Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets.

objectiveAccurate diagnosis and early treatment are crucial for survival in patients with brain metastases. This study aims to expand the capability of radiomics-based classification algorithms with novel features and compare results with deep learning-based algorithms to differentiate the subtypes of lung cancer from MRI of metastatic lesions in the brain.

methodsThis study includes 75 small cell lung carcinoma, 72 squamous cell carcinoma, and 75 adenocarcinoma segments. For the radiomics-based algorithm, novel features from the original Laplacian of Gaussian filtered and two-dimensional wavelet transformed images were extracted, and a new three-stage feature selection algorithm was proposed for feature selection. Two classification methods were applied to images to identify the subtypes of lung cancer. Additionally, EfficientNet and ResNet with transfer learning were used as classifiers to compare the results of the proposed algorithm.

resultsThe sensitivity and specificity values of the radiomics-based classifier are 94.44 and 95.33%, and for the second classifier are 87.67% and 92.62%, respectively. Besides, a one

conclusionThe results suggest that the proposed radiomics-based algorithm is a helpful diagnostic assistant to improve decision-making for treating patients with brain metastases in small datasets. ADVANCES IN KNOWLEDGE: Firstly, the proposed method of this study extracts novel features from transformations of the original images, such as wavelet and Laplacian of Gaussian filter for the first time in literature. Secondly, this is the first study that investigates the classification performance of the shallow and deep learning approaches to identify subtypes of lung cancer.