Testicular cavernous hemangioma masquerading as testicular malignancy.

Cavernous hemangiomas are benign tumors of vascular origin that can develop in any part of the body. However, its occurrence in the testis is rare. To the best of our knowledge, we are reporting the first case of a patient with cavernous hemangioma with concern for an extracapsular extension on ultrasound imaging.

Imaging of the Left Atrial Appendage Before Occluder Device Placement: Evaluation of Virtual Monoenergetic Images in a Single-Bolus Dual-Phase Protocol.

PURPOSE: Preimplantation cardiac computed tomography (CT) for assessment of the left atrial appendage (LAA) enables correct sizing of the device and the detection of contraindications, such as thrombi. In the arterial phase, distinction between false filling defects and true thrombi can be hampered by insufficient contrast medium distribution. A delayed scan can be used to further differentiate both conditions, but contrast in these acquisitions is relatively lower. In this study, we investigated whether virtual monoenergetic images (VMI) from dual-energy spectral detector CT (SDCT) can be used to enhance contrast and visualization in the delayed phase. MATERIALS AND METHODS: Forty-nine patients receiving SDCT imaging of the LAA were retrospectively enrolled. The imaging protocol comprised dual-phase acquisitions with single-bolus contrast injection. Conventional images (CI) from both phases and 40-keV VMI from the delayed phase were reconstructed. Attenuation, signal-, and contrast-to-noise ratios (SNR/CNR) were calculated by placing regions-of-interest in the LAA, left atrium, and muscular portion of interventricular septum. Two radiologists subjectively evaluated conspicuity and homogeneity of contrast distribution within the LAA. RESULTS: Contrast of the LAA decreased significantly in the delayed phase but was significantly improved by VMI, showing comparable attenuation, SNR, and CNR to CI from the arterial phase (attenuation/SNR/CNR, CI arterial phase: 266.0 ± 117.0 HU/14.2 ± 7.2/6.6 ± 3.9; CI-delayed phase: 107.6 ± 35.0 HU/5.9 ± 3.0/1.0 ± 1.0; VMI delayed phase: 260.3 ± 108.6 HU/18.2 ± 10.6/4.8 ± 3.4). The subjective reading confirmed the objective findings showing improved conspicuity and homogeneity in the delayed phase. CONCLUSIONS: The investigated single-bolus dual-phase acquisition protocol provided improved visualization of the LAA. Homogeneity of contrast media was higher in the delayed phase, while VMI maintained high contrast.

Impact of flexible noise control (FNC) image processing parameters on portable chest radiography.

There is a lack of understanding in the performance of flexible noise control (FNC) processing, which is used in digital radiography on a scanner vendor and has four parameters each involving multiple options. The aim of this study was to investigate the impact of FNC on portable chest imaging. An anthropomorphic chest phantom was imaged using a clinical chest program with 85 kV and five radiation dose levels at 40″ source-to-image distance with software-based scatter reduction method. All images were processed without and with FNC. Noise analysis was performed in two regions of interest (ROI) on subtracted noise-only images, and line profiles were generated through a lung-rib interface. In addition, noise power spectra (NPS) analysis was performed in solid water phantoms of 10 and 20 cm thicknesses, using the same acquisition program and a range of dose levels. Last, feedback on retrospectively deidentified, reprocessed, and randomized clinical images from 20 portable chest exams was gathered from two thoracic radiologists. Noise reduction performances of FNC were demonstrated, with the level depending on specific FNC parameters, dose levels, ROI placement, and phantom sizes. Higher frequency textural patterns were revealed through the NPS analysis, which varied based on FNC parameters, dose levels, and phantom sizes. Overall, the vendor default parameter FGA0.5 yielded the highest noise reduction and textural artifacts. Radiologist feedback showed consistent preference of no FNC due to the presence of textural artifacts in the FNC-processed images. An algorithm improvement to avoid introducing artifacts would be desired.

Deep Learning Improves the Temporal Reproducibility of Aortic Measurement.

