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Background  Radiomics suffers from feature reproducibility. We studied the variability of radiomics features and the relationship of radiomics features with tumor size and shape to determine guidelines for optimal radiomics study.    Methods  We dealt with 260 lung nodules (180 for training, 80 for testing) limited to 2 cm or less. We quantified how voxel geometry (isotropic/anisotropic) and the number of histogram bins, factors commonly adjusted in multi-center studies, affect reproducibility. First, features showing high reproducibility between the original and isotropic transformed voxel settings were identified. Second, features showing high reproducibility in various binning settings were identified. Two hundred fifty-two features were computed and features with high intra-correlation coefficient were selected. Features that explained nodule status (benign/malignant) were retained using the least absolute shrinkage selector operator. Common features among different settings were identified, and the final features showing high reproducibility correlated with nodule status were identified. The identified features were used for the random forest classifier to validate the effectiveness of the features. The properties of the uncalculated feature were inspected to suggest a tentative guideline for radiomics studies.    Results  Nine features showing high reproducibility for both the original and isotropic voxel settings were selected and used to classify nodule status (AUC 0.659–0.697). Five features showing high reproducibility among different binning settings were selected and used in classification (AUC 0.729–0.748). Some texture features are likely to be successfully computed if a nodule was larger than 1000 mm 3 .    Conclusions  Features showing high reproducibility among different settings correlated with nodule status were identified.    Electronic supplementary material  The online version of this article (10.1186/s40644-019-0239-z) contains supplementary material, which is available to authorized users.

Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer