N2 metastases in the cTNM stage, concurrent radical chemoradiotherapy + Durvalumab drug therapy can be selected (class I evidence). However, in practice, about 25 Occult N2 lymph node metastasis occurs in 100% of cN1 (clinicalN1) patients. Although NCCN recommends endoscopic ultrasound and mediastinoscopy for NSCLC patients with no metastatic lymph nodes detected by PET-CT, Dooms’ research confirmed that endoscopic exa...

Claims

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Application Information

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IPC IPC(8): G06T7/00G16H50/20G16H50/50G06N3/08G06N3/04
CPCG06T7/0012G16H50/20G16H50/50G06N3/08G06T2207/20104G06T2207/30096G06T2207/10104G06N3/045
Inventor 李晓峰姚标
Owner 徐州市肿瘤医院
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