Abstract:

Objective To predict the classification of small cell lung cancer and nonsmall cell lung cancer based on Radiomics. Methods This study involved 131 patients with small cell lung cancer and non-small cell lung cancer (including 119 in the training cohort and 12 in the validation cohort). The 107-dimensional omics features were extracted from the manually segmented lesions. The FSelector package in R statistical software was used to screen the key features of the phenomenological features. The support vector machines model and the k-fold cross-validation model were used to classify the pathology of lung cancer patients. The effect of lung cancer typing prediction in the training cohort and validation cohort was evaluated by plotting the receiver operating characteristic curve(ROC) and calculating the area under curve(AUC) values. Results This study selected 20 major Radiomics features for the differential diagnosis of small cell lung cancer and non-small cell lung cancer. These features were well differentiated between small cell lung cancer and non-small cell lung cancer in the training cohort and validation cohort. In the validation cohort, the accuracy of the pre-lung cancer subtype classification was 75%, and the AUC result of the radiomics characteristics was 0.69. Conclusion This paper constructs a unique radiomics feature to be used as a diagnostic factor for distinguishing between small cell lung cancer and nonsmall cell lung cancer, which has important guiding significance for the realization of non-invasive pathologically effective classification of lung cancer and guiding the selection of follow-up treatment options for lung cancer patients.

Key words: Radiomics, Lung cancer, Support vector machine, k-fold cross-validation, Typing prediction, Human