Association between incidental perirenal fat stranding on CT and metabolic syndrome in otherwise healthy adults.

PURPOSE: To investigate the association between metabolic syndrome and perirenal fat stranding (PRFS), which is defined as linear or curvilinear soft tissue densities in the perirenal fat on computed tomography (CT). MATERIAL AND METHODS: Adults who had abdominal CT for health screening at a single institution between October 2022 and March 2023 were included retrospectively. Two radiologists assessed the extent of PRFS for each CT and graded it as absent, mild/moderate, and severe. Logistic regression analyses were used to investigate the associations between PRFS and metabolic syndrome-related factors, as well as age and gender. RESULTS: Among 701 participants (mean age, 56.8 years ± 9.7; 336 women and 365 men), 87 (12.4%) had mild (n = 80) or moderate (n = 7) PRFS. None had severe PRFS. The presence of PRFS was independently associated with higher body mass index (odds ratio [OR], 2.561 and 9.842 for overweight and obese, respectively; p ≤ 0.001), elevated blood pressure with or without anti-hypertensive medication (OR, 2.232; p = 0.015), anti-diabetic medication (OR, 3.129; p < 0.001), and lipid-lowering medication (OR, 1.919; p = 0.019), older age (OR, 4.545 and 9.109 for 50-59 years and ≥ 60 years, respectively; p ≤ 0.002), and male gender (OR, 10.065; p < 0.001). Sixty three of 87 (72.4%) participants with PRFS had metabolic syndrome, while 265 of 614 (43.2%) participants without PRFS did (p < 0.001). CONCLUSION: Incidental mild or moderate PRFS may be associated with the presence of metabolic syndrome or related disorders in otherwise healthy adults.

Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis.

BACKGROUND AND PURPOSE: To investigate the diagnostic performance of fully automated radiomics-based models for multiclass classification of a single enhancing brain tumor among glioblastoma, central nervous system lymphoma, and metastasis. MATERIALS AND METHODS: The training and test sets were comprised of 538 cases (300 glioblastomas, 73 lymphomas, and 165 metastases) and 169 cases (101 glioblastomas, 29 lymphomas, and 39 metastases), respectively. After fully automated segmentation, radiomic features were extracted. Three conventional machine learning classifiers, including least absolute shrinkage and selection operator (LASSO), adaptive boosting (Adaboost), and support vector machine with the linear kernel (SVC), combined with one of four feature selection methods, including forward sequential feature selection, F score, mutual information, and LASSO, were trained. Additionally, one ensemble classifier based on the three classifiers was used. The diagnostic performance of the optimized models was tested in the test set using the accuracy, F1-macro score, and the area under the receiver operating characteristic curve (AUCROC). RESULTS: The best performance was achieved when the LASSO was used as a feature selection method. In the test set, the best performance was achieved by the ensemble classifier, showing an accuracy of 76.3% (95% CI, 70.0-82.7), a F1-macro score of 0.704, and an AUCROC of 0.878. CONCLUSION: Our fully automated radiomics-based models for multiclass classification might be useful for differential diagnosis of a single enhancing brain tumor with a good diagnostic performance and generalizability.

Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography.

OBJECTIVES: To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner. METHODS: We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better). RESULTS: Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing. CONCLUSIONS: Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing. KEY POINTS: • Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.

Bowel Peristalsis Artifact on Dual-Energy CT: In Vitro Study on the Influence of Different Dual-Energy CT Platforms and Enteric Contrast Agents.

BACKGROUND. The value of dual-energy CT (DECT) for bowel wall assessment is increasingly recognized. Although technical improvements reduce peristalsis artifact in conventional CT, the effects of peristalsis on DECT image reconstructions remain poorly studied. OBJECTIVE. The purpose of this study was to evaluate the influence of different DECT scanners and enteric contrast agents on the severity of bowel peristalsis artifact in vitro. METHODS. To simulate bowel peristalsis, a 3-cm-diameter corrugated hollow tube representing the bowel was oscillated constantly in the z-axis within a larger water-filled cylinder. The bowel was serially filled with air, water, and iodinated or experimental dark contrast material and scanned on four different DECT platforms (spectral detector, rapid peak kilovoltage switching, split filter, and dual source) to reconstruct 120-kVp-like and iodine images. Two readers rated each image reconstruction for artifact severity from 0 (none) to 3 (severe) and recorded the degree to which iodine images depicted bowel wall hyperattenuation on 120-kVp-like images as artifactual. Artifact severity scores were compared by ANOVA with Bonferroni correction. RESULTS. Interrater agreement on artifact scores was excellent (intraclass correlation coefficient, 0.82 [95% CI, 0.79-0.84]). For 120-kVp-like images, mean peristalsis artifact scores were lower (all p < .001) for split-filter (1.47) and dual-source (1.86) scanners than for spectral-detector (2.58) and rapid-kilovoltage-switching (2.74) scanners. Compared with those on 120-kVp images, peristalsis artifacts on iodine images were less severe for spectral-detector (score, 1.03; p < .001) and rapid-kilovoltage-switching (2.09; p < .001) systems but more severe for dual-source (2.77; p < .001) and split-filter (2.62; p < .001) systems. Peristalsis artifact was rated less severe with experimental dark bowel contrast medium (score, 1.79) than with other bowel contrast agents (all p < .001). Iodine images helped identify bowel wall hyperattenuation as artifactual in 94.7% of reviewed cases for spectral-detector and 40.7% of cases for rapid-kilovoltage-switching scanners. CONCLUSION. For spectral-detector and rapid-kilovoltage-switching DECT, iodine images minimize peristalsis artifact, but for dual-source and split-filter DECT, mixed 120-kVp-like images are preferred. Compared with iodinated contrast material and water, experimental dark bowel contrast material reduces peristalsis artifact. CLINICAL IMPACT. Knowledge of the preferred images for reducing peristalsis artifact can lessen the effect of peristalsis on clinical DECT interpretation. Dark enteric contrast agents, when they become clinically available, may further reduce the effects of peristalsis.

Positive Versus Neutral Oral Contrast Material for Detection of Malignant Deposits in Intraabdominal Nonsolid Organs on CT.

BACKGROUND. Data are limited regarding utility of positive oral contrast material for peritoneal tumor detection on CT. OBJECTIVE. The purpose of this article is to compare positive versus neutral oral contrast material for detection of malignant deposits in nonsolid intraabdominal organs on CT. METHODS. This retrospective study included 265 patients (133 men, 132 women; median age, 61 years) who underwent an abdominopelvic CT examination in which the report did not suggest presence of malignant deposits and a subsequent CT examination within 6 months in which the report indicated at least one unequivocal malignant deposit. Examinations used positive (iohexol; n = 100) or neutral (water; n = 165) oral agents. A radiologist reviewed images to assess whether the deposits were visible (despite clinical reports indicating no deposits) on unblinded comparison with the follow-up examinations; identified deposits were assigned to one of seven intraabdominal compartments. The radiologist also assessed adequacy of bowel filling with oral contrast material. Two additional radiologists independently reviewed examinations in blinded fashion for malignant deposits. NPV was assessed of clinical CT reports and blinded retrospective readings for detection of malignant deposits visible on unblinded comparison with follow-up examinations. RESULTS. Unblinded review identified malignant deposits in 58.1% (154/265) of examinations. In per-patient analysis of clinical reports, NPV for malignant deposits was higher for examinations with adequate bowel filling with positive oral contrast material (65.8% [25/38]) than for examinations with inadequate bowel filling with positive oral contrast material (45.2% [28/62], p = .07) or with neutral oral contrast material regardless of bowel filling adequacy (35.2% [58/165], p = .002). In per-compartment analysis of blinded interpretations, NPV was higher for examinations with adequate and inadequate bowel filling with positive oral contrast material than for examinations with neutral oral contrast regardless of bowel filling adequacy (reader 1: 94.7% [234/247] and 92.5% [382/413] vs 88.3% [947/1072], both p = .045; reader 2: 93.1% [228/245] and 91.6% [361/394] vs 85.9% [939/1093], both p = .01). CONCLUSION. CT has suboptimal NPV for malignant deposits in intraabdominal nonsolid organs. Compared with neutral material, positive oral contrast material improves detection, particularly with adequate bowel filling. CLINICAL IMPACT. Optimization of bowel preparation for oncologic CT may help avoid potentially severe clinical consequences of missed malignant deposits.

Prediction of the risk of developing hepatocellular carcinoma in health screening examinees: a Korean cohort study.

BACKGROUND: Almost all Koreans are covered by mandatory national health insurance and are required to undergo health screening at least once every 2 years. We aimed to develop a machine learning model to predict the risk of developing hepatocellular carcinoma (HCC) based on the screening results and insurance claim data. METHODS: The National Health Insurance Service-National Health Screening database was used for this study (NHIS-2020-2-146). Our study cohort consisted of 417,346 health screening examinees between 2004 and 2007 without cancer history, which was split into training and test cohorts by the examination date, before or after 2005. Robust predictors were selected using Cox proportional hazard regression with 1000 different bootstrapped datasets. Random forest and extreme gradient boosting algorithms were used to develop a prediction model for the 9-year risk of HCC development after screening. After optimizing a prediction model via cross validation in the training cohort, the model was validated in the test cohort. RESULTS: Of the total examinees, 0.5% (1799/331,694) and 0.4% (390/85,652) in the training cohort and the test cohort were diagnosed with HCC, respectively. Of the selected predictors, older age, male sex, obesity, abnormal liver function tests, the family history of chronic liver disease, and underlying chronic liver disease, chronic hepatitis virus or human immunodeficiency virus infection, and diabetes mellitus were associated with increased risk, whereas higher income, elevated total cholesterol, and underlying dyslipidemia or schizophrenic/delusional disorders were associated with decreased risk of HCC development (p < 0.001). In the test, our model showed good discrimination and calibration. The C-index, AUC, and Brier skill score were 0.857, 0.873, and 0.078, respectively. CONCLUSIONS: Machine learning-based model could be used to predict the risk of HCC development based on the health screening examination results and claim data.

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma.

PURPOSE: We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations. METHODS: This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes. RESULTS: Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction. CONCLUSIONS: MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant.

PURPOSE: In glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant. METHODS: From December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification). All selected patients had preoperative MRI and isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor amplification, and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss status. Patients were randomly split into training and test sets (7:3 ratio). Enhancing tumor and peritumoral T2-hyperintensity were auto-segmented, and 660 radiomics features were extracted. We built binary relevance (BR) and ensemble classifier chain (ECC) models for multi-label classification and compared their performance. In the classifier chain, we calculated the mean absolute Shapley value of input features. RESULTS: The micro-averaged area under the curves (AUCs) for the test set were 0.804 and 0.842 in BR and ECC models, respectively. IDH mutation status was predicted with the highest AUCs of 0.964 (BR) and 0.967 (ECC). The ECC model showed higher AUCs than the BR model for ATRX (0.822 vs. 0.775) and MGMT promoter methylation (0.761 vs. 0.653) predictions. The mean absolute Shapley values suggested that predicted outcomes from the prior classifiers were important for better subsequent predictions along the classifier chains. CONCLUSION: We built a radiomics-based multiple gene prediction chained model that incorporates mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant and performs better than a simple bundle of binary classifiers using prior classifiers' prediction probability.

Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.

OBJECTIVES: To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging. METHODS: A total of 188 patients with brain metastases (917 lesions) who underwent a brain metastasis MRI protocol including contrast-enhanced 3D BB and 3D GRE were included in the training set. DL models based on 3D U-net were constructed. The models were validated in the test set consisting of 45 patients with brain metastases (203 lesions) and 49 patients without brain metastases. RESULTS: The combined 3D BB and 3D GRE model yielded better performance than the 3D GRE model (sensitivities of 93.1% vs 76.8%, p < 0.001), and this effect was significantly stronger in subgroups with small metastases (p interaction < 0.001). For metastases < 3 mm, ≥ 3 mm and < 10 mm, and ≥ 10 mm, the sensitivities were 82.4%, 93.2%, and 100%, respectively. The combined 3D BB and 3D GRE model showed a false-positive per case of 0.59 in the test set. The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while 3D GRE model showed a lower Dice coefficient of 0.756. CONCLUSIONS: The combined 3D BB and 3D GRE DL model may improve the detection and segmentation performance of brain metastases, especially in detecting small metastases. KEY POINTS: • The combined 3D BB and 3D GRE model yielded better performance for the detection of brain metastases than the 3D GRE model (p < 0.001), with sensitivities of 93.1% and 76.8%, respectively. • The combined 3D BB and 3D GRE model showed a false-positive rate per case of 0.59 in the test set. • The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while the 3D GRE model showed a lower Dice coefficient of 0.756.

Dual-Energy CT Images: Pearls and Pitfalls.

Dual-energy CT (DECT) is a tremendous innovation in CT technology that allows creation of numerous imaging datasets by enabling discrete acquisitions at more than one energy level. The wide range of images generated from a single DECT acquisition provides several benefits such as improved lesion detection and characterization, superior determination of material composition, reduction in the dose of iodine, and more robust quantification. Technological advances and the proliferation of various processing methods have led to the availability of diverse vendor-based DECT approaches, each with a different acquisition and image reconstruction process. The images generated from various DECT scanners differ from those from conventional single-energy CT because of differences in their acquisition techniques, material decomposition methods, image reconstruction algorithms, and postprocessing methods. DECT images such as virtual monochromatic images, material density images, and virtual unenhanced images have different imaging appearances, texture features, and quantitative capabilities. This heterogeneity creates challenges in their routine interpretation and has certain associated pitfalls. Some artifacts such as residual iodine on virtual unenhanced images and an appearance of pseudopneumatosis in a gas-distended bowel loop on material-density iodine images are specific to DECT, while others such as pseudoenhancement seen on virtual monochromatic images are also observed at single-energy CT. Recognizing the potential pitfalls associated with DECT is necessary for appropriate and accurate interpretation of the results of this increasingly important imaging tool. Online supplemental material is available for this article. ©RSNA, 2021.

Recognizing and Minimizing Artifacts at Dual-Energy CT.

Dual-energy CT (DECT) is an exciting innovation in CT technology with profound capabilities to improve diagnosis and add value to patient care. Significant advances in this technology over the past decade have improved our ability to successfully adopt DECT into the clinical routine. To enable effective use of DECT, one must be aware of the pitfalls and artifacts related to this technology. Understanding the underlying technical basis of artifacts and the strategies to mitigate them requires optimization of scan protocols and parameters. The ability of radiologists and technologists to anticipate their occurrence and provide recommendations for proper selection of patients, intravenous and oral contrast media, and scan acquisition parameters is key to obtaining good-quality DECT images. In addition, choosing appropriate reconstruction algorithms such as image kernel, postprocessing parameters, and appropriate display settings is critical for preventing quantitative and qualitative interpretive errors. Therefore, knowledge of the appearances of these artifacts is essential to prevent errors and allows maximization of the potential of DECT. In this review article, the authors aim to provide a comprehensive and practical overview of possible artifacts that may be encountered at DECT across all currently available commercial clinical platforms. They also provide a pictorial overview of the diagnostic pitfalls and outline strategies for mitigating or preventing the occurrence of artifacts, when possible. The broadening scope of DECT applications necessitates up-to-date familiarity with these technologies to realize their full diagnostic potential.

Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer.

PURPOSE: To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC). METHODS: A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC). RESULTS: EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively. CONCLUSIONS: Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC.

Development and validation of a nomogram to predict pulmonary function and the presence of chronic obstructive pulmonary disease in a Korean population.

BACKGROUND: Early suspicion followed by assessing lung function with spirometry could decrease the underdiagnosis of chronic obstructive pulmonary disease (COPD) in primary care. We aimed to develop a nomogram to predict the FEV1/FVC ratio and the presence of COPD. METHODS: We retrospectively reviewed the data of 4241 adult patients who underwent spirometry between 2013 and 2019. By linear regression analysis, variables associated with FEV1/FVC were identified in the training cohort (n = 2969). Using the variables as predictors, a nomogram was created to predict the FEV1/FVC ratio and validated in the test cohort (n = 1272). RESULTS: Older age (ß coefficient [95% CI], - 0.153 [- 0.183, - 0.122]), male sex (- 1.904 [- 2.749, - 1.056]), current or past smoking history (- 3.324 [- 4.200, - 2.453]), and the presence of dyspnea (- 2.453 [- 3.612, - 1.291]) or overweight (0.894 [0.191, 1.598]) were significantly associated with the FEV1/FVC ratio. In the final testing, the developed nomogram showed a mean absolute error of 8.2% between the predicted and actual FEV1/FVC ratios. The overall performance was best when FEV1/FVC < 70% was used as a diagnostic criterion for COPD; the sensitivity, specificity, and balanced accuracy were 82.3%, 68.6%, and 75.5%, respectively. CONCLUSION: The developed nomogram could be used to identify potential patients at risk of COPD who may need further evaluation, especially in the primary care setting where spirometry is not available.

Improved Sensitivity and Reader Confidence in CT Colonography Using Dual-Layer Spectral CT: A Phantom Study.

Background Limited cathartic preparations for CT colonography with fecal tagging can improve patient comfort but may result in nondiagnostic examinations from poorly tagged stool. Dual-energy CT may overcome this limitation by improving the conspicuity of the contrast agent, but more data are needed. Purpose To investigate whether dual-energy CT improves polyp detection in CT colonography compared with conventional CT at different fecal tagging levels in vitro. Materials and Methods In this HIPAA-compliant study, between December 2017 and August 2019, a colon phantom 30 cm in diameter containing 60 polyps of different shapes (spherical, ellipsoid, flat) and size groups (5-9 mm, 11-15 mm) was constructed and serially filled with simulated feces tagged with four different iodine concentrations (1.26, 2.45, 4.88, and 21.00 mg of iodine per milliliter), then it was scanned with dual-energy CT with and without an outer fat ring to simulate large body size (total diameter, 42 cm). Two readers independently reviewed conventional 120-kVp CT and 40-keV monoenergetic dual-energy CT images to record the presence of polyps and confidence (three-point scale.) Generalized estimating equations were used for sensitivity comparisons between conventional CT and dual-energy CT, and a Wilcoxon signed-rank test was used for reader confidence. Results Dual-energy CT had higher overall sensitivity for polyp detection than conventional CT (58.8%; 95% confidence interval [CI]: 49.7%, 67.3%; 564 of 960 polyps vs 42.1%; 95% CI: 32.1%, 52.8%; 404 of 960 polyps; P < .001), including with the fat ring (48% and 31%, P < .001). Reader confidence improved with dual-energy CT compared with conventional images on all tagging levels (P < .001). Interrater agreement was substantial (κ = 0.74; 95% CI: 0.70, 0.77). Conclusion Compared with conventional 120-kVp CT, dual-energy CT improved polyp detection and reader confidence in a dedicated dual-energy CT colonography phantom, especially with suboptimal fecal tagging. © RSNA, 2020.

Estimation of Fractional Extracellular Space at CT for Predicting Chemotherapy Response and Survival in Pancreatic Ductal Adenocarcinoma.

OBJECTIVE. The purpose of this study was to investigate the association between primary pancreatic ductal adenocarcinoma fractional extracellular space (fECS) estimated from pretreatment CT and tumor response to chemotherapy and patient outcome. MATERIALS AND METHODS. A database search identified the records of patients with locally advanced or metastatic pancreatic ductal adenocarcinoma treated with systemic therapies who had undergone pretreatment CT that included both unenhanced and equilibrium phase images. An ROI was placed on the primary tumor and aorta, and the tumor fECS was calculated as follows: (tumor attenuation in the equilibrium phase - tumor attenuation in the unenhanced phase) / (aortic attenuation in the equilibrium phase - aortic attenuation in the unenhanced phase) × (1 - hematocrit). Response to therapy was assessed in subsequent CT examinations according to the Response Evaluation Criteria in Solid Tumors version 1.1. Relevant clinical variables, including carbohydrate antigen 19-9 level, chemotherapy regimen, and survival were recorded. Multivariate analyses were performed to determine the predictors of treatment response and patient survival. RESULTS. The median primary tumor fECS was 0.41 (range, 0.02-0.69). When dichotomized to high (> 0.41) versus low fECS (≤ 0.41) values, a larger proportion of patients with high tumor fECS values achieved disease control after chemotherapy than did those with low tumor fECS values: full cohort, 27 of 30 versus 19 of 30 (p = 0.030); cohort with locally advanced disease, 23 of 24 versus 10 of 15 (p = 0.024). The mean progression-free survival among patients with high primary tumor fECS values was significantly longer than that among those with low fECS values (191 versus 115 days, p = < 0.0001). Primary tumor fECS was an independent predictor of progression-free survival (p = 0.003) in multivariate analysis. CONCLUSION. High primary tumor fECS value estimated from staging CT was associated with chemotherapy response and progression-free survival of patients with advanced pancreatic ductal adenocarcinoma.

Preoperative prediction of futile surgery in patients with radiologically resectable or borderline resectable pancreatic adenocarcinoma.

BACKGROUND AND AIM: The aim of this study is to identify the predictive factors for futile surgery in patients with radiologically resectable or borderline resectable pancreatic cancer and to develop a prediction model. METHODS: This retrospective study included patients who underwent pancreatic surgery for pancreatic cancer between 2006 and 2017. To identify independent risk factors for futile surgery, logistic regression and random forest analyses were performed in the training cohort, based on which a nomogram was established. The predictive accuracy and discriminative ability of the nomogram were validated in the validation cohort. RESULTS: Of 389 patients who underwent pancreatic surgery, the laparotomy was futile in 40 patients (10.3%). In the training cohort, the univariate and multivariate logistic regression analyses revealed that serum carbohydrate antigen 19-9 level of ≥ 150 U/mL (P = 0.003), the presence of suspicious lymph node (P = 0.013), and more extensive peripancreatic tumor infiltration (P < 0.001) were independent predictive factors for futile surgery. The bootstrap-corrected concordance index of the nomogram was high in the training cohort, 0.826 with a 95% confidence interval of 0.745-0.907. This model also showed a good discriminative performance in the validation cohort, with a concordance index of 0.831. CONCLUSIONS: We established and validated a novel nomogram that predicts the risk of futile surgery due to occult distant metastasis in patients with radiologically resectable or borderline resectable pancreatic cancer.

Hepatocellular Carcinoma versus Other Hepatic Malignancy in Cirrhosis: Performance of LI-RADS Version 2018.

Purpose To evaluate the diagnostic accuracy of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 for differentiating between hepatocellular carcinoma (HCC) and other (hepatic) malignancy (OM) in patients with liver cirrhosis. Materials and Methods From 2008 to 2017, 55 patients with untreated OM and liver cirrhosis were eligible for this retrospective case-control study (mean age, 58 years ± 10 [standard deviation] [range, 32-79 years], with 45 men [mean age, 58 years ± 11] and 10 women [mean age, 62 years ± 7]). Control subjects consisted of 165 treatment-naive patients with HCC and liver cirrhosis (mean age, 58 years ± 10 [range, 29-80 years], with 134 men [mean age, 58 years ± 9] and 31 women [mean age, 59 years ± 11]). Two radiologists blinded to the final diagnosis independently determined the presence of LR-M features and major HCC features (non-rim arterial phase hyperenhancement, non-peripheral washout, and enhancing capsule). The diagnostic performances of each feature, the LR-M criteria (probably or definitely malignant, but not specific for HCC), and the LR-5 criteria (definitely HCC) were calculated and compared by using the generalized estimating equation method. Results Individual LR-M features had a sensitivity of 9%-71% and a specificity of 83%-97% for the diagnosis of OM. Major features of HCC had a sensitivity of 62%-83% and a specificity of 69%-89% for the diagnosis of HCC. The LR-M criteria had a sensitivity of 89% (95% confidence interval [CI]: 81%, 97%) for diagnosing OM, with a specificity of 48% (95% CI: 40%, 56%). The LR-5 criteria had a sensitivity of 74% (95% CI: 67%, 81%) for diagnosing HCC, with a specificity of 89% (95% CI: 81%, 97%). The accuracy of the LR-5 criteria was higher than that of the LR-M criteria (78% [95% CI: 72%, 83%] vs 58% [95% CI: 52%, 65%], P <. 001). Conclusion The LR-5 criteria as well as the LR-M criteria can effectively distinguish hepatocellular carcinoma from other hepatic malignancy in patients with liver cirrhosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Furlan in this issue.

Hepatobiliary versus Extracellular MRI Contrast Agents in Hepatocellular Carcinoma Detection: Hepatobiliary Phase Features in Relation to Disease-free Survival.

Background MRI with hepatobiliary contrast material is more sensitive than MRI with extracellular contrast material in the detection of hepatocellular carcinoma (HCC). Purpose To determine whether postsurgical outcomes differ between patients who undergo MRI with hepatobiliary contrast material and those who undergo MRI with extracellular contrast material by analyzing disease-free survival (DFS) rates after curative resection of HCC. Materials and Methods From January 2014 to December 2015, 170 treatment-naïve patients who underwent contrast-enhanced preoperative liver MRI and curative hepatic resection for HCC were retrospectively included and observed until September 2018. DFS rates were compared between the two groups, which were classified based on the type of MRI contrast agent used. The MRI with hepatobiliary contrast material group was further divided into a hypointense nodule-positive group and a hypointense nodule-negative group according to the presence of residual hepatobiliary phase (HBP) hypointense nodules without arterial phase hyperenhancement (APHE) after surgery. DFS rates were calculated by using the Kaplan-Meier method and were compared among the three groups by using a log-rank test. Results Patients were included in either the MRI with extracellular contrast material group (n = 53; mean age, 60 years ± 9 [standard deviation]) or the hepatobiliary contrast material group (n = 117; mean age, 60 years ± 8; 26 patients were in the hypointense nodule-positive group). Over a median follow-up period of 34.1 months, median DFS rates did not differ between the extracellular contrast material group (35.8 months) and the hepatobiliary contrast material group (43.5 months) (P = .46). However, median DFS in the extracellular contrast material group was longer than that in the hypointense nodule-positive group (35.8 months vs 25.8 months, P < .001) and shorter than that in the hypointense nodule-negative group (35.8 months vs 48.6 months, P = .02). Conclusion Patients who undergo preoperative MRI with hepatobiliary contrast material and resection of hepatobiliary phase hypointense nodules without arterial phase hyperenhancement may show better disease-free survival. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Motosugi in this issue.

Problematic lesions in cirrhotic liver mimicking hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a unique malignancy that can be diagnosed and treated based on non-invasive imaging criteria without histological confirmation in cirrhotic patients, which opens the possibility, although rare, of false-positive diagnosis of the tumor. This brief review illustrates benign and non-HCC malignant lesions arising in cirrhotic liver that could have been erroneously diagnosed as HCC based on imaging criteria: focal nodular hyperplasia-like nodules, serum amyloid A-positive nodules, dysplastic nodules, spontaneously regressing lesions, combined hepatocellular-cholangiocarcinoma, cholangiocarcinoma, sarcomatoid carcinoma, lymphoepithelioma-like carcinoma, hepatoblastoma, and metastatic adenocarcinoma. To determine the potential differences in clinical courses and post-treatment outcome of HCC diagnosed by imaging alone and those histologically, we suggest the terms HCCi and HCCp to distinguish between lesions that are diagnosed as HCC based on imaging alone from those diagnosed based on pathological examination, respectively.Key Points • Benign lesions, such as focal nodular hyperplasia-like nodules, serum amyloid A-positive nodules, dysplastic nodules, and spontaneously regressing lesions, may show imaging findings that mislead to the diagnosis of HCC. • Non-hepatocellular malignant lesions, such as sarcomatoid carcinoma, lymphoepithelioma-like carcinoma, hepatoblastoma, and metastatic adenocarcinomas, can be erroneously diagnosed as HCC based on imaging findings alone, even in cirrhotic liver.

A proposal of imaging classification of intrahepatic mass-forming cholangiocarcinoma into ductal and parenchymal types: clinicopathologic significance.

OBJECTIVES: To investigate the clinicopathologic significance of a subclassification of mass-forming intrahepatic cholangiocarcinoma (MF-iCCA) into ductal and parenchymal types based on magnetic resonance imaging (MRI) METHODS: We enrolled 72 consecutive patients, in whom MF-iCCA was diagnosed on preoperative MRI and surgical resection from January 2000 to March 2013. Two readers independently evaluated MRI findings of adjacent bile duct dilation, periductal tumor spread, and presence of diffuse dilatation or abnormality of the intrahepatic bile duct. MF-iCCAs with none of the aforementioned findings were defined as parenchymal type, and those with one or more findings were defined as ductal type. The enhancement pattern in the arterial phase was also evaluated. Clinical and histopathological findings, as well as post-surgical outcomes, were collected from medical records. RESULTS: Parenchymal-type MF-iCCA (21/78, 27%) exhibited significantly lower serum carbohydrate antigen 19-9 (12.8 vs. 173.8 U/mL) and carcinoembryonic antigen (1.7 vs. 4.2 ng/mL), more frequent viral hepatitis (43% vs. 18%), less frequent biliary intraepithelial neoplasia (0% vs. 26%), and less frequent perineural invasion (0% vs. 59%) and lymph node metastasis (7% vs. 46%), compared with the ductal type (57/78, 73%) (p < 0.05 for all). Parenchymal-type MF-iCCA showed more frequent arterial hypervascularity (p = 0.001) and better overall survival (p = 0.030) than the ductal type. CONCLUSION: Subclassification of MF-iCCAs into parenchymal and ductal types may be useful to discriminate clinical and histopathological characteristics and post-surgical outcomes. KEY POINTS: • We propose subclassification of mass-forming intrahepatic cholangiocarcinoma (MF-iCCA) as parenchymal and ductal types, on the basis of magnetic resonance imaging findings of biliary abnormality. • Two types of MF-iCCAs exhibit different clinical and histopathological characteristics and post-surgical outcomes.