Imaging-based measurements form the basis of surgical decision making in patients with aortic aneurysm. Unfortunately, manual measurement suffer from suboptimal temporal reproducibility, which can lead to delayed or unnecessary intervention. We tested the hypothesis that deep learning could improve upon the temporal reproducibility of CT angiography-derived thoracic aortic measurements in the setting of imperfect ground-truth training data. To this end, we trained a standard deep learning segmentation model from which measurements of aortic volume and diameter could be extracted. First, three blinded cardiothoracic radiologists visually confirmed non-inferiority of deep learning segmentation maps with respect to manual segmentation on a 50-patient hold-out test cohort, demonstrating a slight preference for the deep learning method (p < 1e-5). Next, reproducibility was assessed by evaluating measured change (coefficient of reproducibility and standard deviation) in volume and diameter values extracted from segmentation maps in patients for whom multiple scans were available and whose aortas had been deemed stable over time by visual assessment (n = 57 patients, 206 scans). Deep learning temporal reproducibility was superior for measures of both volume (p < 0.008) and diameter (p < 1e-5) and reproducibility metrics compared favorably with previously reported values of manual inter-rater variability. Our work motivates future efforts to apply deep learning to aortic evaluation.

Electromagnetic Navigational Bronchoscopy versus CT-guided Percutaneous Sampling of Peripheral Indeterminate Pulmonary Nodules: A Cohort Study.

Purpose To compare the diagnostic yield and complication rates of electromagnetic navigational bronchoscopic (ENB)-guided and computed tomography (CT)-guided percutaneous tissue sampling of lung nodules. Materials and Methods Retrospectively identified were 149 patients sampled percutaneously with CT guidance and 146 patients who underwent ENB with transbronchial biopsy of a lung lesion between 2013 and 2015. Clinical data, incidence of complications, and nodule pathologic analyses were assessed through electronic medical record review. Lung nodule characteristics were reviewed through direct image analysis. Molecular marker studies and pathologic analyses from surgical excision were reviewed when available. Multiple-variable logistic regression models were built to compare the diagnostic yield and complication rates for each method and for different patient and disease characteristics. Results CT-guided sampling was more likely to be diagnostic than ENB-guided biopsy (86.0% [129 of 150] vs 66.0% [99 of 150], respectively), and this difference remained significant even after adjustments were made for patient and nodule characteristics (P < .001). Age, American Society of Anesthesiologists class, emphysema grade, nodule size, and distance from pleura were not significant predictors of increased diagnostic yield. Intraprocedural time for physicians was significantly lower with CT-guided sampling (P < .001). Similar yield for molecular analyses was noted with the two approaches (ENB-guided sampling, 88.9% [32 of 36]; CT-guided sampling, 82.0% [41 of 50]). The two groups had similar rates of major complications (symptomatic hemorrhage, P > .999; pneumothorax requiring chest tube and/or admission, P = .417). Conclusion CT-guided transthoracic biopsy provided higher diagnostic yield in the assessment of peripheral pulmonary nodules than navigational bronchoscopy with a similar rate of clinically relevant complications. © RSNA, 2017 Online supplemental material is available for this article.

Right middle pulmonary vein classification by 3D-CT reconstruction and its clinical significance.

Background: A profound understanding of the type of right middle lobe (RML) vein return is crucial for thoracic surgeons to ensure safe anatomic lung resection. In this study, the type of venous return in the RML and its clinical significance were analyzed using the 3D computed tomography (3D-CT) reconstruction technique. Methods: This retrospective study analyzed the patients who underwent anatomical lobectomy or anatomical lung segment resection with preoperative 3D-CT reconstruction at the Department of Thoracic Oncology Surgery, Fujian Cancer Hospital from January 2020 to October 2022. The exclusion criteria were as follows: (I) blurred CT images and inadequate IQQA 3D reconstruction for clear visualization of bronchial and vascular subsegmental branches; (II) patients with a previous history of right middle lobectomy; (III) incomplete medical records. Results: This study included 608 eligible cases, 245 males (40.30%) and 363 females (59.70%), with an average age of 58.61 years. We identified four major types of RML venous return, including 407 cases (66.94%) of central vein type, 123 cases (20.23%) of isolated vein type, 11 cases (1.81%) of basal vein type, and 67 cases (11.02%) of combined vein type. Furthermore, the central venous type comprised four subtypes: 191 cases (27.3%) had one branch, 165 cases (27.14%) had two branches, 43 cases (7.07%) had three branches, and 8 cases (1.32%) had more than three branches. The combined venous type includes four subtypes: combination type A (n=37 cases, 6.09%), combination type B (n=7 cases, 1.15%), combination type C (n=21 cases, 3.45%), and combination type D (n=2 cases, 0.33%). Conclusions: Based on 3D-CT reconstruction, numerous types of venous return in the RML were found, highlighting the significance of 3D-CT reconstruction for thoracic surgeons. It plays a crucial role in understanding the type of RML venous return prior to surgery, providing invaluable information to ensure surgical safety and success.

