Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment

Application of artificial intelligence in liver diseases: From diagnosis to treatment

Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden. There were an estimated 2.14 million liver-related deaths in 2017, representing an 11.4% increase since 2012. Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease. As a hot research topic in recent years, the application of artificial intelligence (AI) in different fields has attracted extensive attention, and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases. Machine learning (ML) is the core of AI and the basic way to make a computer intelligent. ML technology has many potential uses in hepatology, ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis. The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly, accurately and scientifically, which is of importance for reducing the incidence and mortality of liver diseases, reducing medical errors, and promoting the development of medicine. This paper reviews the application and prospects of AI in liver diseases, and aims to improve clinicians’ awareness of the importance of AI in the diagnosis and treatment of liver diseases.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Evaluation of pancreatic steatosis prevalence and anthropometric measurements using non-contrast computed tomography

BACKGROUND/AIMS

Pancreatic steatosis (PS) is a subject of current interest and its prevalence has been reported to range from 16.1% to 30.7% using various radiological methods. This study aimed to evaluate PS prevalence with non-contrast computed tomography (CT).

MATERIALS AND METHODS

The non-contrast CT scans taken in 2016 and 2017 in our hospital were retrospectively screened. A total of 637 cases (320 males, 317 females) were included in the study. CT number measurements were performed from three anatomic regions of the pancreas using regions of interest (ROI) of approximately 1 cm2. The cases with a <0.7 ratio of the pancreatic over splenic CT number were accepted as quantitatively steatosis-positive. Anthropometric evaluations were undertaken by determining various parameters defined on CT.

RESULTS

PS was determined visually in 30.6% of the males and 29% of the females, and quantitatively in 32.8% and 30.6%, respectively. A positive agreement was determined between the quantitative and visual evaluations of steatosis (Cohen's kappa coefficient=0.587, p<0.001). Although PS was seen to be mostly diffuse, the tail region of the pancreas was determined to be the area with most steatosis.

CONCLUSION

PS is usually overlooked in radiology practice but it has a clinical presentation with non-insignificant prevalence. Current radiological methods are adequate in the evaluation of PS. The determination of the cut-off values for various criteria on non-contrast CT can provide more objective evaluations.

Early Prediction of Acute Biliary Pancreatitis Using Clinical and Abdominal CT Features

Background Assessment of the biliary origin of acute pancreatitis (AP) is crucial because it affects patient treatment to avoid recurrence. Although CT is systematically performed to determine severity in AP, its usefulness in assessing AP biliary origin has not been evaluated. Purpose To assess abdominal CT features associated with acute biliary pancreatitis (ABP) and to evaluate the predictive value of a combination of CT and clinical data for determining a biliary origin in a first episode of AP. Materials and Methods From December 2014 to May 2019, all consecutive patients who presented with a first episode of AP and with at least 6 months of follow-up were retrospectively reviewed. Evidence of gallstones was mandatory for a clinical diagnosis of ABP. Abdominal CT images were reviewed by two abdominal radiologists. Univariable and multivariable statistical analyses were performed, and a nomogram was constructed on the basis of the combination of clinical and CT features. This nomogram was validated in a further independent internal cohort of patients. Results A total of 271 patients (mean age ± standard deviation, 56 years ± 20; 160 men) were evaluated. Of these, 170 (63%) had ABP. At multivariable analysis, age (odds ratio [OR], 1.06; 95% CI: 1.03, 1.09; P < .001), alanine aminotransferase level (OR, 1.00; 95% CI: 1.00, 1.01; P = .009), gallbladder gallstone (OR, 15.59; 95% CI: 4.61, 68.62; P < .001), choledochal ring sign (OR, 5.73; 95% CI: 2.11, 17.05; P < .001), liver spontaneous attenuation (OR, 1.07; 95% CI: 1.04, 1.11; P < .001), and duodenal thickening (OR, 0.17; 95% CI: 0.03, 0.61; P = .01) were independently associated with ABP. The matching nomogram combining both clinical and CT features displayed an area under the curve of 0.94 (95% CI: 0.91, 0.97) in the study sample (n = 271) and 0.91 (95% CI: 0.84, 0.99) in the validation cohort (n = 51). Conclusion Abdominal CT provided useful features for diagnosis of acute biliary pancreatitis (ABP). Combining CT and clinical features in a nomogram showed good diagnostic performance for early diagnosis of ABP. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chang in this issue.

Dose-response relationship between World Trade Center dust exposure and hepatic steatosis

BACKGROUND

The World Trade Center (WTC) attack exposed thousands of workers to toxic chemicals that have been linked to liver diseases and cancers. This study examined the relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis in the WTC General Responders Cohort (GRC).

METHODS

All low-dose computed tomography (CT) scans of the chest performed on the WTC GRC between September 11, 2001 and December 31, 2018, collected as part of the World Trade Center Health Program, were reviewed. WTC dust exposure was categorized into five groups based on WTC arrival time. CT liver density was estimated using an automated algorithm, statistics-based liver density estimation from imaging. The relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis was examined using univariate and multivariable regression analyses.

RESULTS

Of the 1788 WTC responders, 258 (14.4%) had liver attenuation less than 40 Hounsfield units (HU < 40) on their earliest CT. Median time after September 11, 2001 and the earliest available CT was 11.3 years (interquartile range: 8.0-14.9 years). Prevalence of liver attenuation less than 40 HU was 17.0% for arrivals on September 11, 2001, 16.0% for arrivals on (September 12, 2001 or September 13, 2001), 10.9% for arrivals on September 14-30, 2001, and 9.0% for arrivals on January 10, 2001 or later (p = 0.0015). A statistically significant trend of increasing liver steatosis was observed with earlier arrival times (p < 0.0001). WTC arrival time remained a significant independent factor for decreased liver attenuation after controlling for other covariates.

CONCLUSIONS

Early arrival at the WTC site was significantly associated with increasing hepatic steatosis.

Role of fatty liver in coronavirus disease 2019 patients' disease severity and hospitalization length: a case-control study

Background and purpose

Fatty liver is one of the most common pre-existing illnesses; it can cause liver injury, leading to further complications in coronavirus disease 2019 patients. Our goal is to determine if pre-existing fatty liver is more prevalent in hospitalized COVID-19 patients compared to patients admitted before the SARS-CoV-2 pandemic and determine the disease severity among fatty liver patients.

Experimental approach

This retrospective study involves a case and a control group consisting of 1162 patients; the case group contains hospitalized COVID-19 patients with positive PCR tests and available chest CT-scan; the control group contains patients with available chest CT-scan previous to the COVID-19 pandemic. Patients’ data such as liver Hounsfield unit, hospitalization length, number of affected lobes, and total lungs involvement score were extracted and compared between the patients.

Results

The findings indicate that 37.9% of hospitalized COVID-19 patients have a pre-existing fatty liver, which is significantly higher (P < 0.001) than the prevalence of pre-existing fatty liver in control group patients (9.02%). In comparison to hospitalized non-fatty liver COVID-19 patients, data from hospitalized COVID-19 patients with fatty liver indicate a longer hospitalization length (6.81 ± 4.76 P = 0.02), a higher total lungs involvement score (8.73 ± 5.28 P < 0.001), and an increased number of affected lobes (4.42 ± 1.2 P < 0.001).

Conclusion

The statistical analysis shows fatty liver is significantly more prevalent among COVID-19 against non-COVID-19 patients, and they develop more severe disease and tend to be hospitalized for more extended periods.

Influence of sarcopenia, clinical data, and 2-[18F] FDG PET/CT in outcome prediction of patients with early-stage adenocarcinoma esophageal cancer

PURPOSE

To determine the prognostic value of sarcopenia measurements done on staging 2-[18F] FDG PET/CT together with metabolic activity of the tumor in patients with adenocarcinoma esophagogastric cancer with surgical treatment.

