Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features

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Purpose: To evaluate and compare the predictive performance of machine learning methods using clinical-semantic, radiomic, and combined features in distinguishing squamous cell carcinoma (SCC) from adenocarcinoma (ADC) in non-small cell lung cancer (NSCLC). Methods: A total of 399 patients with pathologically confirmed NSCLC were retrospectively enrolled in 2017, and randomly divided into a training set (n=279) and a validation set (n=120). Clinical factors, semantic features, and radiomics features were collected and screened via the minimum redundancy maximum relevance (mRMR) method and least absolute shrinkage and selection operator (LASSO). We investigated 3 models constructed with 4 classifiers for histologic subtype prediction. The models were trained on the training cohort and their performance was evaluated on the independent validation cohort using accuracy, sensitivity, specificity, F1 score, precision and area under the receiver operating characteristic curve (AUC). Results: After feature selection, 10 representative features were finalized, comprising 4 clinical-semantic and 6 radiomic features. In the validation cohort, the support vector machine (SVM) classifier demonstrated promising predictive performance. When integrating clinical-semantic and radiomic features, the combined model (AUC = 0.871) showed potential in distinguishing NSCLC pathological subtypes, outperforming models based solely on clinical-semantic (AUC = 0.594) or radiomic features (AUC = 0.713). It achieved an accuracy of 0.892, a sensitivity of 0.758, a specificity of 0.943, a F1 score of 0.794, and a precision of 0.833. However, the AUC differences were not statistically significant, highlighting the need for further multi-center prospective validation. Conclusion: In this study, the SVM-based combined model, which integrated clinical-semantic and radiomic features, demonstrated promising performance among the four classifiers-based combined models in distinguishing between ADC and SCC. However, due to the study's single-center, retrospective design and the lack of statistically significant differences in AUC for some models, the findings should be interpreted with caution. These results show potential but require future multi-center prospective validation before clinical application.

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