Salary Equity in Academic Radiology Relative to Other Clinical Specialties

BACKGROUND

Equity in faculty compensation in U.S. academic radiology physicians relative to other specialties is not well known.

OBJECTIVE

The aim of this study is to assess salary equity in U.S. academic radiology physicians at different ranks relative to other clinical specialties.

METHODS

The American Association of Medical Colleges (AAMC) Faculty Salary Survey was used to collect information for full-time faculty at U.S. medical schools. Financial compensation data were collected for 2023 for faculty with MD or equivalent degree in medical specialties, stratified by gender and rank.

RESULTS

The AAMC Faculty Salary Survey data for 2023 included responses for 97,224 faculty members in clinical specialties, with 5847 faculty members in Radiology departments. In radiology, compared to men (n = 3839), the women faculty members (n = 1763) had a lower median faculty compensation by 6% at the rank of Assistant Professor, 3% for Associate Professors, 4% for Professors and 6% for Section Chief positions. Surgery had the highest difference in median compensation with 21%, 24%, 22% and 19% lower faculty compensation, respectively, for women faculty members at corresponding ranks. Pathology had the lowest percent difference (<1%) in median compensation for all professor ranks. Salary inequity in radiology was lower compared to most other specialties. From assistant to full professors, all other clinical specialties except Pathology and Psychiatry, had a greater salary inequity than Radiology.

CONCLUSION

The salary inequity in academic radiology faculty is lower than most other specialties. Further efforts should be made to reduce salary inequities as broader efforts to provide a more diverse, equitable and inclusive environment.

SUMMARY STATEMENT

Salary inequity in academic radiology faculty is lower than most other specialties.

Establishing national clinical diagnostic reference levels and achievable doses for CT examinations in Brazil: A prospective study

PURPOSE

Diagnostic reference levels (DRL) and achievable doses (AD) are important tools for radiation dose optimization. Therefore, a prospective study was performed which aimed to establish a multi-parametric, clinical indication based - DRL(DRLCI) and clinical indication - AD (ADCI) for adult CT in Brazil.

METHODS

The prospective study included 4787 patients (50 ± 18 years old; male:female 2041:2746) at 13 Brazilian sites that have been submitted to head, paranasal sinus, cervical spine, chest, or abdomen-pelvis CT between January and October 2021 for 13 clinical indications. The sites provided the following information: patient age, gender, weight, height, body mass index[BMI], clinical indications, scanner information(vendor, model, detector configuration), scan parameters (number of scan phases, kV, mA, pitch) and dose-related quantities (CT dose index volume- CTDIvol, dose length product- DLP). Median(AD) and 75th(DRL) percentile CTDIvol and DLP values were estimated for each body region and clinical indications. Non-normal data were analyzed with the Kruskal-Wallis test.

RESULTS

In majority of Brazilian sites, body region and clinical indications based DRLs were at or lower than the corresponding DRLs in the US and higher than Europe. Although radiation doses varied significantly for patients in different body mass index groups (p < 0.001), within each body region, there were no differences in radiation doses for different clinical indications (p > 0.1). Radiation doses for 7/13 clinical indications were higher using iterative reconstruction technique than for the filtered back projection.

CONCLUSIONS

There was substantial variation in Brazil DRLCI across different institutions with higher doses compared to the European standards. There was also a lack of clinical indication-based protocol and dose optimization based on different clinical indications for the same body region.

Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs

RATIONALE AND OBJECTIVES

Suboptimal chest radiographs (CXR) can limit interpretation of critical findings. Radiologist-trained AI models were evaluated for differentiating suboptimal(sCXR) and optimal(oCXR) chest radiographs.

MATERIALS AND METHODS

Our IRB-approved study included 3278 CXRs from adult patients (mean age 55 ± 20 years) identified from a retrospective search of CXR in radiology reports from 5 sites. A chest radiologist reviewed all CXRs for the cause of suboptimality. The de-identified CXRs were uploaded into an AI server application for training and testing 5 AI models. The training set consisted of 2202 CXRs (n = 807 oCXR; n = 1395 sCXR) while 1076 CXRs (n = 729 sCXR; n = 347 oCXR) were used for testing. Data were analyzed with the Area under the curve (AUC) for the model's ability to classify oCXR and sCXR correctly.

RESULTS

For the two-class classification into sCXR or oCXR from all sites, for CXR with missing anatomy, AI had sensitivity, specificity, accuracy, and AUC of 78%, 95%, 91%, 0.87(95% CI 0.82-0.92), respectively. AI identified obscured thoracic anatomy with 91% sensitivity, 97% specificity, 95% accuracy, and 0.94 AUC (95% CI 0.90-0.97). Inadequate exposure with 90% sensitivity, 93% specificity, 92% accuracy, and AUC of 0.91 (95% CI 0.88-0.95). The presence of low lung volume was identified with 96% sensitivity, 92% specificity, 93% accuracy, and 0.94 AUC (95% CI 0.92-0.96). The sensitivity, specificity, accuracy, and AUC of AI in identifying patient rotation were 92%, 96%, 95%, and 0.94 (95% CI 0.91-0.98), respectively.

CONCLUSION

The radiologist-trained AI models can accurately classify optimal and suboptimal CXRs. Such AI models at the front end of radiographic equipment can enable radiographers to repeat sCXRs when necessary.

Association of body mass index with 30-day outcomes following groin hernia repair

PURPOSE

Although groin hernia repairs are relatively safe, efforts to identify factors associated with greater morbidity and resource utilization following these operations are warranted. An emphasis on obesity has limited studies from a comprehensive evaluation of the association between body mass index (BMI) and outcomes following groin hernia repair. Thus, we aimed to ascertain the association between BMI class with 30-day outcomes following these operations.

METHODS

The 2014-2020 National Surgical Quality Improvement Program database was queried to identify adults undergoing non-recurrent groin hernia repair. Patient BMI was used to stratify patients into six groups: underweight, normal, overweight, and obesity classes I-III. Association of BMI with major adverse events (MAE), wound complication, and prolonged length of stay (pLOS) as well as 30-day readmission and reoperation were evaluated using multivariable regressions.

RESULTS

Of the 163,373 adults who underwent groin hernia repair, the majority of patients were considered overweight (44.4%). Underweight patients more commonly underwent emergent operations and femoral hernia repair compared to others. After adjustment of intergoup differences, obesity class III was associated with greater odds of an MAE (AOR 1.50), wound complication (AOR 4.30), pLOS (AOR 1.40), and 30-day readmission (AOR 1.50) and reoperation (AOR 1.75, all p < 0.05). Underweight BMI portended greater odds of pLOS and unplanned readmission.

CONCLUSION

Consideration of BMI in patients requiring groin hernia repair could help inform perioperative expectations. Preoperative optimization and deployment of a minimally invasive approach when feasible may further reduce morbidity in patients at the extremes of the BMI spectrum.

Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia

PURPOSE

To assess feasibility of automated segmentation and measurement of tracheal collapsibility for detecting tracheomalacia on inspiratory and expiratory chest CT images.

METHODS

Our study included 123 patients (age 67 ± 11 years; female: male 69:54) who underwent clinically indicated chest CT examinations in both inspiration and expiration phases. A thoracic radiologist measured anteroposterior length of trachea in inspiration and expiration phase image at the level of maximum collapsibility or aortic arch (in absence of luminal change). Separately, another investigator separately processed the inspiratory and expiratory DICOM CT images with Airway Segmentation component of a commercial COPD software (IntelliSpace Portal, Philips Healthcare). Upon segmentation, the software automatically estimated average lumen diameter (in mm) and lumen area (sq.mm) both along the entire length of trachea and at the level of aortic arch. Data were analyzed with independent t-tests and area under the receiver operating characteristic curve (AUC).

RESULTS

Of the 123 patients, 48 patients had tracheomalacia and 75 patients did not. Ratios of inspiration to expiration phases average lumen area and lumen diameter from the length of trachea had the highest AUC of 0.93 (95% CI = 0.88-0.97) for differentiating presence and absence of tracheomalacia. A decrease of ≥25% in average lumen diameter had sensitivity of 82% and specificity of 87% for detecting tracheomalacia. A decrease of ≥40% in the average lumen area had sensitivity and specificity of 86% for detecting tracheomalacia.

CONCLUSION

Automatic segmentation and measurement of tracheal dimension over the entire tracheal length is more accurate than a single-level measurement for detecting tracheomalacia.

Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm

Purpose: Motion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion artifacts on CT pulmonary angiography (CTPA) that have a negative impact on diagnostic interpretation. Methods: With IRB approval and HIPAA compliance, we queried our multicenter radiology report database (mPower, Nuance) for CTPA reports between July 2015 and March 2022 for the following terms: "motion artifacts", "respiratory motion", "technically inadequate", and "suboptimal" or "limited exam". All CTPA reports were from two quaternary (Site A, n = 335; B, n = 259) and a community (C, n = 199) healthcare sites. A thoracic radiologist reviewed CT images of all positive hits for motion artifacts (present or absent) and their severity (no diagnostic effect or major diagnostic impairment). Coronal multiplanar images from 793 CTPA exams were de-identified and exported offline into an AI model building prototype (Cognex Vision Pro, Cognex Corporation) to train an AI model to perform two-class classification ("motion" or "no motion") with data from the three sites (70% training dataset, n = 554; 30% validation dataset, n = 239). Separately, data from Site A and Site C were used for training and validating; testing was performed on the Site B CTPA exams. A five-fold repeated cross-validation was performed to evaluate the model performance with accuracy and receiver operating characteristics analysis (ROC). Results: Among the CTPA images from 793 patients (mean age 63 ± 17 years; 391 males, 402 females), 372 had no motion artifacts, and 421 had substantial motion artifacts. The statistics for the average performance of the AI model after five-fold repeated cross-validation for the two-class classification included 94% sensitivity, 91% specificity, 93% accuracy, and 0.93 area under the ROC curve (AUC: 95% CI 0.89-0.97). Conclusion: The AI model used in this study can successfully identify CTPA exams with diagnostic interpretation limiting motion artifacts in multicenter training and test datasets. Clinical relevance: The AI model used in the study can help alert technologists about the presence of substantial motion artifacts on CTPA, where a repeat image acquisition can help salvage diagnostic information.

Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT

PURPOSE

Knowledge of kidney stone composition can help in patient management; urine composition analysis and dual-energy CT are frequently used to assess stone type. We assessed if threshold-based stone segmentation and radiomics can determine the composition of kidney stones from single-energy, non-contrast abdomen-pelvis CT.

METHODS

With IRB approval, we identified 218 consecutive patients (mean age 64 ± 13 years; male:female 138:80) with the presence of kidney stones on non-contrast, abdomen-pelvis CT and surgical or biochemical proof of their stone composition. CT examinations were performed on one of the seven multidetector-row scanners from four vendors (GE, Philips, Siemens, Toshiba). Deidentified CT images were processed with a radiomics prototype (Frontier, Siemens Healthineers) to segment the entire kidney volumes with an AI-based organ segmentation tool. We applied a threshold of 130 HU to isolate stones in the segmented kidneys and to estimate radiomics over the segmented stone volume. A coinvestigator verified kidney stone segmentation and adjusted the volume of interest to include the entire stone volume when necessary. We applied multiple logistic regression tests with precision recall plots to obtain area under the curve (AUC) using a built-in R statistical program.

