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We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models.EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis. Three radiomics and three clinical models were then constructed for comparison.Patients with EfficientNetV2-based model score ≤ 0.5 had better TTP than those with higher scores (hazard ratio [HR]: 0.32, 95%CI: 0.22-0.46, P 0.0001; HR: 0.28, 95%CI: 0.20-0.41, P 0.0001; and HR: 0.55, 95%CI: 0.36-0.88, P = 0.005 in the training, validation, and test datasets, respectively). Patients with model score ≤ 0.5 had better OS (38.8 months vs 20.9 months, HR: 0.58, 95%CI: 0.37-0.90, P = 0.008). Compared with the radiomics (intra-tumoral and peri-tumoral) and three clinical models, the EfficientNetV2-based model showed better survival prognosis for TACE (P 0.05) in the test dataset.The EfficientNetV2-based model enables assessment of both TTP and OS prognosis of TACE in treatment-naïve, intermediate-stage HCC. Patients with lower scores will benefit from TACE. The model can potentially be used by clinicians to improve decision making regarding TACE treatment choices.

Highlights • Radiomics-based classifier agreed with radiologists for the classification of COVID-19. • Radiomics enables to quantify the CT variation with SARS-CoV-2 positivity & SARS-CoV-2 negativity. • The features identified by multi-classifier can interpret and generalise the CT signs of COVID-19., Purpose To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19. Methods Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists. The latest version of Pyradiomics was used for feature extraction. Four classifiers (linear classifier, k-nearest neighbour, least absolute shrinkage and selection operator [LASSO], and random forest) were used to differentiate SARS-CoV-2 positive and SARS-CoV-2 negative patients. Comparison of the performance of the classifiers and radiologists was evaluated by ROC curve and Kappa score. Results We manually segmented 16,053 CT slices, comprising 32,625 pneumonia lesions, from the CT scans of all patients. Using Pyradiomics, 120 radiomic features were extracted from each image. The key radiomic features screened by different classifiers varied and lead to significant differences in classification accuracy. The LASSO achieved the best performance (sensitivity: 72.2%, specificity: 75.1%, and AUC: 0.81) on the external validation dataset and attained excellent agreement (Kappa score: 0.89) with radiologists (average sensitivity: 75.6%, specificity: 78.2%, and AUC: 0.81). All classifiers indicated that "Original_Firstorder_RootMeanSquared" and "Original_Firstorder_Uniformity" were significant features for this task. Conclusions We identified radiomic features that were significantly associated with the classification of COVID-19 pneumonia using multiple classifiers. The quantifiable interpretation of the differences in features between the two groups extends our understanding of CT imaging characteristics of COVID-19 pneumonia.

Purpose To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020. Methods Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors’ affiliation was manually extracted from each article. The Bibliographic Item Co-Occurrence Matrix Builder and VOSviewer were used to identify the research topics of the included articles and illustrate the future trajectories of studies. Results The study included 2,685 original articles. The number of publications on DL and medical imaging has increased substantially since 2017, accounting for 97.2% of all included articles. We evaluated the rules of the application of 47 DL networks to eight radiological tasks on 11 human organ sites. Neuroradiology, thorax, and abdomen were frequent research subjects, while thyroid was under-represented. Segmentation and classification tasks were the primary purposes. U-Net, ResNet, and VGG were the most frequently used Convolutional neural network-derived networks. GAN-derived networks were widely developed and applied in 2020, and transfer learning was highlighted in the COVID-19 studies. Brain, prostate, and diabetic retinopathy-related studies were mature research topics in the field. Breast- and lung-related studies were in a stage of rapid development. Conclusions This study evaluates the general rules and future trajectories of DL network application in medical image analyses and provides guidance for future studies.

To determine the characteristics of and trends in research in the emerging field of radiomics through bibliometric and hotspot analyses of relevant original articles published between 2013 and 2018.We evaluated 553 original articles concerning radiomics, published in a total of 61 peer-reviewed journals between 2013 and 2018. The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study purpose, software used for feature calculation, funding declarations, author number, first author's affiliation, study origin, and journal name. Qualitative and quantitative analyses were performed for the manually extracted data for identification and visualization of the trends in radiomics research.The annual growth rate in the number of published papers was 177.82% (p0.001). The characteristics and trends of research hotspots in the field of radiomics were clarified and visualized in this study. It was found that the field of radiomics is at a more mature stage for lung, breast, and prostate cancers than for other sites. Radiomics studies primarily focused on radiological characterization (215) and monitoring (182). Logistic regression and LASSO were the two most commonly used techniques for feature selection. Non-clinical researchers without a medical background dominated radiomics studies (70.52%), the vast majority of which only highlighted positive results (97.80%) while downplaying negative findings.The reporting of quantifiable knowledge about the characteristics and trajectories of radiomics can inform researchers about the gaps in the field of radiomics and guide its future direction.