High-Pitch Multienergy Coronary CT Angiography in Dual-Source Photon-Counting Detector CT Scanner at Low Iodinated Contrast Dose.

OBJECTIVES: The aim of this study was to evaluate the high-helical pitch, multienergy (ME) scanning mode of a clinical dual-source photon-counting detector (PCD) computed tomography (CT) and the benefit of virtual monoenergetic images (VMIs) for low-contrast-dose coronary CT angiography (CTA). MATERIALS AND METHODS: High-pitch (3.2) ME coronary CTA was performed in PCD-CT in 27 patients using low contrast dose (30 mL of iohexol 350 mg/mL) and in 26 patients at routine contrast dose (60 mL). Low-energy-threshold 120 kV images (also known as T3D images) and 50 kiloelectron volts (50 keV) and 100 kiloelectron volts (100 keV) VMIs were reconstructed using a 1024 × 1024 matrix and 0.6-mm slices. The CT numbers, noise, and contrast-to-noise ratio (CNR) were measured in the ascending aorta (AA), left main coronary artery (LMCA), and distal left anterior descending (LAD) artery. Confidence in grading luminal stenosis with calcific plaque, noncalcific plaque, and stent was evaluated by 2 independent readers on a 0-100 scale (0 the lowest), and a CAD-RADS score was assigned. Image contrast enhancement, sharpness, noise, artifacts, and overall image quality were rated using a 5-point ordinal scale (1 the lowest). RESULTS: The radiation doses (CTDI) in low- and routine-contrast cohorts were 2.5 ± 0.6 mGy and 3.1 ± 1.7 mGy, respectively ( P = 0.12). At all measured locations, the mean CT number was >300 HU in 120 kV (LMCA 382.9 ± 76.2, distal LAD 341.0 ± 53.9, AA 399.5 ± 76.1) and 50 keV images (LMCA 667.5 ± 139.9, distal LAD 578.1 ± 121.5, AA 700.8 ± 142.5) in the low-contrast cohort, with a 96% increase in CT numbers for 50 keV over 120 kV. The CT numbers were significantly higher ( P < 0.0001) in 50 keV than 120 kV and 100 keV VMI. The CNR was also significantly ( P < 0.0001) higher in 50 keV than 120 kV and 100 keV images in all vessels. Confidence in the assessment of luminal stenosis in the presence of calcific plaque was significantly higher ( P = 0.001) with the addition of 100 keV VMI (median score, 100) than using 50 keV alone (median score, 70) and 120 kV (median score, 70) for reader 1, but no significant differences were seen for reader 2 who had same median scores of 100 for all image types. The confidence in the assessment of luminal stenosis within a stent improved with the use of 100 keV images for both readers (reader 1: median scores for 50 + 100 keV = 100, 50 keV = 82.5, 120 kV = 82.5; reader 2: 50 + 100 keV = 100, 50 keV = 90, 120 kV = 90). There were no significant differences in confidence scores for assessment of luminal stenosis from noncalcific plaques for both readers. The reader-averaged qualitative scores for vascular enhancement and overall image quality were significantly higher for 50 keV VMI than for 120 kV images in both low- and routine-contrast dose cohorts. The image sharpness was nonsignificantly higher at 50 keV VMI than 120 kV images, and the artifact score was comparable for 50 keV VMI and 120 kV images. The noise was higher in 50 keV VMI than in 120 kV images. CONCLUSIONS: High-pitch ME PCD-CT mode produced diagnostic quality coronary CTA images at low radiation and iodinated contrast doses. The availability of ME VMIs significantly improved the CNR, overall image quality, and confidence in assessment of luminal stenosis in the presence of calcific plaques and stents, and resulted in change of CAD-RADS categories in 9 patients.

