Histological Subtype Classification Of Non-small Cell Lung Cancer Based On PET/CT Image

Lung cancer is a malignant tumor.Currently,the incidence of lung cancer remains high and is one of the leading causes of cancer deaths worldwide.In clinical practice,non-small cell lung cancer accounts for more than 80% of primary lung cancers and is the major component of primary lung cancer.Histological subtype is an important factor in the development of treatment plans for patients with non-small cell lung cancer,and differentiating the histological subtype of non-small cell lung cancer patients before surgery is important for personalized treatment of non-small cell lung cancer patients.A combined model and fully automated classification model for automatic and noninvasive distinguishing the histological subtypes of non-small cell lung cancer patients was established by Radiomics and deep learning methods on a dataset containing 151 non-small cell lung cancer patients whose histological subtypes were pathologically confirmed as squamous cell carcinoma and adenocarcinoma,in order to distinguish the histological subtypes of non-small cell lung cancer patients more accurately and noninvasively before surgery.The combined model of non-small cell lung cancer histological subtype classification was constructed by combining handcrafted features obtained from traditional Radiomics,deep learning features obtained from convolutional neural networks constructed by deep learning methods,and clinical information of non-small cell lung cancer patients using a machine learning classifier as the final classification tools.Using a series of comparative experiments,this study confirmed that this combined model performed better for the non-small cell lung cancer histological subtype classification task(AUC= 0.834,accuracy= 0.755,precision= 0.752,recall= 0.736,and F1 value= 0.740)and outperformed the results obtained using either traditional imaging histology or convolutional neural network models alone.In the fully automated classification model for non-small cell lung cancer histological subtypes,this study overcame the dependence on physician manually labeled patient lesion areas in non-small cell lung cancer histological subtype classification studies,constructed three fully automated classification models,and selected the 2D deep learning method based on the maximum intensity projection of patient PET/CT images as the best fully automated classification model by experimental comparison,and in this fully automated The classification accuracy of 0.768 was obtained in this fully automated classification model.This article shows that both conventional Radiomics method and deep learning techniques can distinguish histological subtypes of non-small cell lung cancer patients,in which the combined model built by fusing manual features obtained from conventional Radiomics method,deep learning features obtained from convolutional neural networks constructed by deep learning methods,and clinical information of non-small cell lung cancer patients using machine learning classifiers as the final classification tools has more advantages than the other two methods.Furthermore,it is feasible and effective for non-small cell lung cancer histologic subtypes to be classified directly from the patient’s medical images,without relying on the physician’s manual labeling of the patient’s lesion area.