Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various
image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to
the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional
radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy
prediction on an open database that consists of 1297 manually delineated lung nodules.
Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images.
Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung
nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection
and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By
pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets
with respect to malignancy prediction.
Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved
AUROC values (mean±standard deviations) of 0.792±0.025, 0.801±0.018, and 0.817±0.032, respectively through 5-fold crossvalidation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as
well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models
achieved AUROC values of 0.921±0.010, 0.824±0.021, and 0.936±0.011, respectively. We achieved the best prediction accuracy from
the hybrid feature set (AUROC: 0.938±0.010).
Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On
the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and
deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising
model for lung nodule malignancy prediction in this comparative study.