Pulmonary manifestations in VEXAS syndrome.

BACKGROUND: Vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic (VEXAS) syndrome is a recently recognized multisystem disorder caused by somatic mutations in the UBA1 gene. METHODS: A retrospective cohort study was conducted on all patients with VEXAS syndrome evaluated at our institution from June 2020 through May 2022. Medical records and chest imaging studies were reviewed. RESULTS: We identified 45 subjects with median age of 68 years (range, 57-89), all men. Prior to VEXAS diagnosis, most patients had been diagnosed with various hematologic, rheumatologic, and dermatologic disorders. Most patients (84%) demonstrated canonical UBA1 methionine-41 (p.Met41) somatic mutations in hematopoietic cells. Fever (82%), skin lesions (91%), and respiratory symptoms (93%) were common presenting features. Chest CT manifested abnormalities in 91% of patients including parenchymal opacities in 25 (74%), most commonly ground-glass opacities (47%), along with mediastinal lymphadenopathy (29%), airway abnormalities (29%), and pleural effusion (24%). Pulmonary function test results available in 18 (40%) patients demonstrated mild restrictive impairment or normal results. Bronchoalveolar lavage and lung biopsy performed in a minority of patients demonstrated neutrophilic alveolitis and parenchymal inflammation, respectively. All patients received glucocorticoid therapy with at least partial response, but relapses were common and other immunosuppressive agents were employed in most patients. Pulmonary involvement appeared to improve in patients who received tocilizumab and JAK inhibitors. CONCLUSION: The pulmonary manifestations in VEXAS are relatively nonspecific and nonsevere, occur in the context of systemic inflammation and are responsive to escalation in glucocorticoid dosing.

Imaging Patterns in Occupational Lung Disease-When Should I Consider?

Occupational lung diseases (OLDs) encompass a broad group of entities related to the inhalation of a variety of agents in the workplace. OLDs may affect the lung parenchyma, pleura, and/or airways. OLDs can pose a diagnostic challenge for radiologists due to a lack of exposure history and overlap in imaging findings with nonoccupational-related entities. For this reason, it is important for the radiologist to be familiar with the high-resolution computed tomography patterns associated with OLDs and consider OLDs when formulating a differential.

A rare presentation of unilateral periureteral renal lymphangiomatosis.

Renal lymphangiomatosis is a rare developmental malformation of the perirenal lymphatic system. We report a unique case with unilateral massive periureteral involvement in addition to intrarenal and peripelvic lymphangiomatosis. Although this is a rare entity, it should be considered in patients with peripelvic or periureteric cystic lesions as it may affect appropriate management and follow-up. This case report reviews the imaging features of this entity and a comprehensive literature review and discussion about the entity will be provided.

Autopsy study of fatal invasive pulmonary aspergillosis: Often undiagnosed premortem.

Invasive aspergillosis (IA) is a serious complication in immunocompromised and critically ill patients but is difficult to diagnose. We sought to examine how often cases go undiagnosed and to understand the presenting clinical and radiologic features associated with fatal IA. We reviewed cases of fatal IA confirmed at autopsy (N = 67) between 1999 and 2019 at a tertiary academic hospital. At autopsy, pulmonary involvement was present in 97% of cases--46% were limited to the lungs and 51% had concomitant extrapulmonary involvement. Immunosuppression with either glucocorticoids and/or other immunosuppressive agents was present in 85%. Among those not immunocompromised (15%), chronic lung disease was present in 70%, and a respiratory coinfection was found in 50%. Chest imaging abnormalities including consolidation, ground glass opacities, halo sign, cavitation, and air crescent sign were present in 49%, 49%, 37%, 22%, and 7% of cases, respectively. Diagnostic bronchoscopy was performed in 61% of cases and yielded aspergillus in 63% of those cases by either bronchoalveolar lavage (galactomannan and/or culture), bronchial washings, or transbronchial biopsy cultures. Either a respiratory coinfection or other systemic coinfection was diagnosed in 64%. The performance of diagnostic bronchoscopy was associated with accurate pre-mortem identification of IA (p = 0.001). Clinicians correctly identified IA as the cause of death in only 27% of fatal IA cases identified at autopsy. Complex presenting features, high rates of co-infections, and low rates of invasive diagnostic procedures may have led to missed diagnoses of IA.

Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification.

Background: Bronchoscopy is a key step in the diagnosis and treatment of respiratory diseases. However, the level of expertise varies among different bronchoscopists. Artificial intelligence (AI) may help them identify bronchial lumens. Thus, a bronchoscopy quality-control system based on AI was built to improve the performance of bronchoscopists. Methods: This single-center observational study consecutively collected bronchoscopy videos from Shanghai Chest Hospital and segmented each video into 31 different anatomical locations to develop an AI-assisted system based on a convolutional neural network (CNN) model. We then designed a single-center trial to compare the accuracy of lumen recognition by bronchoscopists with and without the assistance of the AI system. Results: A total of 28,441 qualified images of bronchial lumen were used to train the CNNs. In the cross-validation set, the optimal accuracy of the six models was between 91.83% and 96.62%. In the test set, the visual geometry group 16 (VGG-16) achieved optimal performance with an accuracy of 91.88%, and an area under the curve of 0.995. In the clinical evaluation, the accuracy rate of the AI system alone was 54.30% (202/372). For the identification of bronchi except for segmental bronchi, the accuracy was 82.69% (129/156). In group 1, the recognition accuracy rates of doctors A, B, a and b alone were 42.47%, 34.68%, 28.76%, and 29.57%, respectively, but increased to 57.53%, 54.57%, 54.57%, and 46.24% respectively when combined with the AI system. Similarly, in group 2, the recognition accuracy rates of doctors C, D, c, and d were 37.90%, 41.40%, 30.91%, and 33.60% respectively, but increased to 51.61%, 47.85%, 53.49%, and 54.30% respectively, when combined with the AI system. Except for doctor D, the accuracy of doctors in recognizing lumen was significantly higher with AI assistance than without AI assistance, regardless of their experience (P<0.001). Conclusions: Our AI system could better recognize bronchial lumen and reduce differences in the operation levels of different bronchoscopists. It could be used to improve the quality of everyday bronchoscopies.

The incidence and risk factors of acute pain after preoperative needle localization of pulmonary nodules: a cross-sectional study.

Background: The incidence, severity and associated risk factors of acute pain after preoperative needle localization of pulmonary nodules are poorly characterized. We therefore conducted a cross-sectional study to quantify the acute pain induced by preoperative needle localization of small pulmonary nodules before video-assisted thoracoscopic surgery (VATS). Methods: We conducted this study at Shanghai Chest Hospital from September 2021 through December 2021. Eligible patients were between 18 and 75 years old and had small pulmonary nodules requiring preoperative CT-guided needle localization. The intensity of acute pain was assessed using the visual analogue scale (VAS) after preoperative needle localization. A VAS score ≥4 cm indicated moderate to severe pain. Patient demographics and CT-guided localization factors were collected to identify significant predictors associated with moderate to severe pain. Results: A total of 300 patients were included in the final analysis, with a mean (SD) age of 51 (SD =12) years old; 63% were female. Moderate to severe pain occurred in 50.8% of patients during deep breathing and 45.7% of patients during movement. Multivariate logistic regression analysis showed that multiple localization needles [multiple needle localizations vs. single needle localization, odds ratio (OR): 2.363, 95% confidence interval (CI): 1.157-4.825, P=0.018] and the specific location of needle puncture on the chest wall were significant predictors of moderate to severe pain after CT-guided needle localization (lateral chest wall vs. anterior chest wall OR: 2.235, 95% CI: 1.106-4.518, P=0.025; posterior chest wall vs. anterior chest wall OR: 1.198, 95% CI: 0.611-2.349, P=0.599). Conclusions: In adult patients receiving hookwire CT-guided localization, moderate to severe pain was common. Avoiding the localization route through lateral chest wall may be helpful and pharmacological medications or regional blockade is necessitated in high-risk population.

