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Purpose: The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC)., Method: Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [O-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D., Results: The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms., Conclusions: A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis., Competing Interests: Declaration of Competing Interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Contrast-induced nephropathy (CIN) has been considered to be a cause of renal failure for over 50 years, but careful review of past and recent studies reveals the risks of CIN to be overestimated. Older studies frequently cited the use of high-osmolality contrast media, which have since been replaced by low-osmolality contrast media, which have lower risks for nephropathy. In addition, literature regarding CIN typically describes the incidence following cardiac angiography, whereas the risk of CIN from intravenous injection is much lower. Most of the early published literature also lacked appropriate control groups to compare to those that received iodinated contrast, and thus attributed rises in creatinine to intravenous contrast without considering normal creatinine fluctuations (frequent in patients with kidney disease) and other acute pathologic states such as hypotension or nephrotoxic drug administration. The aim of this paper is to review the literature detailing CIN risk, discuss why CIN risk is often overestimated and how withholding contrast can lead to misdiagnosis and delay in appropriate patient management., (Copyright © 2017 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Purpose: To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.Material and Methods: We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated.Results: Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: DeltaV-A_DWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61-0.80) and 0.66 (95%CI 0.64-0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement.Conclusion: A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians' decision by identifying a subgroup of patients with high HCC risk.Key Points: • In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management. • Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the "washout" pattern appraised visually using EASL and EASL guidelines. • A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome. [ABSTRACT FROM AUTHOR]

Acute abdominal pain is a common cause of ED visits and often requires imaging to identify a specific diagnosis. Prompt and appropriate imaging plays a crucial role in patient management and leads to improved patient outcomes, decreased hospital stay, and improved ED workflow. There are many cases of abdominal pain in the ED with delayed diagnosis and management secondary to a combination of institutional policies and knowledge deficits in current imaging guidelines. Inappropriate use of abdominal radiographs, use of oral contrast for CT abdomen and pelvis, and concern for iodinated contrast-induced acute kidney injury are three of the more commonly encountered roadblocks to prompt imaging diagnosis of abdominal pain. The purpose of this review is to discuss why these potential causes of delayed diagnosis occur and how radiologists can help improve both imaging and ED workflow by utilizing the most up-to-date imaging guidelines such the American College of Radiology (ACR) Appropriateness Criteria and ACR Manual on Contrast Media to assist clinicians working in the emergency setting. • Prompt radiology evaluation of acute nontraumatic abdominal pain leads to improved patient outcomes • Abdominal radiographs are less accurate than CT in diagnosing common causes of acute abdominal pain • Oral contrast does not provide significant diagnostic benefit to CT abdomen and pelvis but takes longer to perform • Contrast-induced acute kidney injury risk is overestimated due to results from outdated research studies • Reviewing up to date imaging and contrast guidelines can help limit delays in ordering the proper imaging study [ABSTRACT FROM AUTHOR]