Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

OBJECTIVE. The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. MATERIALS AND METHODS. Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index [weight in kilograms divided by the square of height in meters] = 27 ± 5) who underwent routine chest (n = 22; 16 women, six men) and abdominopelvic (n = 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction [AIDR] 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution [FIRST], Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine [AiCE], Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed. RESULTS. Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) (p < 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies (p = 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (p < 0.0001). CONCLUSION. At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.

What Patients Want to Know about Imaging Examinations: A Multiinstitutional U.S. Survey in Adult and Pediatric Teaching Hospitals on Patient Preferences for Receiving Information before Radiologic Examinations.

Purpose To identify what information patients and parents or caregivers found useful before an imaging examination, from whom they preferred to receive information, and how those preferences related to patient-specific variables including demographics and prior radiologic examinations. Materials and Methods A 24-item survey was distributed at three pediatric and three adult hospitals between January and May 2015. The χ2 or Fisher exact test (categorical variables) and one-way analysis of variance or two-sample t test (continuous variables) were used for comparisons. Multivariate logistic regression was used to determine associations between responses and demographics. Results Of 1742 surveys, 1542 (89%) were returned (381 partial, 1161 completed). Mean respondent age was 46.2 years ± 16.8 (standard deviation), with respondents more frequently female (1025 of 1506, 68%) and Caucasian (1132 of 1504, 75%). Overall, 78% (1117 of 1438) reported receiving information about their examination most commonly from the ordering provider (824 of 1292, 64%), who was also the most preferred source (1005 of 1388, 72%). Scheduled magnetic resonance (MR) imaging or nuclear medicine examinations (P < .001 vs other examination types) and increasing education (P = .008) were associated with higher rates of receiving information. Half of respondents (757 of 1452, 52%) sought information themselves. The highest importance scores for pre-examination information (Likert scale ≥4) was most frequently assigned to information on examination preparation and least frequently assigned to whether an alternative radiation-free examination could be used (74% vs 54%; P < .001). Conclusion Delivery of pre-examination information for radiologic examinations is suboptimal, with half of all patients and caregivers seeking information on their own. Ordering providers are the predominant and preferred source of examination-related information, with respondents placing highest importance on information related to examination preparation. © RSNA, 2018 Online supplemental material is available for this article.

Diagnostic Yield of CT-Guided Percutaneous Transthoracic Needle Biopsy for Diagnosis of Anterior Mediastinal Masses.

OBJECTIVE: The purpose of this study was to evaluate the diagnostic yield and accuracy of CT-guided percutaneous biopsy of anterior mediastinal masses and assess prebiopsy characteristics that may help to select patients with the highest diagnostic yield. MATERIALS AND METHODS: Retrospective review of all CT-guided percutaneous biopsies of the anterior mediastinum conducted at our institution from January 2003 through December 2012 was performed to collect data regarding patient demographics, imaging characteristics of biopsied masses, presence of complications, and subsequent surgical intervention or medical treatment (or both). Cytology, core biopsy pathology, and surgical pathology results were recorded. A per-patient analysis was performed using two-tailed t test, Fisher's exact test, and Pearson chi-square test. RESULTS: The study cohort included 52 patients (32 men, 20 women; mean age, 49 years) with mean diameter of mediastinal mass of 6.9 cm. Diagnostic yield of CT-guided percutaneous biopsy was 77% (40/52), highest for thymic neoplasms (100% [11/11]). Non-diagnostic results were seen in 12 of 52 patients (23%), primarily in patients with lymphoma (75% [9/12]). Fine-needle aspiration yielded the correct diagnosis in 31 of 52 patients (60%), and core biopsy had a diagnostic rate of 77% (36/47). None of the core biopsies were discordant with surgical pathology. There was no statistically significant difference between the diagnostic and the nondiagnostic groups in patient age, lesion size, and presence of necrosis. The complication rate was 3.8% (2/52), all small self-resolving pneumothoraces. CONCLUSION: CT-guided percutaneous biopsy is a safe diagnostic procedure with high diagnostic yield (77%) for anterior mediastinal lesions, highest for thymic neoplasms (100%), and can potentially obviate more invasive procedures.

The 10 Pillars of Lung Cancer Screening: Rationale and Logistics of a Lung Cancer Screening Program.

On the basis of the National Lung Screening Trial data released in 2011, the U.S. Preventive Services Task Force made lung cancer screening (LCS) with low-dose computed tomography (CT) a public health recommendation in 2013. The Centers for Medicare and Medicaid Services (CMS) currently reimburse LCS for asymptomatic individuals aged 55-77 years who have a tobacco smoking history of at least 30 pack-years and who are either currently smoking or had quit less than 15 years earlier. Commercial insurers reimburse the cost of LCS for individuals aged 55-80 years with the same smoking history. Effective care for the millions of Americans who qualify for LCS requires an organized step-wise approach. The 10-pillar model reflects the elements required to support a successful LCS program: eligibility, education, examination ordering, image acquisition, image review, communication, referral network, quality improvement, reimbursement, and research frontiers. Examination ordering can be coupled with decision support to ensure that only eligible individuals undergo LCS. Communication of results revolves around the Lung Imaging Reporting and Data System (Lung-RADS) from the American College of Radiology. Lung-RADS is a structured decision-oriented reporting system designed to minimize the rate of false-positive screening examination results. With nodule size and morphology as discriminators, Lung-RADS links nodule management pathways to the variety of nodules present on LCS CT studies. Tracking of patient outcomes is facilitated by a CMS-approved national registry maintained by the American College of Radiology. Online supplemental material is available for this article.

The potential clinical utility of an artificial intelligence model for identification of vertebral compression fractures in chest radiographs.

PURPOSE: To assess the ability of the Annalise Enterprise CXR Triage Trauma artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment. MATERIALS AND METHODS: This retrospective study used a consecutive cohort of 596 chest radiographs from four U.S. hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related ICD-10 diagnostic codes and medication use for the study period and an additional year of follow up. RESULTS: The model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% CI: 0.939 to 0.968), sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity 89.2% (95% CI: 85.4 to 92.3%). Out of the 236 true-positive cases (i.e., correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease modifying medication for osteoporosis. CONCLUSION: The model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity of 89.2% (95% CI: 85.4 to 92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease modifying medications.

Thoracic Imaging Manifestations of Treated Lymphomas: Response Evaluation, Posttherapeutic Sequelae, and Complications.

Lymphoma is the most common hematologic malignancy comprising a diverse group of neoplasms arising from multiple blood cell lineages. Any structure of the thorax may be involved at any stage of disease. Imaging has a central role in the initial staging, response assessment, and surveillance of lymphoma, and updated standardized assessment criteria are available to assist with imaging interpretation and reporting. Radiologists should be aware of the modern approaches to lymphoma treatment, the role of imaging in posttherapeutic surveillance, and manifestations of therapy-related complications.

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.

Importance: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care. Objective: To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax. Design, Setting, and Participants: This diagnostic study was a retrospective standalone performance assessment using a data set of 1000 chest radiographs captured between June 1, 2015, and May 31, 2021. The radiographs were obtained from patients aged at least 18 years at 4 hospitals in the Mass General Brigham hospital network in the United States. Included radiographs were selected using 2 strategies from all chest radiography performed at the hospitals, including inpatient and outpatient. The first strategy identified consecutive radiographs with pneumothorax through a manual review of radiology reports, and the second strategy identified consecutive radiographs with tension pneumothorax using natural language processing. For both strategies, negative radiographs were selected by taking the next negative radiograph acquired from the same radiography machine as each positive radiograph. The final data set was an amalgamation of these processes. Each radiograph was interpreted independently by up to 3 radiologists to establish consensus ground-truth interpretations. Each radiograph was then interpreted by the AI model for the presence of pneumothorax and tension pneumothorax. This study was conducted between July and October 2021, with the primary analysis performed between October and November 2021. Main Outcomes and Measures: The primary end points were the areas under the receiver operating characteristic curves (AUCs) for the detection of pneumothorax and tension pneumothorax. The secondary end points were the sensitivities and specificities for the detection of pneumothorax and tension pneumothorax. Results: The final analysis included radiographs from 985 patients (mean [SD] age, 60.8 [19.0] years; 436 [44.3%] female patients), including 307 patients with nontension pneumothorax, 128 patients with tension pneumothorax, and 550 patients without pneumothorax. The AI model detected any pneumothorax with an AUC of 0.979 (95% CI, 0.970-0.987), sensitivity of 94.3% (95% CI, 92.0%-96.3%), and specificity of 92.0% (95% CI, 89.6%-94.2%) and tension pneumothorax with an AUC of 0.987 (95% CI, 0.980-0.992), sensitivity of 94.5% (95% CI, 90.6%-97.7%), and specificity of 95.3% (95% CI, 93.9%-96.6%). Conclusions and Relevance: These findings suggest that the assessed AI model accurately detected pneumothorax and tension pneumothorax in this chest radiograph data set. The model's use in the clinical workflow could lead to earlier identification and improved care for patients with pneumothorax.