Idiopathic Chronic Eosinophilic Pneumonia Evolving to Pulmonary Fibrosis: A Retrospective Analysis.

Background: Patients with idiopathic chronic eosinophilic pneumonia (ICEP) may have pulmonary fibrosis. Objectives: To investigate the predictors of pulmonary fibrosis in ICEP, to describe the timeline of pulmonary fibrosis after ICEP diagnosis, and to detail the radiologic pattern of fibrosis. Methods: A retrospective computer-assisted search was performed to identify patients with ICEP seen at Mayo Clinic in Rochester, Minnesota, from January 1, 1997, through September 1, 2019. Patients with follow-up chest computed tomography (CT) beyond 12 months after the ICEP diagnosis were included in the study. Demographic, clinical, radiologic, and histopathologic characteristics were analyzed. Proportional hazards regression was used to assess the predictors of pulmonary fibrosis. Results: We identified 62 patients (mean [SD] age at ICEP diagnosis, 60 [13] years; female sex, 37 [60%]). Cough (87%) and shortness of breath (85%) were the most common presenting symptoms. Of patients, 27 (44%) had a history of smoking and 27 (44%) had a history of asthma. During follow-up, 23 patients (37%) had CT evidence of pulmonary fibrosis, of whom 16 patients (70%) had a CT pattern inconsistent with usual interstitial pneumonia. In 29% of the patients, the CT evidence of pulmonary fibrosis developed within 2 years after ICEP. Age and male sex were predictors of pulmonary fibrosis. Of note, a history of asthma decreased the likelihood of pulmonary fibrosis. Conclusions: Development of pulmonary fibro-sis is not uncommon in patients with ICEP, especially older men, and is associated with increased risk of death.

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis. RESEARCH QUESTION: Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning? STUDY DESIGN AND METHODS: This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists' performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility. RESULTS: For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff's alpha did not include zero). INTERPRETATION: Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.

A case report of primary signet ring cell carcinoma of the lung: imaging study and literature review.

Signet ring cell carcinoma (SRCC) is a subtype of adenocarcinoma with characteristics of strong invasion and a poor prognosis. While it can occur in various organs, including the stomach, colon, esophagus, bladder, prostate, pancreas, and breast, primary lung SRCC is rare, and most SRCC found there are from gastrointestinal metastasis. Reports on primary lung SRCC are few and the aim of this study is to describe the imaging, histopathological, and immunohistochemical characteristics of a case of primary lung SRCC in our hospital. A 68-year-old female with no smoking history was admitted with recurrent cough, chest pain, and dyspnea of 2 months duration. Computed tomographic (CT) chest showed multiple solids nodules of different sizes and mass in the left upper lobe, lower lobe, and subpleural region. Multiple enlarged lymph nodes were seen in the mediastinum and left hilum. The aim of this paper is to improve the understanding of this tumor. A literature review identified 15 cases of primary lung SRCC with available CT imaging. Except for two patients with multiple ground glass nodules and multiple small nodules, the rest were solid, and ranged in size from 1.0 to 8 cm. Only one patient had a cavity in the solid lesion. Immunohistochemical stains for thyroid transcription factor-1 (TTF-1) (13/13) and CK7 (12/12) showed positive reaction in all cases evaluated, and napsin A (3/4) were also positive, while all cases including CK20 (12/12) and CDX2 (6/6) were negative.

Presenting Clinicoradiologic Features, Causes, and Clinical Course of Exogenous Lipoid Pneumonia in Adults.

