A 41-year-old man was admitted to the hospital because of lymphadenopathy, fatigue, and fever, which had worsened during the previous 6 months, with weight loss and a recent onset of cough and dyspnea. He had had multiple sexual partners since a divorce 4 years earlier; there was no recent travel or exposure to ill persons. Evaluation disclosed cervical, axillary, and abdominal lymphadenopathy, hepatosplenomegaly, anemia, and thrombocytopenia. A diagnostic procedure was performed.
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.
To investigate the predictive factors for a non-diagnostic result and the final diagnosis of pulmonary lesions with an initial non-diagnostic result on CT-guided percutaneous transthoracic needle biopsy.All percutaneous transthoracic needle biopsies performed over a 4-year period were retrospectively reviewed. The initial pathological results were classified into three categories-malignant, benign, and non-diagnostic. A non-diagnostic result was defined when no malignant cells were seen and a specific benign diagnosis could not be made. The demographic data of patients, lesions' characteristics, technique, complications, initial pathological results, and final diagnosis were reviewed. Statistical analysis was performed using binary logistic regression.Of 894 biopsies in 861 patients (male:female, 398:463; mean age 67, range 18-92 years), 690 (77.2%) were positive for malignancy, 55 (6.2%) were specific benign, and 149 (16.7%) were non-diagnostic. Of the 149 non-diagnostic biopsies, excluding 27 cases in which the final diagnosis could not be confirmed, 36% revealed malignant lesions and 64% revealed benign lesions. Predictive factors for a non-diagnostic biopsy included the size ≤ 15 mm, needle tract traversing emphysematous lung parenchyma, introducer needle outside the lesion, procedure time > 60 minutes, and presence of alveolar hemorrhage. Non-diagnostic biopsies with a history of malignancy or atypical cells on pathology were more likely to be malignant (p = 0.043 and p = 0.001).The predictive factors for a non-diagnostic biopsy were lesion size ≤ 15 mm, needle tract traversing emphysema, introducer needle outside the lesion, procedure time > 60 minutes, and presence of alveolar hemorrhage. Thirty-six percent of the non-diagnostic biopsies yielded a malignant diagnosis. In cases with a history of malignancy or the presence of atypical cells in the biopsy sample, a repeat biopsy or surgical intervention should be considered.
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.
Interface design is a key element in the efficient use of a picture archiving and communication system (PACS) workstation. In many cases, multiple mouse clicks or keyboard commands are required to open and close a case, to mark it as complete, and to retrieve and allocate screen positions to the next case. We evaluated the work flow effect of software designed for automated image display in which all of these operations are consolidated in a single mouse click.Automated image display increases efficiency in image interpretation and remedies the normally cluttered presentation environment. At our institution, acceptance of automated image display has been overwhelmingly positive. In fact, automated image display has improved radiologist productivity.
Lymphangioleiomyomatosis (LAM) is a prominent finding in the setting of tuberous sclerosis complex (TSC).
Objective:
The present study was designed to compare cystic lung changes consistent with LAM in patients with a TSC1 disease-causing mutation, TSC2 disease-causing mutation, or no mutation identified (NMI).
Methods and results:
We conducted a retrospective review of the chest computed tomography (CT) of 45 female and 20 male patients with TSC and found cysts consistent with LAM in 22 (49%) women and two (10%) men. In the female population, changes consistent with LAM were observed in six of 15 (40%) patients with TSC1, 11 of 23 (48%) with TSC2, and five of seven (71%) with NMI. While the predominant size of cysts did not differ across these three groups, TSC2 women with LAM had a significantly greater number of cysts than did TSC1 patients (p = 0.010).
Conclusions:
These findings suggest a higher rate of LAM in TSC1 than previously recognised, as well as a fundamental difference in CT presentation between TSC1 and TSC2.
Presentation of CaseAn 82-year-old man was admitted to the hospital because of dyspnea and peripheral edema.The patient had a long history of hypertension, diabetes mellitus, and depression with mild dementia. He had smoked heavily until 30 years before admission and had chronic obstructive pulmonary disease. Fifteen months before the current admission, he was admitted to another hospital because of substernal pain, dyspnea, and sustained ventricular tachycardia. The rhythm did not respond to the administration of adenosine, metoprolol, diltiazem, or lidocaine, but electrical cardioversion restored a normal rhythm. The results of laboratory tests are shown in Table 1.First Admission . . .
