The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer

作者：Hongyue Zhao, Yexin Su, Mengjiao Wang, Zhehao Lyu, Peng Xu, Yuying Jiao, Linhan Zhang, Wei Han, Lin Tian, Peng Fu · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.875761 · 被引用次数：33 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、AI in cancer detection

Purpose Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[ 18 F]fluoro-D-glucose ([ 18 F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features. Methods One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters—four clinical factors, four laboratory indicators, and 91 [ 18 F]F-FDG PET/CT radiomic features—were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance. Results Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators...