Background Despite its high sensitivity in diagnosing COVID-19 in a screening population, chest CT appearances of COVID 19 pneumonia are thought to be non-specific. Purpose To assess the performanc...

Background COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy. Purpose To establish and ev...

Radiofrequency ablation (RFA) promotes tumor antigen-specific T cell responses and enhances the effect of immunotherapy in preclinical settings. Here we report that the existence of remnant tumor masses due to incomplete RFA (iRFA) is associated with earlier new metastases and poor survival in patients with colorectal cancer liver metastases (CRCLM). Using mouse models, we demonstrate that iRFA promotes tumor progression and hinders the efficacy of anti-PD-1 therapy. Immune analysis reveals that iRFA induces sustained local inflammation with predominant myeloid suppressor cells, which inhibit T cell function in tumors. Mechanistically, tumor cell-derived CCL2 is critical for the accumulation of monocytes and tumor-associated macrophages (TAMs). The crosstalk between TAMs and tumor cells enhances the CCL2 production by tumor cells. Furthermore, we find that administration of a CCR2 antagonist or the loss of CCL2 expression in tumor cells enhances the antitumor activity of PD-1 blockade, providing a salvage alternative for residual tumors after iRFA.

Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796–0·828) to 0·846 (0·815–0·852; p<0·0001) on internal testing and 0·731 (0·712–0·738) to 0·792 (0·780–0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755–0·786) to 0·805 (0·800–0·820; p<0·0001) on internal testing and 0·707 (0·695–0·729) to 0·752 (0·739–0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

The prognosis of colorectal cancer (CRC) is largely dependent on the early detection of hepatic metastases. With the advantages of nonradioactivity and the availability of multiple scanning sequences, the efficacy of magnetic resonance imaging (MRI) in the detection of colorectal liver metastases (CRLM) is not yet clear. We performed this meta-analysis to address this issue. PubMed, Embase, and the Cochrane Library were searched for studies reporting diagnostic performance of MRI for CRLM. Descriptive and quantitative data were extracted. The study quality was evaluated for the identified studies and a random effects model was used to determine the integrated diagnosis estimation. Meta-regression and subgroup analyses were implemented to investigate the potential contributors to heterogeneity. As a result, seventeen studies were included for analysis (from the year 1996 to 2018), comprising 1121 patients with a total of 3279 liver lesions. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.90 (95% confidence intervals (CI): 0.81–0.95), 0.88 (0.80–0.92), and 62.19 (23.71–163.13), respectively. The overall weighted area under the curve was 0.94 (0.92–0.96). Using two or more imaging planes and a quantitative/semiquantitative interpretation method showed higher diagnostic performance, although only the latter demonstrated statistical significance (P < 0.05). Advanced scanning sequences with DWI and liver-specific contrast media tended to increase the sensitivity for CRLM detection. We therefore concluded that contemporary MRI has high sensitivity and specificity for screening CRLM, especially for those with advanced scanning sequences. Using two or more imaging planes and adopting a quantitative/semiquantitative imaging interpretation may further improve diagnosis. However, the MRI results should be interpreted with caution because of substantial heterogeneity among studies.