Intelligent prediction method and system for NSCLC lymph node metastasis risk

Abstract

The invention discloses an intelligent prediction method and system for an NSCLC lymph node metastasis risk. The method comprises the following steps: acquiring Hamp of a lung cancer patient; e, dyeing the full-view digital section and clinical data of the patient; performing quality control, color block processing and screening on the image data; and applying the preprocessed color blocks to a two-stage analysis process, wherein the two-stage analysis process comprises establishing a CNN model for identifying non-cancer tissues and a CNN model for predicting early-stage non-small cell lung cancer lymph node metastasis. The analysis of the two parts comprises the steps of dividing color block images into a training set and a verification set, constructing a model through a CNN algorithm, optimizing parameters of the trained CNN model, and verifying the optimized CNN model in the verification set. And finally predicting the risk of early non-small cell lung cancer lymph node metastasis by using the detected CNN model. The deep learning algorithm is adopted to judge the pathological section, so that the accuracy of tumor metastasis risk assessment is improved, the medical cost is greatly reduced, and the method is easy to widely popularize.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence detection, and in particular relates to a method and system for predicting the risk of lymph node metastasis of early non-small cell lung cancer based on a convolutional neural network. Background technique [0002] Primary lung cancer is currently the malignant tumor with the highest morbidity and mortality in the world, and primary non-small cell lung cancer (NSCLC) is the main pathological type of primary lung cancer. The detection rate of NSCLC is increasing. Precise treatment for early stage NSCLC has important clinical significance. [0003] To achieve precise treatment of early-stage lung cancer, it is necessary to achieve the goal of radical cure on the basis of the least operation time and minimal surgical trauma. However, although the operation time of simple lumpectomy is short, some patients develop regional lymph node metastasis 1 year after surgery. Mass excision a...

AI Technical Summary

Problems solved by technology

Because it is performed manually, it is limited by human factors, and has time and reagent consumable costs

However, there is a lack of relevant technologies for the application of artificial intelligence in NSCLC lymph node metastasis, which requires effective artificial intelligence algorithms, a large amount of image data support, and powerful graphics central processing unit (GPU), and the entire training process requires the guidance of professional clinical oncologists

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