Percutaneous lung biopsy after pneumonectomy: factors for improving success in the care of patients at high risk.

OBJECTIVE: The purpose of this study was to assess the risks and complications of CT-guided needle biopsy of lung nodules in patients with a single lung after pneumonectomy. MATERIALS AND METHODS: A database search for the records of patients who had undergone lung biopsy over a 9-year period revealed that 1771 patients had done so. Fourteen (0.7%) of these patients (11 men, three women; mean age, 63 years; range, 42.4-79.6 years) had undergone pneumonectomy and been referred for biopsy of the contralateral lung. The images and medical records of these patients were reviewed in detail. RESULTS: Lung biopsy was technically successful in 86% (12/14) of cases. All procedures were fine-needle aspiration, and a core biopsy specimen also was obtained in one case. Fifty percent (6/12) of the procedures were performed with local anesthesia alone and 50% with a combination of local anesthesia and conscious sedation. The pneumothorax rate was 25% (3/12). All pneumothoraces were small and asymptomatic, and none required a chest drain. There were no cases of hemoptysis. No other immediate or delayed complications were encountered. Malignancy was found in 83% (10/12) of cases. In one of the other two cases (8%) the result was false-negative, and in the other, the nodules resolved without chemotherapy and were presumed to be inflammatory. CONCLUSION: Percutaneous lung biopsy performed on the single lung in patients who have undergone pneumonectomy is feasible and successful. Lung biopsy in these circumstances should be performed by an experienced radiologist with thoracic surgical backup.

Deep learning in chest radiography: Detection of findings and presence of change.

BACKGROUND: Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs. METHODS AND FINDINGS: We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis. RESULTS: About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities. CONCLUSIONS: DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.

Bedside Chest Radiographs in the Intensive care Setting: Wireless Direct Radiography Compared to Computed Radiography.

OBJECTIVES: To compare image quality, visibility of anatomic landmarks, tubes and lines, and other clinically significant findings on portable (bedside) chest radiographs acquired with wireless direct radiography (DRw) and computed radiography (CR). METHODS: In a prospective IRB-approved and HIPAA-compliant study, portable DRw (DRX-1C mobile retrofit portable wireless direct radiography, CareStream Inc., Rochester, NY) and portable CR (AGFA CR (DXG) version; NIM2103, AGFA Healthcare, Ridgefield Park, NJ) images of the chest were acquired within 24-hours in 80 patients in the intensive care unit (ICU). Image pairs of 75 patients (37% female) with a mean age of 60.7±16 years were independently compared side-by-side by 7 experienced thoracic radiologists using a five-point scale. When tubes and lines were present, the radiologist also compared an edge-enhanced copy of the DRw image to the CR image. RESULTS: Most radiologists found significantly fewer artifacts on DRw images compared to CR images and all readers agreed that when present, these artifacts did not significantly preclude the ability to evaluate anatomic landmarks, tubes and lines, or clinically significant findings. None of the radiologists (0/7) reported superior visibility of anatomic structures on CR images compared to DRw images and some radiologists (3/7) found DRw images significantly better for visibility of anatomic landmarks such as the carina (p=0.01-0.001). Most radiologists (6/7) found DRw images to be better or clearly better than CR images for position of tubes and lines, and edge-enhanced DRw images to be especially helpful for evaluation of central venous catheters and esophageal tubes (p=0.027-0.001). None of the radiologists deemed CR images superior for visibility of clinically significant findings. CONCLUSIONS: Critical care chest radiography with a portable DRw system can provide similar or superior information compared to a CR system regarding clinically significant findings and position of tubes and lines.