RESULTS

The threshold-based stone segmentation successfully isolated kidney stones (uric acid: n = 102 patients, calcium oxalate/phosphate: n = 116 patients) in all patients. Radiomics differentiated between calcium and uric acid stones with an AUC of 0.78 (p < 0.01, 95% CI 0.73-0.83), 0.79 sensitivity, and 0.90 specificity regardless of CT vendors (GE CT: AUC = 0.82, p < 0.01, 95% CI 0.740-0896; Siemens CT: AUC = 0.77, 95% CI 0.700-0.846, p < 0.01).

CONCLUSION

Automated threshold-based stone segmentation and radiomics can differentiate between calcium oxalate/phosphate and urate stones from non-contrast, single-energy abdomen CT.

Complex Relationship Between Artificial Intelligence and CT Radiation Dose

Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.

Effect of technologist and patient attributes on centering for body CT examinations: Influence of cultural and ethnic factors

There are no published data on the effect of patient and technologist gender and ethnicity attributes on off-centering in CT. Therefore, we assessed the impact of patient and technologist variations on off-centering patients undergoing body CT. With institutional review board approval, our retrospective study included 1000 consecutive adult patients (age ranged 22–96 years; 756 males: 244 females) who underwent chest or abdomen CT examinations. We recorded patient (age, gender, nationality, body weight, height,), technologist gender, and scan-related (scanner vendor, body region imaged, scan length, CT dose index volume, dose length product) information. Lateral and anteroposterior (AP) diameters were recorded to calculate effective diameter and size-specific dose estimate (SSDE). Off-centering represented the distance between the anterior-posterior centers of the scan field of view and the patient at the level of carina (for chest CT) and iliac crest (for abdomen CT). About 76% of the patients (760/1000) were off-centered with greater off-centering for chest (22 mm) than for abdomen (15 mm). Although ethnicity or patient gender was not a significant determinant of off-centering, technologist-patient gender mismatch was associated with a significantly greater frequency of off-centering (p<0.001). Off-centering below the gantry isocenter was twice as common as off-centering above the gantry isocenter (p<0.001). The latter occurred more frequently in larger patients and was associated with higher radiation doses than those centered below the isocenter (p<0.001). Technologists’ years of experience and patient factors profoundly affect the presence and extent of off-centering for both chest and abdomen CTs. Larger patients are more often off-centered than smaller patients.

Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

IMPORTANCE

The efficient and accurate interpretation of radiologic images is paramount.

OBJECTIVE

To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH).

MAIN OUTCOMES AND MEASURES

The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding.

RESULTS

A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.

Characterization of Benign and Malignant Pancreatic Lesions with DECT Quantitative Metrics and Radiomics

RATIONALE AND OBJECTIVES

To compare dual energy CT (DECT) quantitative metrics and radiomics for differentiating benign and malignant pancreatic lesions on contrast enhanced abdomen CT.

MATERIALS AND METHODS

Our study included 103 patients who underwent contrast-enhanced DECT for assessing focal pancreatic lesions at one of the two hospitals (Site A: age 68 ± 12 yrs; malignant = 41, benign = 18; Site B: age 46 ± 2 yrs; malignant = 23, benign = 21). All malignant lesions had histologic confirmation, and benign lesions were stable on follow up CT (>12 months) or had characteristic benign features on MRI. Arterial-phase, low- and high-kV DICOM images were processed with the DECT Tumor Analysis (DETA) to obtain DECT quantitative metrics such as HU, iodine and water content from a region of interest (ROI) over focal pancreatic lesions. Separately, we obtained DECT radiomics from the same ROI. Data were analyzed with multiple logistic regression and receiver operating characteristics to generate area under the curve (AUC) for best predictive variables.

RESULTS

DECT quantitative metrics and radiomics had AUCs of 0.98-0.99 at site A and 0.89-0.94 at site B data for classifying benign and malignant pancreatic lesions. There was no significant difference in the AUCs and accuracies of DECT quantitative metrics and radiomics from lesion rims and volumes among patients at both sites (p > 0.05). Supervised learning-based model with data from the two sites demonstrated best AUCs of 0.94 (DECT radiomics) and 0.90 (DECT quantitative metrics) for characterizing pancreatic lesions as benign or malignant.

CONCLUSION

Compared to complex DECT radiomics, quantitative DECT information provide a simpler but accurate method of differentiating benign and malignant pancreatic lesions.

Spectral segmentation and radiomic features predict carotid stenosis and ipsilateral ischemic burden from DECT angiography

PURPOSE The purpose of this study is to compare spectral segmentation, spectral radiomic, and single- energy radiomic features in the assessment of internal and common carotid artery (ICA/CCA) stenosis and prediction of surgical outcome. METHODS Our ethical committee-approved, Health Insurance Portability and Accountability Act (HIPAA)- compliant study included 85 patients (mean age, 73 ± 10 years; male : female, 56 : 29) who under- went contrast-enhanced, dual-source dual-energy CT angiography (DECTA) (Siemens Definition Flash) of the neck for assessing ICA/CCA stenosis. Patients with a prior surgical or interventional treatment of carotid stenosis were excluded. Two radiologists graded the severity of carotid ste- nosis on DECTA images as mild (<50% luminal narrowing), moderate (50%-69%), and severe (>70%) stenosis. Thin-section, low- and high-kV DICOM images from the arterial phase acquisi- tion were processed with a dual-energy CT prototype (DTA, eXamine, Siemens Healthineers) to generate spectral segmentation and radiomic features over regions of interest along the entire length (volume) and separately at a single-section with maximum stenosis. Multiple logistic regressions and area under the receiver operating characteristic curve (AUC) were used for data analysis. RESULTS Among 85 patients, 22 ICA/CCAs had normal luminal dimensions and 148 ICA/CCAs had luminal stenosis (mild stenosis: 51, moderate: 38, severe: 59). For differentiating non-severe and severe ICA/CCA stenosis, radiomic features (volume: AUC=0.94, 95% CI 0.88-0.96; section: AUC=0.92, 95% CI 0.86-0.93) were significantly better than spectral segmentation features (volume: AUC = 0.86, 95% CI 0.74-0.87; section: AUC = 0.68, 95% CI 0.66-0.78) (P < .001). Spectral radiomic features predicted revascularization procedure (AUC = 0.77) and the presence of ipsilateral intra- cranial ischemic changes (AUC = 0.76). CONCLUSION Spectral segmentation and radiomic features from DECTA can differentiate patients with differ- ent luminal ICA/CCA stenosis grades.

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

RATIONALE AND OBJECTIVES

To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.

MATERIALS AND METHODS

We audited publicly available details of regulated AI/ML algorithms in imaging from 2008 until April 2021. We reviewed 127 regulated software (118 AI/ML) to classify information related to their parent company, subspecialty, body area and specific anatomy type, imaging modality, date of FDA clearance, indications for use, target pathology (such as trauma) and findings (such as fracture), technique (CAD triage, CAD detection and/or characterization, CAD acquisition or improvement, and image processing/quantification), product performance, presence, type, strength and availability of clinical validation data. Pertaining to validation data, where available, we recorded the number of patients or studies included, sensitivity, specificity, accuracy, and/or receiver operating characteristic area under the curve, along with information on ground-truthing of use-cases. Data were analyzed with pivot tables and charts for descriptive statistics and trends.

RESULTS

We noted an increasing number of FDA-regulated AI/ML from 2008 to 2021. Seventeen (17/118) regulated AI/ML algorithms posted no validation claims or data. Just 9/118 reviewed AI/ML algorithms had a validation dataset sizes of over 1000 patients. The most common type of AI/ML included image processing/quantification (IPQ; n = 59/118), and triage (CADt; n = 27/118). Brain, breast, and lungs dominated the targeted body regions of interest.

CONCLUSION

Insufficient public information on validation datasets in several FDA-regulated AI/ML algorithms makes it difficult to justify clinical applications since their generalizability and presence of bias cannot be inferred.

Diagnostic Accuracy of Magnetic Resonance Cholangiopancreatography to Detect Benign and Malignant Biliary Strictures

Background:

Magnetic resonance cholangiopancreatography (MRCP) is a noninvasive method to detect pancreaticobiliary strictures. In this study, we aimed to evaluate the diagnostic performance of MRCP and detect sensitive and specific radiologic features in distinguishing malignant and benign pathologies.

Materials and Methods:

In this study, 50 patients with biliary obstruction and a confirmed diagnosis using histopathology were included. The pathologies were evaluated using MRCP which were categorized into malignant and benign strictures. The etiology of strictures was detected using histopathology and endoscopic retrograde cholangiopancreatography. The diagnostic performance of MRCP was calculated using SPSS software. P < 0.05 was considered statistically significant.

Results:

Of 50 patients, 23 patients (46%) had malignant strictures based on MRCP and histopathology. The sensitivity and specificity of MRCP to detect malignancy were 95.7% and 96.3%, respectively. The most sensitive MRCP features to detect malignancy were upstream biliary duct dilation, abrupt tapering, and the presence of a solid mass with sensitivity 100%, 95.7%, and 78.2%, respectively. The malignancy rate was significantly higher in the strictures with length >11.5 mm or wall thickness >2.75 mm (P < 0.05).

Conclusion:

MRCP is a sensitive method to differentiate malignant lesions from benign pathologies. A long and thick stricture with the presence of a solid mass, upstream biliary duct dilation, and abrupt tapering is highly suggestive of malignancy.

CHEST CT USAGE IN COVID-19 PNEUMONIA: MULTICENTER STUDY ON RADIATION DOSES AND DIAGNOSTIC QUALITY IN BRAZIL

We assessed variations in chest CT usage, radiation dose and image quality in COVID-19 pneumonia. Our study included all chest CT exams performed in 533 patients from 6 healthcare sites from Brazil. We recorded patients' age, gender and body weight and the information number of CT exams per patient, scan parameters and radiation doses (volume CT dose index-CTDIvol and dose length product-DLP). Six radiologists assessed all chest CT exams for the type of pulmonary findings and classified CT appearance of COVID-19 pneumonia as typical, indeterminate, atypical or negative. In addition, each CT was assessed for diagnostic quality (optimal or suboptimal) and presence of artefacts. Artefacts were frequent (367/841), often related to respiratory motion (344/367 chest CT exams with artefacts) and resulted in suboptimal evaluation in mid-to-lower lungs (176/344) or the entire lung (31/344). There were substantial differences in CT usage, patient weight, CTDIvol and DLP across the participating sites.

An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

IMPORTANCE

Most early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs.

OBJECTIVE

To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included images were selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control.

EXPOSURES

All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period.

MAIN OUTCOMES AND MEASURES

Each test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzed metrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC).

RESULTS

Images from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4% (95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6% (95% CI, -1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, -2% to 9%) as compared with junior radiologists (4%; 95% CI, -3% to 5%).

CONCLUSIONS AND RELEVANCE

In this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience.

Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT

RATIONALE AND OBJECTIVES

To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease severity in patients with emphysema.

METHODS

Our IRB approved HIPAA-compliant study included 113 adults (71±8 years; 47 females, 66 males) who had both non-contrast chest CT and pulmonary function tests performed within a span of 2 months. The disease severity was classified based on the forced expiratory volume in 1 second (FEV1 as % of predicted) into mild, moderate, and severe. 2 thoracic radiologists (RA), blinded to the clinical and AI results, graded severity of emphysema on a 5-point scale suggested by the Fleischner Society for each lobe. The whole lung scores were derived from the summation of lobar scores. Thin-section CT images were processed with the AI-Rad Companion Chest prototype (Siemens Healthineers) to quantify low attenuation areas (LAA < - 950 HU) in whole lung and each lobe separately. Bronchial abnormality was assessed by both radiologists and a fully automated software (Philips Healthcare).

RESULTS

Both AI (AUC of 0.77; 95% CI: 0.68 - 0.85) and RA (AUC: 0.76, 95% CI: 0.65 - 0.84) emphysema quantification could differentiate mild, moderate, and severe disease based on FEV1. There was a strong positive correlation between AI and RA (r = 0.72 - 0.80; p <0.001). The combination of emphysema and bronchial abnormality quantification from radiologists' and AI assessment could differentiate between different severities with AUC of 0.80 - 0.82 and 0.87, respectively.

CONCLUSION

The assessed AI-prototypes can predict the disease severity in patients with emphysema with the same predictive value as the radiologists.

PRACTICAL CHALLENGES WITH IMAGING COVID-19 IN BRAZIL: MITIGATION IN AND BEYOND THE PANDEMIC

Computed tomography (CT) provides useful information in patients with known or suspected COVID-19 infection. However, there are substantial variations and challenges in scanner technologies and scan practices that have negative effect on the image quality and can increase radiation dose associated with CT.

OBJECTIVE

In this article, we present major issues and challenges with use of CT at five Brazilian CT facilities for imaging patients with known or suspected COVID-19 infection and offer specific mitigating strategies.

METHODS

Observational, retrospective and prospective study of five CT facilities from different states and regions of Brazil, with approval of research and ethics committees.

RESULTS

The most important issues include frequent use of CT, lack of up-to-date and efficient scanner technologies, over-scanning and patient off-centring. Mitigating strategies can include updating scanner technology and improving scan practices.

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia

Purpose

Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Methods

The multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >−200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis.

Results

Moderate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission.

Conclusion

DL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes.

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records

PURPOSE

As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction.

METHOD

We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction.

RESULTS

For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort.

CONCLUSION

The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Purpose

To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi.

Methods

The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output.

Results

Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85–0.92). Higher-order radiomics (gray-level size zone matrix – GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89–0.92).

Conclusion

Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.

Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues

Purpose

Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses.

Methods

With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDI_vol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA).

Results

The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11–40% relative to those with arms up) had greater mis-centering and higher CTDI_vol and DLP at 2/4 facilities (p = 0.027–0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDI_vol and DLP across the four facilities (p < 0.001).

Conclusions

Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses.

Pulmonary artery obstruction index, pulmonary artery diameter and right ventricle strain as prognostic CT findings in patient with acute pulmonary embolism

OBJECTIVE

This study was designed to determine predictors of pulmonary hypertension and signs of right heart dysfunction caused by pulmonary embolism (PE) that may lead to early detection of high-risk patients. So the predictive value of pulmonary artery obstruction index (PAOI), measured by pulmonary CT angiography (PCTA) in the acute setting, in predicting the patients susceptible to PE cardiac complications was evaluated. Also two other PCTA indices, pulmonary artery diameter (PAD), and right ventricle (RV) strain, in these patients were investigated and their predictive value for cardiac complications on follow up echocardiography were demonstrated.

MATERIALS AND METHODS

In the study 120 patients with a definite diagnosis of PE were included. The PAOI, PAD and RV strain were measured using PCTA at the time of the initial diagnosis. Transthoracic echocardiography was done 6 months after the diagnosis of PE and RV echocardiographic indices were measured. Pearson correlation was used to investigate correlation between PAOI, PAD, RV strain and signs of right heart dysfunction.

RESULTS

PAOI was strongly correlated with systolic pulmonary artery pressure (SPAP) (r=0.83), RV systolic pressure (r=0.78) and RV wall thickness (r=0.61) in long-term follow up echocardiography. A higher rate of RV dysfunction and RV dilation was detected among the patients with higher PAOI (P<0.001). PAOI≥18 was strongly predictive for development of RV dysfunction. Also developments of pulmonary hypertension, RV systolic hypertension, RV dilation, RV dysfunction, and RV hypertrophy were significantly more common among patients with higher PAD and RV strain (P<0.001).

CONCLUSIONS

PAOI, PAD and RV strain are sensitive and specific PCTA indices that can predict the development of long-term complications such as pulmonary hypertension and right heart dysfunction, at the time of initial PE diagnosis.

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79-0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients' age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90-0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87-0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

A review of glioblastoma tumors with primitive neuronal component and a case report of a patient with this uncommon tumor at a rare location

Glioblastoma with primitive neuronal component should be considered as a differential diagnosis of infratentorial tumors.

Interobserver and intraobserver variability in magnetic resonance imaging evaluation of patients with suspected disc herniation

OBJECTIVE

Magnetic resonance imaging (MRI) is usually the modality of choice to assess sciatica and intervertebral disc herniation. Despite remarkable progression in diagnostic imaging and surgical techniques, definite diagnosis based on imaging interpretation is still a great challenge. The aim of this study was to determine interobserver and intraobserver variability in reporting lumbar MRI between two neuroradiologists based on the new 2014 version of disc nomenclature.

PATIENTS AND METHODS

The study population was composed of 134 irresponsive to conservative therapy patients with clinical presentations of disc herniation and lumbar radiculopathy. MRI was taken from all the participants using a 1.5 T MRI system. Two neuroradiologists evaluated the images, separately and one of them did it twice and interpreted the scans in sagittal and axial planes. Disc bulge, disc herniation and nerve root compression were evaluated at each level. Interobserver and interaobserver agreements between two neuroradiologists, and one neuroradiologist in two times of reporting were calculated for the evaluation of bulging and herniated discs and nerve root compression by applying the Kappa statistics.

RESULTS

Bulging disc, herniated disc, the type of disc, location of the discs, and nerve root compression diagnosis were significantly in excellent agreement (kappa>0.7, p-value<0.001) through intraobserver assessments, while interobserver assessments presented statistically significant with a fair agreement (kappa:0.4-0.7 and p-value<0.05).

CONCLUSION

Remarkable intraobserver agreement was found between diagnoses of disc-related pathologies of the lumbar spine while interobserver assessments revealed only fair concordance.

Associations between bone mineral density, trabecular bone score, and body mass index in postmenopausal females

Objectives

Bone mineral density (BMD), as a gold standard determinant of osteoporosis, assesses only one of many characteristics contributing to the bone. Trabecular bone score (TBS) is applied to evaluate the microarchitecture of trabecular bone. A high body mass index (BMI) has been reported to have a positive correlation with BMD. However, the relation between BMI and TBS has remained unclear. Therefore, the aim of this study is to shed light on the associations between BMI, T-score, and TBS in postmenopausal women without a diagnosed underlying disease.

Methods

In this cross-sectional study, 1054 postmenopausal women were randomly recruited from the Department of Radiology, Isfahan University of Medical Sciences. Demographic characteristics and medical history of all subjects were collected from documents. TBS measurements for L1-L4 vertebrae were retrospectively performed by the TBS iNsight software using the dual X-ray absorptiometry (DXA) from the same region of spine of the subjects. The analysis was done to detect the correlation between TBS and BMI.

Results

A statistically significant negative correlation was found between TBS and BMI in patients with osteoporosis and low bone mass. In patients with normal T-scores, BMI was not significantly correlated to TBS (P > 0.05). Furthermore, there was a significant positive association between T-score and BMI.

Conclusions

Although a higher BMI had a protective effect against osteoporosis, higher BMI was associated with a lower TBS in patients with an abnormal T-score. However, BMI did not have a significant effect on TBS in patients with normal T-scores.

CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia

Purpose

To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables.

Materials and Methods

This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed.

Results

Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission.

Conclusion

Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.

The assessment of acute pulmonary embolism severity using CT angiography features

Background

This study was conducted to detect the association between radiologic features of CT pulmonary angiography (CTPA) and pulmonary embolism severity index (PESI).

Methods

A total of 150 patients with a definite diagnosis of PE entered the study. The CTPA feature including obstruction index, pulmonary trunk size, presence of backwash contrast, septal morphology, right ventricular (RV) and left ventricular (LV) dimensions, and RV/LV ratio were examined. The severity of the PE was estimated using PESI. The association between CTPA indices and PESI was measured. Statistical analysis was conducted using the SPSS software. P value < 0.05 was considered as statistically significant.

Results

A positive correlation was detected between the obstruction index and PESI (r = 0.45, P < 0.05). Moreover, PESI was significantly higher in patients with a more dilated pulmonary trunk (r = 0.20, P < 0.05). The backwash contrast and abnormal septal morphology were significantly more common among patients with higher PESI (P < 0.05). However, no significant correlation was detected between RV, LV, RV/LV, and PESI. The most predictor of high-risk PE was dilated pulmonary trunk with an odds ratio of 4.4.

Conclusion

Higher Obstruction index, dilated pulmonary trunk, presence of backwash contrast, and an abnormal septal morphology can be associated with a higher PESI.

Is High Preprocedural Renal Resistive Index Sensitive Enough to Predict Iodine Contrast-Induced Nephropathy in Patients Receiving Intra-Arterial Iodinate Contrast?

PURPOSE

Renal Resistive Index (RRI) is a newly introduced sonographic index in predicting contrast-induced nephropathy (CIN) development. It has been suggested that RRI > 0.69 should be considered as a risk factor for CIN development. The present study aimed to calculate the predictive value of RRI using a cutoff point of 0.69.

METHODS

A total of 90 patients who were a candidate for coronary vessels angiography were enrolled in this study. Color Doppler ultrasonography was performed and RRI was measured. Patients were followed up for 48 hours after contrast media exposure for the CIN development. The diagnosis of CIN was based on a 25% relative rise or 0.5 mg/dL absolute rise in creatinine level. The predictive values of RRI were measured using 0.69 as a cutoff point.

RESULTS

Out of 90 patients, CIN developed in 3 patients and 17 patients had preprocedural RRI > 0.69. Of 3 patients with CIN, 1 had RRI > 0.69. Using 0.69 as the cutoff point, the measured sensitivity and specificity of RRI were 33.3% and 83.9%, respectively.

CONCLUSIONS

RRI > 0.69 is not a sensitive index in predicting the CIN development and cannot be used as an independent factor.

A study on bone mass density using dual energy X-ray absorptiometry: Does high body mass index have protective effect on bone density in obese patients?

Background:

Osteoporosis is known as reduction of bone density, which is diagnosed using dual-energy X-ray absorptiometry. Although some studies have shown high body mass index (BMI) as a protective factor for osteoporosis and fracture risks, some other studies demonstrated obesity as a risk factor for osteoporosis. The aim of this study is to evaluate the relationship between BMI and bone mineral density (BMD) in premenopausal and postmenopausal females. Furthermore, we determined the correlation between BMI and fracture risk in postmenopausal females.

Materials and Methods:

In this study, we evaluated the relationship between the age and BMI with 10-year probability fracture risk (estimated using fracture risk assessment tool) and BMD in the L1–L4 spine and femoral neck. Data were collected from BMD center, Askariye Hospital, Isfahan, Iran, from May 2016 to July 2017.

Results:

The study consisted of 1361 individuals, including 305 premenopausal females and 1056 postmenopausal females. The results showed a statistically significant increase of BMD (P < 0.001) and a decrease of fracture risk (β = −0.158, R² = 0.518) with an increase of BMI in postmenopausal females. Moreover, lumbar spine and femoral neck BMD were significantly higher in individuals with BMI ≥30 than in those with BMI <25 in both premenopausal and postmenopausal females (P < 0.001). In addition, older postmenopausal females indicated significantly lower L1–L4 BMD (r = −0.280, P < 0.05) and femoral neck BMD (r = −0.358, P < 0.05).

Conclusion:

The results showed a positive correlation between BMI and BMD of the spine and femoral neck which did not differ by menopausal status. However, there was a correlation between BMI and fracture risk in postmenopausal females.

Diagnostic Performance of Ultrasonography in Detecting Fatty Liver Disease in Comparison with Fibroscan in People Suspected of Fatty Liver

Background:

Fatty liver disease is a common hepatic disorder that remains undiagnosed due to the high number of asymptomatic patients and lack of a proper noninvasive diagnostic tool. Liver biopsy, the gold standard of liver steatosis diagnosis, is an invasive method that can be replaced by fibroscan. Fibroscan can detect liver steatosis with high sensitivity and specificity, but it is not accessible around the world. In this study, we compared ultrasonography (US) as a cheap and accessible device with fibroscan method in detecting patients with liver steatosis.

Materials and Methods:

We enrolled 77 patients in this study. US and fibroscan were done in each patient at a single day. Liver steatosis stages were recorded using US and fibroscan. The diagnostic performance of US was calculated, using fibroscan as the reference method.

Results:

The sensitivity and specificity of US in detecting fatty liver disease using fibroscan as a standard method were 73% and 69%, respectively.

Conclusion:

Based on sensitivity and specificity achieved from US, this study suggests that ultrasound is a suitable method for detecting patients with liver steatosis obviating liver biopsy and fibroscan.

Prediction of thyroid nodule malignancy using thyroid imaging reporting and data system (TIRADS) and nodule size

OBJECTIVES

Thyroid imaging reporting and data system (TIRADS) is a combination of ultrasonographic features developed to help physicians in predicting the malignancy risk of thyroid nodules based on sonographic characteristics. Thyroid nodule size is another factor in determining whether a nodule is malignant. The aim of this study was detecting the predictive value of TIRADS and nodule size based on Bethesda classification in prognostication of malignancy.

METHODS

This was a cross-sectional study of 239 patients with thyroid nodules. The patients underwent ultrasonography using TIRADS classification and FNA biopsy based on Bethesda categorization. The results were analyzed using SPSS with the cut off points and predictive values measured.

RESULTS

TIRADS ≥4 could detect malignant nodules with a sensitivity of 91.67% and specificity of 52.8%. An inverse relationship was observed between nodule size and malignancy risk and cutoff point of 12 mm was found for detecting malignant nodules.

CONCLUSIONS

Thyroid nodules with TIRADS 4 and 5 and diameter lower than 12 mm, are highly suspicious for malignancy and should be considered as indications for fine needle aspiration biopsy.

ADVANCES IN KNOWLEDGE

The study suggests TIRADS and thyroid nodule size as sensitive predictors of malignancy.

A case report of a mammary myofibroblastoma in a male and literature review of radiologic and pathologic features of breast myofibroblastoma

We reported a 65-year-old male with a benign mammary myofibroblastoma. This report shows that not all masses of male breast are malignant. To differentiate benign masses from malignant neoplasms, careful preoperative diagnosis should be performed. Preoperative diagnosis of the tumor prevents placing a huge financial and mental burdens on patients.

Patent ductus venosus in an infant with direct hyperbilirubinemia

Patent ductus venosus is caused by a defect in obliteration of ductus venosus after birth. Ductus venosus connects umbilical vein and inferior vena cava during fetal period. Patent ductus venosus is a very rare cause of cholestatic jaundice.

Intraluminal bezoar caused obstruction and pancreatitis: A case report

Acute pancreatitis from bezoar-induced obstruction is rare. We present an uncommon case report of a man with manifestations of Rapunzel syndrome with no known history of mental disorders. Surgical removal of the bezoar through gastrostomy and enterotomy in the absence of a psychiatric undertone will undoubtedly prevent a relapse.

Quantitative diagnosis of osteoporosis using lumbar spine signal intensity in magnetic resonance imaging

OBJECTIVE

Osteoporosis is the most common metabolic bone disease that is not recognized in many elderly people. To determine the cause of low back pain, lumbosacral MRI is done for a large population who may not have gone under dual energy X-ray absorptiometry (DXA). The aim of this study was to predict bone density using lumbar spine signals in lumbosacral MRI in high risk patients for osteoporosis including post-menopausal females and calculate a threshold for a new quantitative MRI-based score to be used in estimation of lumbar spine bone mass density.

METHODS

82 menopaused females, who had undergone DXA before, were selected and MRI was done within 6 months after DXA. 69 healthy females aged 20-29 years who had undergone lumbar MRI were selected as reference group. Results were analyzed and threshold and diagnostic performance of MRI-based score (M-score) on the method of T-score was calculated.

RESULTS

Negative correlation between M-score and T-score was detected. Cut off point of 2.05 was found for M-score with near sensitivity of 90% and specificity of 87% for detecting osteoporotic patients from non-osteoporotic individuals.

CONCLUSION

M-score is a MRI-based method which can identify patients at risk of osteoporosis. Early diagnosis of osteoporosis can reduce morbidity and mortality caused by it.

ADVANCES IN KNOWLEDGE

The research introduced cut of points for M-score as a new MRI quantitative method to be used as an opportunistic technique for detecting osteoporotic patients.

Gender difference in Cisplatin-induced nephrotoxicity in a rat model: greater intensity of damage in male than female

Background

Nephrotoxicity and hepatotoxicity are side effects of Cisplatin (CP) therapy.

Objectives

We investigated the role of gender in CP-induced nephrotoxicity and hepatotoxicity.

Materials and Methods

Low dose of CP (1 mg/kg/day; ip) was administered daily to male and female Wistar rats for 15 consecutive days. Serum creatinine (Cr), blood urea nitrogen (BUN), malondialdehyde (MDA), nitric oxide (NO) metabolite, and magnesium (Mg) levels were determined.

Results

The percentage of weight loss and the serum levels of MDA and nitrite in male and female animals were not statistically different. However, the serum levels of BUN, Cr, Mg, and kidney MDA levels, and kidney weight and damage score were significantly greater in males than in females (P < 0.05).

Conclusions

CP-induced nephrotoxicity is gender related for which the mechanisms should be determined.

Solution structure of 3-oxo-delta5-steroid isomerase

The three-dimensional structure of the enzyme 3-oxo-delta5-steroid isomerase (E.C. 5.3.3.1), a 28-kilodalton symmetrical dimer, was solved by multidimensional heteronuclear magnetic resonance spectroscopy. The two independently folded monomers pack together by means of extensive hydrophobic and electrostatic interactions. Each monomer comprises three alpha helices and a six-strand mixed beta-pleated sheet arranged to form a deep hydrophobic cavity. Catalytically important residues Tyr14 (general acid) and Asp38 (general base) are located near the bottom of the cavity and positioned as expected from mechanistic hypotheses. An unexpected acid group (Asp99) is also located in the active site adjacent to Tyr14, and kinetic and binding studies of the Asp99 to Ala mutant demonstrate that Asp99 contributes to catalysis by stabilizing the intermediate.

Synthesis and biochemical studies of 7 alpha-substituted androsta-1,4-diene-3,17-diones as enzyme-activated irreversible inhibitors of aromatase

Several 7 alpha-thiosubstituted derivatives of androstenedione have demonstrated effective inhibition of aromatase, the cytochrome P450 enzyme complex responsible for the biosynthesis of estrogens. Introduction of an additional double bond in the A ring resulted in 7 alpha-(4'-amino)phenylthioandrosta-1,4-diene-3,17-dione (7 alpha-APTADD), a potent inhibitor that inactivated aromatase by an enzyme-catalyzed process. Additional 7 alpha-thiosubstituted androsta-1,4-diene-3,17-dione derivatives were designed to further examine enzyme-catalyzed inactivation. Two halogenated and one unsubstituted 7 alpha-phenylthioandrosta-1,4-diene-3,17-diones were synthesized via an acid-catalyzed conjugate Michael addition of substituted thiophenols with androsta-1,4,6-triene-3,17-dione. Two 7 alpha-naphthylthioandrosta-1,4-diene-3,17-diones were synthesized via either acid-catalyzed or based-catalyzed conjugate Michael addition of substituted thionaphthols with androsta-1,4,6-triene-3,17-dione. These agents were evaluated for aromatase inhibitory activity in the human placental microsomal preparation. Under initial velocity assay conditions of low product formation, the inhibitors demonstrated potent inhibition of aromatase, with apparent Ki's ranging from 12 to 27 nM. Furthermore, these compounds produced time-dependent, first-order inactivation of aromatase in the presence of NADPH, whereas no aromatase inactivation was observed in the absence of NADPH. This enzyme-activated irreversible inhibition, also referred to as mechanism-based inhibition, can be prevented by the substrate androstenedione. Thus, the apparent Ki values for these inhibitors are consistent with earlier studies on 7 alpha-substituted competitive inhibitors that indicate bulky substituents can be accommodated at the 7 alpha-position.(ABSTRACT TRUNCATED AT 250 WORDS)

Steroidal inhibitors as chemical probes of the active site of aromatase

Androstenedione analogs containing 7 alpha-substituents have proven to be potent inhibitors of aromatase in human placental microsomes, in MCF-7 mammary cell cultures, and in JAr choriocarcinoma cells. Recent investigations have focused on the use of mechanism-based inhibitors, such as 7 alpha-substituted 1,4-androstadienediones, to biochemically probe the active site of aromatase. Inhibition kinetics were determined under initial velocity conditions using purified human placental cytochrome P450arom protein in a reconstituted system. Derivatives of 1,4-androstadiene-3,17-dione and 1,4,6-androstatriene-3,17-dione exhibited high affinity in the purified enzyme system. 7 alpha-(4'-Amino)phenylthio-1,4-androstadiene-3,17-dione, abbreviated 7 alpha-APTADD, demonstrated rapid time-dependent, first-order inactivation of reconstituted aromatase activity only in the presence of NADPH. The apparent Kinact for 7 alpha-APTADD is 11.8 nM, the first-order rate of inactivation is 2.72 x 10(-3) sec-1, and the half-time of inactivation at infinite inhibitor concentration is 4.25 min. The values for the rate constant and half-time of inactivation are similar to those observed in the placental microsomal assay system. Further studies were performed with radioiodinated 7 alpha-(4'-iodo)phenylthio-1,4-androstadienedione, 7 alpha-IPTADD, and the reconstituted aromatase system. Incubations with [125I] 7 alpha-IPTADD were followed by protein precipitation, solvent extraction, and column chromatography. Analysis of the isolated cytochrome P450arom by gel electrophoresis and autoradiography demonstrated the presence of only one radioactive band, which corresponded to the protein staining band for cytochrome P450arom. HPLC radiochromatographic analysis of the isolated cytochrome P450aroM confirmed the presence of only one radioactive peak coeluting with the u.v. peak for cytochrome P450arom. Peptide mapping analysis by reverse-phase HPLC of digested inhibitor-cytochrome P450arom complex demonstrates that the radioactive inhibitor is covalently bound to a lipophilic fragment. In summary, these inhibitors produced enzyme-catalyzed inactivation of reconstituted aromatase activity, and radioiodinated 7 alpha-IP-TADD binds covalently to the cytochrome P450arom.