BACKGROUND: Exogenous lipoid pneumonia (ELP) develops when lipid-containing substances enter the airways through aspiration or inhalation and incite an inflammatory response. The diagnosis of ELP often is difficult because findings may be nonspecific. The clinical course of ELP has not been well characterized. RESEARCH QUESTION: What are the presenting clinicoradiologic features of ELP, its causative agents, and clinical course? STUDY DESIGN AND METHODS: We searched the Mayo Clinic electronic medical records for patients diagnosed with ELP between 1998 and 2020. Inclusion diagnostic criteria were: (1) lipoid pneumonia (LP) on histopathologic examination, (2) lipid-laden macrophages in BAL fluid, or (3) fatty attenuation of parenchymal opacities on chest CT imaging. Additionally, all patients were required to have a clinician diagnosis of LP in the absence of conditions known to cause endogenous LP. RESULTS: Thirty-four patients were identified. Mean age was 71 years, with no sex predominance; one-half were asymptomatic. The diagnosis was confirmed by lung biopsy (including three lobectomies for suspected malignancy) in 71% of patients, CT scan in 24% of patients, and BAL in 5% of patients. Most patients manifested bilateral parenchymal opacities that commonly involved the lower lobes; fatty attenuation was identifiable in only 41% of patients. A causative substance was identified in 79% of patients, in most cases after the diagnosis was established. Over a median follow-up of 1.2 years, only 20% of patients with chronic respiratory symptoms improved, whereas 50% worsened. Over a median follow-up interval of 1 year, CT scan abnormalities improved or resolved in 33% of patients and progressed in 39% of patients. Patients who deteriorated were older, with a higher prevalence of GI disorders than those who remained stable or improved. INTERPRETATION: ELP often is asymptomatic and may not manifest fatty attenuation on chest CT imaging. Clinical and radiologic abnormalities persist or worsen in most affected patients, even when the causative agent is discontinued.

Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules.

BACKGROUND: Clinical management of subsolid nodules (SSNs) is defined by the suspicion of tumor invasiveness. We sought to develop an artificial intelligent (AI) algorithm for invasiveness assessment of lung adenocarcinoma manifesting as radiological SSNs. We investigated the performance of this algorithm in classification of SSNs related to invasiveness. METHODS: A retrospective chest computed tomography (CT) dataset of 1,589 SSNs was constructed to develop (85%) and internally test (15%) the proposed AI diagnostic tool, SSNet. Diagnostic performance was evaluated in the hold-out test set and was further tested in an external cohort of 102 SSNs. Three thoracic surgeons and three radiologists were required to evaluate the invasiveness of SSNs on both test datasets to investigate the clinical utility of the proposed SSNet. RESULTS: In the differentiation of invasive adenocarcinoma (IA), SSNet achieved a similar area under the curve [AUC; 0.914, 95% confidence interval (CI): 0.813-0.987] with that of the 6 doctors (0.900, 95% CI: 0.867-0.922). When interpreting with the assistance of SSNet, the sensitivity of junior doctors, specificity of senior doctor, and their accuracy were significantly improved. In the external test, SSNet (AUC: 0.949, 95% CI: 0.884-1.000) achieved a better AUC than doctors (AUC: 0.883, 95% CI: 0.826-0.939) whose AUC increased (AUC: 0.908, 95% CI: 0.847-0.982) with SSNet assistance. In the histological subtype classifications, SSNet achieved better performance than practicing doctors. The AUCs of doctors were significantly improved with the assistance of SSNet in both 4-category and 3-category classifications to 0.836 (95% CI: 0.811-0.862) and 0.852 (95% CI: 0.825-0.882), respectively. CONCLUSIONS: The AI diagnostic system achieved non-inferior performance to doctors, and will potentially improve diagnostic performance and efficiency in SSN evaluation.

A case of liver abscesses and porto-enteric fistula caused by an ingested toothpick: A review of the distinctive clinical and imaging features.

Though foreign body (FB) ingestions are a relatively common occurrence in the bustling emergency department, particularly among children, the vast majority of FBs either pass uneventfully or can be retrieved endoscopically. Only a small percentage of patients will experience complications such as bowel obstruction, ischemia, or perforation that may progress to abscess, septic thrombophlebitis, peritonitis, or shock. Depending on their composition, small FBs can be very difficult to detect on computed tomography (CT). However, a delay in definitive treatment resulting from the failure to clinically or radiologically recognize that a FB may be responsible for the acute presentation can lead to substantial morbidity and mortality. We present a case of unresolving hepatic abscess and recurrent sepsis caused by a toothpick-induced porto-enteric fistula in which the FB was not initially identified, thereby leading to multiple treatment failures and readmissions. This is followed by a literature review with comprehensive discussion of the distinctive clinical and imaging features of migrated FB-induced liver abscesses.

Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules.

Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.