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019 and p < 0.001, respectively). However, the PDE4D32 and PDE4D87 variants were not correlated with recurrent stroke. Multivariate analysis showed that plaque enhancement from VW-MRI (HR 4.52, 95% CI 2.35-8.73, p < 0.001) and the PDE4D83 variant (HR 7.43, 95% CI 1.75-31.87, p = 0.005) were independently correlated with stroke recurrence. Kaplan-Meier curves showed significant differences in stroke recurrence rates between the plaque-enhanced group and the non-enhanced group (p < 0.001) and between the PDE4D83 variant carriers and noncarriers (p = 0.002).

Plaque enhancement on VW-MRI and the presence of the PDE4D83 variant are associated with ischemic stroke recurrence in subjects with symptomatic ICAS.

Plaque enhancement on VW-MRI and the presence of the PDE4D83 variant are associated with ischemic stroke recurrence in subjects with symptomatic ICAS.The clinical and neuroimaging findings of a family with a variant ACTA2 gene (c351C > G), presenting with smooth muscle dysfunction in structures of neural crest derivation, are discussed. The combination of aortic abnormalities, patent ductus arteriosus, congenital mydriasis and distinctive cerebrovascular and brain morphological abnormalities characterise this disorder. Two sisters, heterozygous for the variant, and their mother, a mosaic, are presented. Brain parenchymal changes are detailed for the first time in a non-Arg179His variant. Radiological features of the petrous canal and external carotid are highlighted. We explore the potential underlying biological and embryological mechanisms.

Although C-reactive protein to prealbumin ratio (CPR) can predict the outcomes of several types of cancer surgeries, little is known about the implication of CPR in patients undergoing esophagectomy for esophageal squamous cell carcinoma (ESCC).

Between 2009 and 2018, 682 consecutive ESCC patients who underwent curative esophagectomy were enrolled. The clinicopathological factors and prognoses were compared between the groups stratified by preoperative CPR levels. A logistic regression model was used to determine the risk factors of postoperative pneumonia. Survival curves were constructed using the Kaplan-Meier method and compared using the log-rank test. The Cox proportional hazards model was used to elucidate prognostic factors.

There were more elderly patients, more males, and more advanced clinical T and N categories in the high CPR group than in the low CPR group. Also, the incidence of postoperative pneumonia was significantly higher in the high CPR group than in the low CPR group (32.4% vs. 20.3%, p < 0.01). In multivariate analyses, high CPR was one of the independent predictive factors for postoperative pneumonia (OR, 1.71; 95% CI, 1.15-2.54; p < 0.03). Moreover, high CPR was an independent prognostic factor for overall, cancer-specific, and recurrence-free survivals (HR 1.62; 95% CI 1.18-2.23; p < 0.01, HR 1.57; 95% CI 1.08-2.32; p = 0.02, HR 1.42; 95% CI 1.06-1.90; p = 0.02).

Preoperative CPR was found to be a useful inflammatory and nutritional indicator for predicting the occurrence of pneumonia and prognosis in patients with ESCC undergoing esophagectomy.

Preoperative CPR was found to be a useful inflammatory and nutritional indicator for predicting the occurrence of pneumonia and prognosis in patients with ESCC undergoing esophagectomy.

Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.

This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation cos from chest radiographs. CRT-0105446 solubility dmso • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

• Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed.

We followed the PRISMA guidelines and performed a systematic review of studies of AI in radiology published from 2015 to 2019. Our published protocol was prospectively registered.

Our search yielded 11,083 results. Seven hundred sixty-seven full texts were reviewed, and 535 articles were included. Ninety-eight percent were retrospective cohort studies. The median number of patients included was 460. Most studies involved MRI (37%). Neuroradiology was the most common subspecialty. Eighty-eight percent used supervisedlines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic.

• While there are many papers reporting expert-level results by using deep learning in radiology, most apply only a narrow range of techniques to a narrow selection of use cases. • The literature is dominated by retrospective cohort studies with limited external validation with high potential for bias. • The recent advent of AI extensions to systematic reporting guidelines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic.

This study aims to evaluate the feasibility of imaging breast cancer with glucosamine (GlcN) chemical exchange saturation transfer (CEST) MRI technique to distinguish between tumor and surrounding tissue, compared to the conventional MRI method.

Twelve patients with newly diagnosed breast tumors (median age, 53 years) were recruited in this prospective IRB-approved study, between August 2019 and March 2020. Informed consent was obtained from all patients. All MRI measurements were performed on a 3-T clinical MRI scanner. For CEST imaging, a fat-suppressed 3D RF-spoiled gradient echo sequence with saturation pulse train was applied. CEST signals were quantified in the tumor and in the surrounding tissue based on magnetization transfer ratio asymmetry (MTRasym) and a multi-Gaussian fitting.

GlcN CEST MRI revealed higher signal intensities in the tumor tissue compared to the surrounding breast tissue (MTRasym effect of 8.12 ± 4.09%, N = 12, p = 2.2 E-03) with the incremental increase due to GlcN uptake of clinical setup for breast cancer detection and should be tested as a complementary method to conventional clinical MRI methods.

• GlcN CEST MRI method is demonstrated for its the ability to differentiate between breast tumor lesions and the surrounding tissue, based on the differential accumulation of the GlcN in the tumors. • GlcN CEST imaging may be used to identify metabolic active malignant breast tumors without using a Gd contrast agent. • The GlcN CEST MRI method may be considered for use in a clinical setup for breast cancer detection and should be tested as a complementary method to conventional clinical MRI methods.

To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).

This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split in placement is more accurate at detecting CS PCa. • Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. • Applying deep learning to an auto-fixed VOI radiomics approach can be valuable.

To evaluate the prognostic value of fibrosis for patients with pancreatic adenocarcinoma (PDAC) and preoperatively predict fibrosis using clinicoradiological features. Tumor fibrosis plays an important role in the chemoresistance of PDAC. However, the prognostic value of tumor fibrosis remains contradiction and accurate prediction of tumor fibrosis is required.

The study included 131 patients with PDAC who underwent first-line surgery. The prognostic value of fibrosis and rounded cutoff fibrosis points for median overall survival (OS) and disease-free survival (DFS) were determined using Cox regression and receiver operating characteristic (ROC) analyses. Then the whole cohort was randomly divided into training (n = 88) and validation (n = 43) sets. Binary logistic regression analysis was performed to select independent risk factors for fibrosis in the training set, and a nomogram was constructed. Nomogram performance was assessed using a calibration curve and decision curve analysis (DCA).

Hazard ratioeatic tumor infiltration is useful for preoperatively predicting tumor fibrosis.

• Tumor fibrosis is correlated with poor prognosis in patients with pancreatic adenocarcinoma. • Tumor fibrosis can be categorized according to its association with overall survival and disease-free survival. • A nomogram incorporating carbohydrate antigen 19-9 level, tumor diameter, and peripancreatic tumor infiltration is useful for preoperatively predicting tumor fibrosis.

The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery.

A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method.

In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases.