Lung cancer multi-omics detection system

Abstract

The invention provides a lung cancer multi-omics detection system, and relates to the field of lung cancer research. The system comprises a clinical data collection module, a clinical sample and information acquisition module and a test data standardization and analysis module. The clinical sample and information acquisition module comprises a rule setting unit, a peripheral blood collecting and processing unit, an epigenome test unit and a proteome and metabolome test unit; the test data standardization and analysis module comprises a pulmonary nodule AI automatic analysis unit and a multi-omics data analysis and pulmonary nodule benign and malignant identification model construction unit. Patients with pulmonary nodules are detected and evaluated on the basis of medical images in combination with related technical means such as peripheral blood epimics, proteomics and metabonomics, and benign and malignant difference biomarkers of pulmonary nodules, apparent modification mechanisms and protein expression and metabolism differences caused by the effects of the benign and malignant difference biomarkers of pulmonary nodules are studied, so that early warning for lung cancer is established, and a clinical lung cancer detection scheme with high sensitivity and high specificity for identifying benign and malignant pulmonary nodules is provided.

Description

technical field [0001] The invention relates to the field of lung cancer research, in particular to a lung cancer multi-omics detection system. Background technique [0002] The morbidity and mortality of lung cancer in China rank first in the world. Early detection and diagnosis of lung cancer are the key to lung cancer prevention and survival. Accurate assessment of the aggressiveness of lung adenocarcinoma directly determines the choice of treatment options and prognosis. Relying on traditional imaging, computer-aided diagnosis, and radiomics to assess the aggressiveness of lung adenocarcinoma has certain limitations in its accuracy and robustness. The deep learning method (Deep Learning) is to realize the automatic depth analysis of medical imaging images, high-precision intelligent auxiliary diagnosis and integration of multi-omics liquid biopsy, and to develop a high-sensitivity, high-efficiency Specific lung cancer markers provide new opportunities. At present, the...

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Problems solved by technology

At present, the improvement of imaging diagnostic efficiency faces two bottleneck problems: one is that the scanning scheme and image resolution standards have not been established; the other is that the resolution of traditional CT images cannot support in-depth research on the correlation between tumors and surrounding tissues

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