METHODS

Patients with early-stage, surgically treated esophageal adenocarcinoma and available pre-treatment 2-[18F] FDG PET/CT were included. The standard uptake value (SUV) and SUV normalized by lean body mass (SUL) were recorded. Skeletal muscle index (SMI) was measured at the L3 level on the CT component of the PET/CT. Sarcopenia was defined as SMI < 34.4cm²/m² in women and < 45.4cm²/m² in men.

RESULTS

Of the included 145 patients. 30% were sarcopenic at baseline. On the univariable Cox proportional hazards analysis, ECOG, surgical T and N staging, lymphovascular invasion (LVI) positive lymph nodes, and sarcopenia were significant prognostic factors concerning RFS and OS. On multivariable Cox regression analysis, surgical N staging (p = 0.025) and sarcopenia (p = 0.022) remained significant poor prognostic factors for OS and RFS. Combining the clinical parameters with the imaging-derived nutritional evaluation of the patient but not metabolic parameters of the tumor showed improved predictive ability for OS and RFS.

CONCLUSION

Combining the patients' imaging-derived sarcopenic status with standard clinical data, but not metabolic parameters, offered an overall improved prognostic value concerning OS and RFS.

A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography

BACKGROUND

Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre- and postcontrast CT number.

PURPOSE

We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series.

METHODS

The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV).

RESULTS

Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients.

CONCLUSIONS

A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings.

Accuracy of Dual-Energy Computed Tomography Techniques for Fat Quantification in Comparison With Magnetic Resonance Proton Density Fat Fraction and Single-Energy Computed Tomography in an Anthropomorphic Phantom Environment

OBJECTIVE

To investigate in an anthropomorphic phantom study the accuracy of dual-energy computed tomography (DECT) techniques for fat quantification in comparison with magnetic resonance (MR) proton density fat fraction (PDFF) and single-energy computed tomography (SECT), using known fat content as reference standard.

METHODS

Between August 2018 and November 2020, organic material-based cylinders, composed of mixtures of lean and fat tissues mimics, iodine, and iron, were constructed to simulate varying fat content levels (0%, 10%, 15%, 25%, 50%, 75%, and 100%) in a parenchymal organ and were embedded into an anthropomorphic phantom simulating 3 patient sizes (circumference, 91, 126, and 161 cm). The phantom was imaged with multiecho MR, DECT, and SECT. Magnetic resonance PDFF, DECT fat fraction, and computed tomography (CT) numbers (SECT polychromatic and DECT monochromatic data, virtual unenhanced images) were estimated. Performances of MR PDFF and CT techniques to detect differences in fat content were measured using the area under the curve (AUC). Noninferiority of each CT technique relative to MR PDFF was tested using a noninferiority margin of -0.1.

RESULTS

MR PDFF, DECT 140 keV monochromatic data, and fat fraction most closely correlated with known fat content (R2 = 0.98, 0.98, and 0.96, respectively). Unlike SECT and all other DECT techniques, DECT fat fraction was not affected by presence of iodine (mean difference, 0.3%; 95% confidence interval [CI], -0.9% to 1.5%). Dual-energy computed tomography fat fraction showed noninferiority to MR PDFF in detecting differences of 5% in fat content in medium-sized phantoms (ΔAUC, -0.05; 95% CI, -0.08 to -0.01), and 7% in large (ΔAUC, -0.04; 95% CI, -0.0 to 0.00) or extralarge sized phantoms (ΔAUC, -0.02; 95% CI, -0.07 to 0.00).

CONCLUSIONS

Dual-energy computed tomography fat fraction shows linear correlation with true fat content in the range up to 50% fat fraction. Dual-energy computed tomography fat fraction has comparable estimation error and shows noninferiority to MR PDFF in detecting small differences in fat content across different body sizes.

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes

BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. OBJECTIVE. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. METHODS. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. RESULTS. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1-11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture (p = .18-.98) or death (p = .19-.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). CONCLUSION. Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. CLINICAL IMPACT. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.

Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets

The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation.

Ectopic fat in liver and skeletal muscle is associated with shorter overall survival in patients with colorectal liver metastases

BACKGROUND

Myosteatosis has been associated with shorter overall survival in cancer patients. The increase in ectopic fat might not be limited to skeletal muscle only and might also extend to other sites such as the liver, resulting in non-alcoholic fatty liver disease (NAFLD). In this study, we assessed the relationship between myosteatosis and NAFLD and their association with overall survival in patients with colorectal liver metastases undergoing partial hepatectomy.

METHODS

Patients were selected from a prospective cohort of 289 consecutive patients with colorectal liver metastases. All patients with a preoperative computed tomography (CT)-scan and liver biopsy obtained during surgery were included. If available a second pre-operative CT scan was used to calculate changes in body composition over time. Muscle radiation attenuation was defined as the average Hounsfield units on CT of all muscle tissue at the L3 level. Liver biopsies were graded by a liver pathologist using the steatosis, activity, and fibrosis scoring system for NAFLD.

RESULTS

Two-hundred and eighteen patients had an available liver biopsy of which 131 patients had two available pre-operative CT scans with an average time interval of 3.2 months. One-hundred and thirty-five (62%) biopsies were classified as NAFLD. In multivariable Cox-regression analysis, NAFLD [hazard ratio (HR): 1.8, 95%-confidence interval (CI) 1.0-3.0, P = 0.037], increase in myosteatosis (HR 1.8, 95%-CI 1.1-2.9, P = 0.018), and skeletal muscle loss (HR 1.7, 95%-CI 1.0-2.9, P = 0.035) were independently associated with shorter overall survival while high visceral adipose tissue fat content was associated with longer overall survival (HR: 0.7, 95%-CI 0.5-0.9, P = 0.014).

CONCLUSIONS

Ectopic fat content of liver as well as skeletal muscle tissue is independently associated with shorter overall survival in patients with colorectal liver metastases, while increased visceral adipose tissue fat content is associated with longer overall survival.

Current Status of Radiomics and Deep Learning in Liver Imaging

ABSTRACT

Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment

Background

Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment.

Methods

MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted.

Results

Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle.

Conclusions

Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review

Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. Methods: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). Results: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.

Automated assessment of longitudinal biomarker changes at abdominal CT: correlation with subsequent cardiovascular events in an asymptomatic adult screening cohort

BACKGROUND

Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes.

METHODS

Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events.

RESULTS

Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction.

CONCLUSION

Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.

Investigating Dual-Energy CT Post-Contrast Phases for Liver Iron Quantification: A Preliminary Study

Background and Purpose:

Quantification of hepatic virtual iron content (VIC) by using Multidetector Dual Energy Computed Tomography (DECT) has been recently investigated since this technique could offer a good compromise between accuracy and non-invasiveness for liver iron content quantification. The aim of our study is to investigate differences in VIC at different DECT time points (namely baseline and arterial, venous and tardive phases), identifying the most reliable and also exploring the underlying temporal trend of these values.

Materials and Methods:

Eleven patients who underwent DECT examination and were characterized by low liver fat content were included in this retrospective study. By using the Syngo.via Frontier–DE IronVNC tool, regions of interest (ROI) were placed on the VIC images at 3 hepatic levels, both in left and right liver lobes, at each DECT time point. Friedman’s test followed by Bonferroni-adjusted Wilcoxon signed-rank test for post-hoc analysis was performed to assess differences between DECT timepoints. Page’s L test was performed to test the temporal trend of VIC across the 4 examined timepoints.

Results:

For both liver lobes, Friedman’s test followed by Bonferroni-adjusted Wilcoxon signed-rank test revealed that VIC values differed significantly when extracted from ROIs placed at the 4 different timepoints. The Page’s L test for multiple comparison revealed a significant growing trend for VIC, from baseline acquisition to the fourth and last time point post-contrast agent injection.

Conclusions:

The extraction of hepatic VIC in healthy subjects was found to be significantly influenced by the DECT time point chosen for the extrapolation of the VIC values.

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.

Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast

PURPOSE

Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures.

MATERIALS AND METHODS

Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r²) was generated for each comparison.

RESULTS

Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm², 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r² = 0.72), MA (pre = 0.98 × post; r² = 0.92), M-HU (pre = 0.75 × post  + 5.7; r² = 0.75), and V/S (pre = 1.11 × post; r² = 0.94); p < 0.001 for all r² values. There were no significant trends according to patient age or gender that required further correction.

CONCLUSION

Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ^©RSNA, 2021.

Radiomics and deep learning in liver diseases

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives

BACKGROUND

Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce.

OBJECTIVE

This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable.

METHODS

Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded.

RESULTS

In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001).

CONCLUSIONS

Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards.

Utility of Multiparametric CT for Identification of High-Risk NAFLD

OBJECTIVE. The purpose of this study was to evaluate the utility of laboratory and CT metrics in identifying patients with high-risk nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS. Patients with biopsy-proven NAFLD who underwent CT within 1 year of biopsy were included. Histopathologic review was performed by an experienced gastrointestinal pathologist to determine steatosis, inflammation, and fibrosis. The presence of any lobular inflammation and hepatocyte ballooning was categorized as nonalcoholic steatohepatitis (NASH). Patients with NAFLD and advanced fibrosis (stage F3 or higher) were categorized as having high-risk NAFLD. Aspartate transaminase to platelet ratio index and Fibrosis-4 (FIB-4) laboratory scores were calculated. CT metrics included hepatic attenuation, liver segmental volume ratio (LSVR), splenic volume, liver surface nodularity score, and selected texture features. In addition, two readers subjectively assessed the presence of NASH (present or not present) and fibrosis (stages F0-F4). RESULTS. A total of 186 patients with NAFLD (mean age, 49 years; 74 men and 112 women) were included. Of these, 87 (47%) had NASH and 112 (60%) had moderate to severe steatosis. A total of 51 patients were classified as fibrosis stage F0, 42 as F1, 23 as F2, 37 as F3, and 33 as F4. Additionally, 70 (38%) had advanced fibrosis (stage F3 or F4) and were considered to have high-risk NAFLD. FIB-4 score correlated with fibrosis (ROC AUC of 0.75 for identifying high-risk NAFLD). Of the individual CT parameters, LSVR and splenic volume performed best (AUC of 0.69 for both for detecting high-risk NAFLD). Subjective reader assessment performed best among all parameters (AUCs of 0.78 for reader 1 and 0.79 for reader 2 for detecting high-risk NAFLD). FIB-4 and subjective scores were complementary (combined AUC of 0.82 for detecting high-risk NAFLD). For NASH assessment, FIB-4 performed best (AUC of 0.68), whereas the AUCs were less than 0.60 for all individual CT features and subjective assessments. CONCLUSION. FIB-4 and multiple CT findings can identify patients with high-risk NAFLD (advanced fibrosis or cirrhosis). However, the presence of NASH is elusive on CT.

Building Large-Scale Quantitative Imaging Databases with Multi-Scale Deep Reinforcement Learning: Initial Experience with Whole-Body Organ Volumetric Analyses

To explore the feasibility of a fully automated workflow for whole-body volumetric analyses based on deep reinforcement learning (DRL) and to investigate the influence of contrast-phase (CP) and slice thickness (ST) on the calculated organ volume. This retrospective study included 431 multiphasic CT datasets—including three CP and two ST reconstructions for abdominal organs—totaling 10,508 organ volumes (10,344 abdominal organ volumes: liver, spleen, and kidneys, 164 lung volumes). Whole-body organ volumes were determined using multi-scale DRL for 3D anatomical landmark detection and 3D organ segmentation. Total processing time for all volumes and mean calculation time per case were recorded. Repeated measures analyses of variance (ANOVA) were conducted to test for robustness considering CP and ST. The algorithm calculated organ volumes for the liver, spleen, and right and left kidney (mean volumes in milliliter (interquartile range), portal venous CP, 5 mm ST: 1868.6 (1426.9, 2157.8), 350.19 (45.46, 395.26), 186.30 (147.05, 214.99) and 181.91 (143.22, 210.35), respectively), and for the right and left lung (2363.1 (1746.3, 2851.3) and 1950.9 (1335.2, 2414.2)). We found no statistically significant effects of the variable contrast phase or the variable slice thickness on the organ volumes. Mean computational time per case was 10 seconds. The evaluated approach, using state-of-the art DRL, enables a fast processing of substantial amounts irrespective of CP and ST, allowing building up organ-specific volumetric databases. The thus derived volumes may serve as reference for quantitative imaging follow-up.

Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms

OBJECTIVE

Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms.

MATERIALS AND METHODS

International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT. After patients with insufficient clinical data for diagnosis were excluded, the final cohort consisted of 7785 adults (mean age, 57.0 years; 4361 women and 3424 men). Previously validated and fully automated CT-based algorithms for quantifying muscle, visceral and subcutaneous fat, liver fat, and abdominal aortic calcification were applied to this final cohort.

RESULTS

A total of 738 subjects (9.5% of all subjects; mean age, 56.7 years; 372 women and 366 men) met the clinical criteria for metabolic syndrome. Subsequent major cardiovascular events occurred more frequently in the cohort with metabolic syndrome (p < 0.001). Significant differences were observed between the two groups for all CT-based biomarkers (p < 0.001). Univariate L1-level total abdominal fat (area under the ROC curve [AUROC] = 0.909; odds ratio [OR] = 27.2), L3-level skeletal muscle index (AUROC = 0.776; OR = 5.8), and volumetric liver attenuation (AUROC = 0.738; OR = 5.1) performed well when compared with abdominal aortic calcification scoring (AUROC = 0.578; OR = 1.6). An L1-level total abdominal fat threshold of 460.6 cm² was 80.1% sensitive and 85.4% specific for metabolic syndrome. For women, the AUROC was 0.930 when fat and muscle measures were combined.

CONCLUSION

Fully automated quantitative tissue measures of fat, muscle, and liver derived from abdominal CT scans can help identify individuals who are at risk for metabolic syndrome. These visceral measures can be opportunistically applied to CT scans obtained for other clinical indications, and they may ultimately provide a more direct and useful definition of metabolic syndrome.

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Diagnostic Performance of Multitarget Stool DNA and CT Colonography for Noninvasive Colorectal Cancer Screening

BackgroundMultitarget stool DNA (mt-sDNA) screening has increased rapidly since simultaneous approval by the U.S. Food and Drug Administration and Centers for Medicare and Medicaid Services in 2014, whereas CT colonography screening remains underused and is not covered by Centers for Medicare and Medicaid Services.PurposeTo report postapproval clinical experience with mt-sDNA screening for colorectal cancer (CRC) and compare results with CT colonography screening at the same center.Materials and MethodsIn this retrospective cohort study, asymptomatic adults underwent clinical mt-sDNA screening during a 5-year interval (2014-2019). Electronic medical records were searched to verify test results and document subsequent optical colonoscopy and histopathologic findings. A similar analysis was performed for CT colonography screening during a 15-year interval (2004-2019), with consideration of thresholds for positivity of both 6-mm and 10-mm polyp sizes. χ² or two-sample t tests were used for group comparisons.ResultsA total of 3987 asymptomatic adult patients (mean age, 64 years ± 9 [standard deviation]; 2567 women) underwent mt-sDNA screening and 9656 patients (mean age, 57 years ± 8; 5200 women) underwent CT colonography. Test-positive rates for mt-sDNA and for 6-mm- and 10-mm-threshold CT colonography were 15.2%, 16.4%, and 6.7%, respectively. Optical colonoscopy follow-up rates for positive results of mt-sDNA and 6-mm- and 10-mm-threshold CT colonography were 13.1%, 12.3%, and 5.9%, respectively. Positive predictive values (PPVs) for any neoplasm 6 mm or greater, advanced neoplasia, and CRC for mt-sDNA were 54.2%, 22.7%, and 1.9% respectively; for 6-mm-threshold CT colonography, PPVs were 76.8%, 44.3%, and 2.7%; for 10-mm-threshold CT colonography, PPVs were 84.5%, 75.2%, and 5.2%, respectively (P < .001 for mt-sDNA vs CT colonography for all except 6-mm CRC at CT colonography). For mt-sDNA versus 6-mm-threshold CT colonography, overall detection rates for advanced neoplasia were 2.7% and 5.0%, respectively (P < .001); corresponding detection rates for CRC were 0.23% and 0.31%, respectively (P = .43).ConclusionThe detection rates of advanced neoplasia at CT colonography screening were greater than those of multitarget stool DNA. Detection rates were similar for colorectal cancer.© RSNA, 2020See also the editorial by Yee in this issue.

Evaluation of liver tumour response by imaging

The goal of assessing tumour response on imaging is to identify patients who are likely to benefit - or not - from anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickly adopted by the oncology community. At the same time, many interventional oncology treatments were developed to target specific features of liver tumours that result in significant changes in tumours but have little effect on tumour size. New criteria focusing on the viable part of tumours were therefore designed to provide more appropriate feedback to guide patient management. Targeted therapy has resulted in a breakthrough that challenges conventional response criteria due to the non-linear relationship between response and tumour size, requiring the development of methods that emphasize the appearance of tumours. More recently, research into functional and quantitative imaging has created new opportunities in liver imaging. These results have suggested that certain parameters could serve as early predictors of response or could predict later tumour response at baseline. These approaches have now been extended by machine learning and deep learning. This clinical review focuses on the progress made in the evaluation of liver tumours on imaging, discussing the rationale for this approach, addressing challenges and controversies in the field, and suggesting possible future developments.

Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study

Background

Body CT scans are frequently performed for a wide variety of clinical indications, but potentially valuable biometric information typically goes unused. We investigated the prognostic ability of automated CT-based body composition biomarkers derived from previously-developed deep-learning and feature-based algorithms for predicting major cardiovascular events and overall survival in an adult screening cohort, compared with clinical parameters.

Methods

Mature and fully-automated CT-based algorithms with pre-defined metrics for quantifying aortic calcification, muscle density, visceral/subcutaneous fat, liver fat, and bone mineral density (BMD) were applied to a generally-healthy asymptomatic outpatient cohort of 9223 adults (mean age, 57.1 years; 5152 women) undergoing abdominal CT for routine colorectal cancer screening. Longitudinal clinical follow-up (median, 8.8 years; IQR, 5.1-11.6 years) documented subsequent major cardiovascular events or death in 19.7% (n=1831). Predictive ability of CT-based biomarkers was compared against the Framingham Risk Score (FRS) and body mass index (BMI).

Findings

Significant differences were observed for all five automated CT-based body composition measures according to adverse events (p<0.001). Univariate 5-year AUROC (with 95% CI) for automated CT-based aortic calcification, muscle density, visceral/subcutaneous fat ratio, liver density, and vertebral density for predicting death were 0.743(0.705-0.780)/0.721(0.683-0.759)/0.661(0.625-0.697)/0.619 (0.582-0.656)/0.646(0.603-0.688), respectively, compared with 0.499(0.454-0.544) for BMI and 0.688(0.650-0.727) for FRS (p<0.05 for aortic calcification vs. FRS and BMI); all trends were similar for 2-year and 10-year ROC analyses. Univariate hazard ratios (with 95% CIs) for highest-risk quartile versus others for these same CT measures were 4.53(3.82-5.37) /3.58(3.02-4.23)/2.28(1.92-2.71)/1.82(1.52-2.17)/2.73(2.31-3.23), compared with 1.36(1.13-1.64) and 2.82(2.36-3.37) for BMI and FRS, respectively. Similar significant trends were observed for cardiovascular events. Multivariate combinations of CT biomarkers further improved prediction over clinical parameters (p<0.05 for AUROCs). For example, by combining aortic calcification, muscle density, and liver density, the 2-year AUROC for predicting overall survival was 0.811 (0.761-0.860).

Interpretation

Fully-automated quantitative tissue biomarkers derived from CT scans can outperform established clinical parameters for pre-symptomatic risk stratification for future serious adverse events, and add opportunistic value to CT scans performed for other indications.

Computed Tomography Measurements of Hepatic Steatosis in Cholelitihiasis and Cholecystectomy Cases Using Unenhanced Images

INTRODUCTION

Computed tomography (CT) measurements of hepatic steatosis can be performed using unenhanced CT images. The purpose of this study was to assess the occurrence of hepatic steatosis using unenhanced CT images for patients undergoing cholecystectomy or having cholelithiasis.

METHODS

A total of 143 unenhanced CT cases from a single centre were retrospectively examined. The CT number of liver, ratio of CT number of liver to spleen, and CT number of liver minus CT number of spleen were measured in three groups: (1) patients undergoing cholecystectomy, (2) patients having cholelithiasis, and (3) control group. Abdominal circumference, anterior subcutaneous fat tissue thickness, and body mass index were obtained.

RESULTS

Mean CT number of liver was significantly different between the group of patients with cholecystectomy and cholelithiasis and the control group (P < .001) and also between cases of cholecystectomy and cholelithiasis (P = .041), with the lowest CT number of liver in the cholecystectomy group. The mean CT number of liver minus CT number of spleen and mean CT number (liver/spleen) ratios, evaluated separately for both lobes, were not different comparing the cholelithiasis and cholecystectomy groups. The mean CT number of liver minus CT number of spleen and mean CT number (liver/spleen) ratios differed significantly between the control group and both patient groups (P < .001). Positive correlations were identified between abdominal circumference, subcutaneous fat depth, body mass index, and liver size and hepatic steatosis.

CONCLUSION

There was an increased occurrence of hepatic steatosis in patients who have undergone a cholecystectomy compared with patients treated for cholelithiasis and the control group.

Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis

PURPOSE

Patients with recurrent abdominal pain and pancreatic enzyme elevations may be diagnosed clinically with recurrent acute pancreatitis (RAP) even with normal imaging or no imaging at all. Since neither abdominal pain nor enzyme elevations are specific for acute pancreatitis (AP), and patients with RAP often have a normal appearing pancreas on CT after resolution of an AP episode, RAP diagnosis can be challenging. This study aims to determine if quantitative radiomic features of the pancreas on CT can differentiate patients with functional abdominal pain, RAP, and chronic pancreatitis (CP).

METHOD

Contrast enhanced CT abdominal images of adult patients evaluated in a pancreatitis clinic from 2010 to 2018 with the diagnosis of RAP, functional abdominal pain, or CP were retrospectively reviewed. The pancreas was outlined by drawing region of interest (ROI) on images. 54 radiomic features were extracted from each ROI and were compared between the patient groups. A one-vs-one Isomap and Support Vector Machine (IsoSVM) classifier was also trained and tested to classify patients into one of the three diagnostic groups based on their radiomic features.

RESULTS

Among the study's 56 patients, 20 (35.7 %) had RAP, 19 (33.9 %) had functional abdominal pain, and 17 (30.4 %) had CP. On univariate analysis, 11 radiomic features (10 GLCM features and one NGTDM feature) were significantly different between the patient groups. The IsoSVM classifier for prediction of patient diagnosis had an overall accuracy of 82.1 %.

CONCLUSIONS

Certain radiomic features on CT imaging can differentiate patients with functional abdominal pain, RAP, and CP.