Additional nodules detected using EOB-MRI in patients with resectable single hepatocellular carcinoma: an implication for active treatment strategy.

BACKGROUND/AIM: Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOBMRI) further enhances the identification of additional hepatic nodules compared with computed tomography (CT) alone; however, the optimal treatment for such additional nodules remains unclear. We investigated the long-term oncological effect of aggressive treatment strategies for additional lesions identified using EOB-MRI in patients with hepatocellular carcinoma (HCC). METHODS: Data from 522 patients diagnosed with solitary HCC using CT between January 2008 and December 2012 were retrospectively reviewed. Propensity score-matched (PSM) analysis was used to compare the oncologic outcomes between patients with solitary HCC and those with additional nodules on EOB-MRI after aggressive treatment (resection or radiofrequency ablation [RFA]). RESULTS: Among the 383 patients included, 59 had additional nodules identified using EOB-MRI. Compared with patients with solitary HCC, those with additional nodules on EOB-MRI had elevated total bilirubin, aspartate transaminase, and alanine transaminase; had a lower platelet count, higher MELD score, and highly associated with liver cirrhosis (P<0.05). Regarding long-term outcomes, 59 patients with solitary HCC and those with additional nodules after PSM were compared. Disease-free survival (DFS) and overall survival (OS) were comparable between the two groups (DFS, 60.4 vs. 44.3 months, P=0.071; OS, 82.8 vs. 84.8 months, P=0.986). CONCLUSION: The aggressive treatment approach, either resection or RFA, for patients with additional nodules identified on EOBMRI was associated with long-term survival comparable with that for solitary HCC. However, further studies are required to confirm these findings.

Suspicious findings observed retrospectively on CT imaging performed before the diagnosis of pancreatic cancer.

Background: Few studies have focused on computed tomography findings before a pancreatic cancer diagnosis. We aimed to investigate the prediagnostic computed tomography findings of patients who had undergone computed tomography within the prediagnostic period of their pancreatic cancer diagnosis. Methods: Between January 2008 and December 2019, 27 patients who underwent contrast-enhanced abdominal or chest computed tomography including the pancreas within 1 year of a pancreatic cancer diagnosis were enrolled in this retrospective study. The prediagnostic computed tomography imaging findings were divided into pancreatic parenchyma and pancreatic duct findings. Results: All patients underwent computed tomography for reasons unrelated to pancreatic cancer. The pancreatic parenchyma and ducts showed normal findings in seven patients and abnormal findings in 20 patients. Hypoattenuating mass-like lesions were detected in nine patients with a median size of 1.2 cm. Six patients had focal pancreatic duct dilatations, and two patients had distal parenchymal atrophy. In three patients, two of these findings were found simultaneously. Taken together, 14 (51.9%) of 27 patients had findings suggestive of pancreatic cancer in prediagnostic computed tomography. Conclusions: In contrast-enhanced computed tomography performed for other purposes, attention should be paid to the presence of a hypoattenuating mass, focal pancreatic duct dilatation, or distal parenchymal atrophy of the pancreas. These features may be clues for an early diagnosis of pancreatic cancer.

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.

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.

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.

Development and validation of a prognostic model for early triage of patients diagnosed with COVID-19.

We developed a tool to guide decision-making for early triage of COVID-19 patients based on a predicted prognosis, using a Korean national cohort of 5,596 patients, and validated the developed tool with an external cohort of 445 patients treated in a single institution. Predictors chosen for our model were older age, male sex, subjective fever, dyspnea, altered consciousness, temperature ≥ 37.5 °C, heart rate ≥ 100 bpm, systolic blood pressure ≥ 160 mmHg, diabetes mellitus, heart disease, chronic kidney disease, cancer, dementia, anemia, leukocytosis, lymphocytopenia, and thrombocytopenia. In the external validation, when age, sex, symptoms, and underlying disease were used as predictors, the AUC used as an evaluation metric for our model's performance was 0.850 in predicting whether a patient will require at least oxygen therapy and 0.833 in predicting whether a patient will need critical care or die from COVID-19. The AUCs improved to 0.871 and 0.864, respectively, when additional information on vital signs and blood test results were also used. In contrast, the protocols currently recommended in Korea showed AUCs less than 0.75. An application for calculating the prognostic score in COVID-19 patients based on the results of this study is presented on our website ( https://nhimc.shinyapps.io/ih-psc/ ), where the results of the validation ongoing in our institution are periodically updated.

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.

An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum.

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81-0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.

Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results.

This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) "Simple" task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) "difficult" task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training-test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained and evaluated using various validation methods in the training set, and tested in the test set, using the area under the curve (AUC) as an evaluation metric. The AUCs in training and testing varied among different training-test set pairs, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between training and testing was 0.039 (±0.032) for the simple task without undersampling and 0.092 (±0.071) for the difficult task with undersampling. In a training-test set pair with the difficult task without undersampling, for example, the AUC was high in training but much lower in testing (0.882 and 0.667, respectively); in another dataset pair with the same task, however, the AUC was low in training but much higher in testing (0.709 and 0.911, respectively). When the AUC discrepancy between training and test, or generalization gap, was large, none of the validation methods helped sufficiently reduce the generalization gap. Our results suggest that machine learning after a single random training-test set split may lead to unreliable results in radiomics studies especially with small sample sizes.

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.

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.

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.

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.

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.

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.

The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume.

Purpose: To examine the effect of lung volume on the size and volume of pulmonary subsolid nodules (SSNs) measured on CT. Materials and Methods: A total of 42 SSNs from 31 patients were included. CT examination was first performed at total lung capacity (TLC), and a section containing the nodule was additionally scanned at tidal volume (TV). The diameter and volume of each SSN, as well as the cross-sectional lung area containing the nodule, were measured. The significance of the changes in measurements between TLC and TV within the same individuals was evaluated. Results: The lung area and the diameter and volume of SSNs decreased significantly at TV by 12.7 cm2, 0.5 mm, and 46.4 mm3 on average, respectively (p < 0.001), compared to those at TLC. Changes in lung area between TV and TLC were positively correlated with the change in SSN diameter (p = 0.027) and volume (p = 0.014). However, after correction (by considering the change in lung area), the changes in SSN diameter (p = 0.124) and volume (p = 0.062) were not significantly different. Conclusion: SSN size and volume can be significantly affected by lung volume during CT scans of the same individuals.

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.

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.