レディオミクス特徴量に基づく機械学習を用いたNSCLCの組織学的サブタイプの分類

抄録

Non-small cell lung cancer (NSCLC) tumors are categorized into three histological subtypes (adenocarcinoma :AC, squamous cell carcinoma : SCC and large cell carcinoma : LC). Histological classification of NSCLC affects the decision making of treatment policies. However, histological subtypes identified from specimens sampled by a single biopsy occasionally differ from those from surgical resection. For increasing the classification accuracy, we aim to develop an automated approach for classifying NSCLC cases into major two histological subtypes, AC and SCC by using two machine learning techniques ; support vector machine (SVM) and random forest (RF) with radiomic features. After calculating intraclass correlation coefficients (ICCs) for investigating reproducible radiomic features, we extracted 31 stable features using CT images of 155 NSCLC patients, and applied five feature selection methods. A leave-one-out cross validation was performed and model parameters were optimized to maximize the area under receiver operating characteristic curves (AUCs). Finally, the optimized models were applied to 50 NSCLC patients. The most robust combination of the feature selection and machine learning techniques was considered as the SVM classification model using the signature constructed by RF, which achieved the highest AUC of 0.7577. The proposed method based on radiomic features could classify NSCLC into AC and SCC.