Deep Learning-based Computer-aided Diagnosis Of Lymph Node Metastasis In Non-small Cell Lung Cancer

As one of the most threatening cancers to life,lung cancer has always had high morbidity and mortality.Non-small cell lung cancer(NSCLC)accounts for85% of all lung cancers.In general,once tumor metastasis has occurred,the survival and prognosis of patients will also become worse.Computer-aided diagnosis of whether lymph node metastasis(LNM)occurs,the probability of LNM can be calculated in a non-invasive way,further providing radiologists with the necessary information to improve diagnostic accuracy and help improve patient survival and prognosis.Positron Emission Tomography-Computed Tomography(PET-CT)imaging is considered the imaging method of choice for assessing,staging,and diagnosing lung cancer.Radiomics refers to the extraction and analysis of high-dimensional mineable data from medical images,which is an important prognostic tool for cancer risk assessment.Therefore,in this paper,the radiomics features are extracted from PET-CT images and deep learning methods are used to assist in the diagnosis of whether LNM occur in patients with NSCLC.The main studies are as follows:(1)Computer-aided diagnosis of LNM of NSCLC based on deep learning.In this study,an end-to-end deep learning architecture is proposed,which integrates traditional imaging radiomics features,deep learning features,clinical features,and physician diagnosis for LNM computer-aided diagnosis.Firstly,the traditional radiomics features of the patient are extracted from the PET images of the patient,and then the features with significant differences in the image scanner are screened out by the Mann Whitney U test;secondly,the 3D convolutional neural network(CNN)and the 2D CNN are used to extract the deep learning features of the 3D primary tumor and the maximum intensity projection(MIP)of the 2D frontal bust,respectively;finally,the traditional radiomics features and deep learning features are fused,and then the clinical features and doctor’s diagnosis results are added to diagnose whether the lymph nodes have metastasized.This paper conducts PET-CT imaging and clinical data from 121 patients with NSCLC.On the test set,the model achieved an accuracy of 0.82 AUC and 0.86,which was significantly better than the doctor’s diagnosis(AUC: 0.61,precision: 0.80),and confirmed that the fusion of multiple features can assist in the diagnosis of NSCLC lymph node metastasis in a non-invasive manner.(2)Two-stage computer-aided diagnosis of LNM in NSCLC.This study proposes a two-stage deep learning architecture that makes the network pay attention to the region of interest by splitting the task,automatically learning classification characteristics,and eliminating the dependence of the test phase model on the gold standard outlined by the doctor.Firstly,in the first stage,a segmentation network is used to segment the tumor of the frontal bust MIP image,roughly localizing the tumor area,which is used as the basis for the test set processing in the second stage of 3D tumor feature extraction;secondly,in the second stage,the 3D segmentation-classification model and the 2D segmentation-classification model are used to extract deep learning characteristics from the 3D tumor and the 2D frontal bust MIP;finally,the two deep learning features are fused to assist in the diagnosis of LNM.The model achieved an AUC of 0.74 and an accuracy of 0.84 on the test set,confirming that good performance could be achieved without the gold standard of tumors outlined by the doctor in the test set.