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As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low dose CT scans are still poor, mostly due to noise. Deep learning generative models can be used to denoise these images and in turn improve radiomics' reproducibility and performance. However, most generative models are trained on paired data, which can be difficult or impossible to collect. In this article, we investigate the possibility of denoising low dose CTs using cycle generative adversarial networks (GANs) to improve radiomics reproducibility and performance based on unpaired datasets. Two cycle GANs were trained: 1) from paired data, by simulating low dose CTs (i.e., introducing noise) from high dose CTs; and 2) from unpaired real low dose CTs. To accelerate convergence, during GAN training, a slice-paired training strategy was introduced. The trained GANs were applied to three scenarios: 1) improving radiomics reproducibility in simulated low dose CT images and 2) same-day repeat low dose CTs (RIDER dataset) and 3) improving radiomics performance in survival prediction. Cycle GAN results were compared with a conditional GAN (CGAN) and an encoder-decoder network (EDN) trained on simulated paired data.The cycle GAN trained on simulated data improved concordance correlation coefficients (CCC) of radiomic features from 0.87 to 0.93 on simulated noise CT and from 0.89 to 0.92 on RIDER dataset, as well improving the AUC of survival prediction from 0.52 to 0.59. The cycle GAN trained on real data increased the CCCs of features in RIDER to 0.95 and the AUC of survival prediction to 0.58. The results show that cycle GANs trained on both simulated and real data can improve radiomics' reproducibility and performance in low dose CT and achieve similar results compared to CGANs and EDNs.

Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival prediction and lung cancer diagnosis. We used encoder-decoder networks and conditional generative adversarial networks (CGANs) trained in a previous study as generative models to transform low dose CT images into full dose CT images. Radiomic features extracted from the original and improved CT scans were used to build two classifiers - a support vector machine (SVM) and a deep attention based multiple instance learning model - for survival prediction and lung cancer diagnosis respectively. Finally, we compared the performance of the models derived from the original and improved CT scans. Encoder-decoder networks and CGANs improved the area under the curve (AUC) of survival prediction from 0.52 to 0.57 (p-value<0.01). On the other hand, Encoder-decoder network and CGAN can improve the AUC of lung cancer diagnosis from 0.84 to 0.88 and 0.89 respectively (p-value<0.01). Moreover, there are no statistically significant differences in improving AUC by using encoder-decoder network and CGAN (p-value=0.34) when networks trained at 75 and 100 epochs. Generative models can improve the performance of low dose CT-based radiomics in different tasks. Hence, denoising using generative models seems to be a necessary pre-processing step for calculating radiomic features from low dose CTs.

Radiomics is an active area of research in medical image analysis, the low reproducibility of radiomics has limited its applicability to clinical practice. This issue is especially prominent when radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans. In this article, we investigate the possibility of improving the reproducibility of radiomic features calculated on noisy CTs by using generative models for denoising.One traditional denoising method - non-local means - and two generative models - encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) - were selected as the test models. We added noise to the sinograms of full dose CTs to mimic low dose CTs with two different levels of noise: low-noise CT and high-noise CT. Models were trained on high-noise CTs and used to denoise low-noise CTs without re-training. We also test the performance of our model in real data, using dataset of same-day repeat low dose CTs to assess the reproducibility of radiomic features in denoised images. The EDN and the CGAN improved the concordance correlation coefficients (CCC) of radiomic features for low-noise images from 0.87 to 0.92 and for high-noise images from 0.68 to 0.92 respectively. Moreover, the EDN and the CGAN improved the test-retest reliability of radiomic features (mean CCC increased from 0.89 to 0.94) based on real low dose CTs. The results show that denoising using EDN and CGANs can improve the reproducibility of radiomic features calculated on noisy CTs. Moreover, images with different noise levels can be denoised to improve the reproducibility using these models without re-training, as long as the noise intensity is equal or lower than that in high-noise CTs. To the authors' knowledge, this is the first effort to improve the reproducibility of radiomic features calculated on low dose CT scans.

Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer computer aided diagnosis (CAD) can play a crucial role. However, most published CAD methods treat lung cancer diagnosis as a lung nodule classification problem, which does not reflect clinical practice, where clinicians diagnose a patient based on a set of images of nodules, instead of one specific nodule. Besides, the low interpretability of the output provided by these methods presents an important barrier for their adoption. In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output. We chose radiomics as the source of input features and deep attention-based MIL as the classification algorithm.The attention mechanism provides higher interpretability by estimating the importance of each instance in the set for the final diagnosis.In order to improve the model's performance in a small imbalanced dataset, we introduce a new bag simulation method for MIL.The results show that our method can achieve a mean accuracy of 0.807 with a standard error of the mean (SEM) of 0.069, a recall of 0.870 (SEM 0.061), a positive predictive value of 0.928 (SEM 0.078), a negative predictive value of 0.591 (SEM 0.155) and an area under the curve (AUC) of 0.842 (SEM 0.074), outperforming other MIL methods.Additional experiments show that the proposed oversampling strategy significantly improves the model's performance. In addition, our experiments show that our method provides an indication of the importance of each nodule in determining the diagnosis, which combined with the well-defined radiomic features, make the results more interpretable and acceptable for doctors and patients.

Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriate evaluation of recommendation algorithms. In this paper, we revisit alternative experimental settings for evaluating top-N recommendation algorithms, considering three important factors, namely dataset splitting, sampled metrics and domain selection. We select eight representative recommendation algorithms (covering both traditional and neural methods) and construct extensive experiments on a very large dataset. By carefully revisiting different options, we make several important findings on the three factors, which directly provide useful suggestions on how to appropriately set up the experiments for top-N item recommendation.