Comparison of CT findings between basaloid squamous cell carcinoma and non-basaloid squamous cell carcinoma of the lung.

This study aimed to compare computed tomography (CT) findings between basaloid lung squamous cell carcinoma (SCC) and non-basaloid SCC. From July 2003 to April 2021, 39 patients with surgically proven basaloid SCC were identified. For comparison, 161 patients with surgically proven non-basaloid SCC from June 2018 to January 2019 were selected consecutively.

Automatic Virtual Contrast-Enhanced CT Synthesis Using Dual-Energy CT and Residual U-Net with Attention Module for Detecting Pulmonary Hilar Lymphadenopathy.

Rationale And Objectives: To propose an automatic virtual contrast-enhanced chest computed tomography (CT) synthesis using dual-energy CT and a Residual U-Net with an attention module to detect clinically significant hilar lymphadenopathy.

Materials And Methods: We conducted a retrospective analysis of 2082 patients who underwent dual-energy chest CT scans. Our approach utilized a Residual U-Net combined with a Convolutional Block Attention Module (CBAM) to transform non-contrast CT images into virtual contrast-enhanced CT images.

Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT.

Materials And Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal ( = 13 116) and humeral tumor ( = 1593) cases. The data were divided into training and test groups.

CT evaluation of systemic artery to pulmonary artery fistula: an underdiagnosed disease in patients with hemoptysis.

Background: Systemic artery to pulmonary artery fistula (SA-PAF) is an uncommon disease which is often incidentally diagnosed during evaluation of hemoptysis patients. The aim of our study was to describe the cases of SA-PAF in our institution and to report the correlating clinical and radiological findings.

Methods: We reviewed 231 chest computed tomography (CT) scans performed in our institution due to hemoptysis from January 2020 to February 2023.

CT-Derived Deep Learning-Based Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease.

Purpose: Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD).

Materials And Methods: A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm.

Placental transmogrification of the lung: CT findings with pathologic correlation in six patients.

Background: Placental transmogrification of the lung is a very rare benign lung disease with a characteristic finding being alveoli resembling chorionic villi of the placenta. The purpose of this study was to assess the computed tomography (CT) findings of placental transmogrification of the lung in six patients and their relation to the histopathologic findings.

Methods: Six patients with histopathologically proven placental transmogrification of the lung from 2004 to 2021 were included.

Automated precision localization of peripherally inserted central catheter tip through model-agnostic multi-stage networks.

Background: Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT.

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks.

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated.

Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study.

Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters.

Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs ( = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.

Missed and Detected Incidental Breast Cancers on Contrast Enhanced Chest CT: Detection Rates and CT Features.

This study investigated the rate at which radiologists miss or detect incidental breast cancers on chest CT and to compare the CT features between the two groups. This retrospective study evaluated chest CT examinations and medical records of patients who registered with the diagnosis code of "breast cancer" between January 2016 and December 2020, and who had undergone contrast enhanced chest CT 3-18 months before registration, during which they were unaware of any breast lesions. This study found that out of 84 patients, incidental breast cancer lesions were missed in 54 (64.

Left Atrial Appendage Aneurysm: A Case Report.

Left atrial appendage aneurysm (LAAA) is a rare heart anomaly caused by congenital dysplasia of the pectinate muscle or by an acquired pathological condition of the mitral valve or cardiac muscle. It is often incidentally discovered during chest CT or echocardiography as an abnormal dilatation of the LAA. LAAA is associated with life-threatening complications and most patients require surgical treatment.

A Pictorial Review of Radiologic Findings of Foreign Bodies in the Thorax.

Thoracic foreign bodies (FBs) are serious and relatively frequent in emergency departments. Thoracic FBs may occur in association with aspiration, ingestion, trauma, or iatrogenic causes. Imaging plays an important role in the identification of FBs and their dimensions, structures, and locations, before the initiation of interventional treatment.

CT findings of basaloid squamous cell carcinoma of the lung in 12 patients: A distinct category of squamous cell carcinoma in 2015 WHO classification of lung tumors.

Basaloid squamous cell carcinoma (SCC) is very rare subtype of SCC of the lung and it is important to distinguish basaloid to other subtypes of SCCs, since the prognosis of basaloid subtype is considered poorer than that of other non-basaloid subtypes of SCCs. Aim of this study was to assess computed tomography (CT) findings of basaloid SCC of the lung in 12 patients.From January 2016 to April 2021, 12 patients with surgically proven basaloid SCC of the lung were identified.

Incidence and risk factors for sternal osteomyelitis after median sternotomy.

Background: Sternal osteomyelitis (OM) after median sternotomy is the rarest form of deep sternal wound infections (DSWIs). A retrospective study was implemented to evaluate the incidence and potential risk factors of sternal OM after median sternotomy.

Methods: We analyzed 3,410 consecutive patients who underwent cardiothoracic surgery via median sternotomy from January 2005 to December 2019 at our institution.

Completeness of Reporting of Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy (DTA) of Radiological Articles Based on the PRISMA-DTA Reporting Guideline.

Rationale And Objectives: This study evaluated the completeness of systematic reviews and meta-analyses in radiology using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) and PRISMA-DTA for Abstracts guidelines between articles published before and those published after the issuance of the guideline and identify areas that have been poorly reported.

Materials And Methods: PubMed were searched for systematic reviews on DTA with or without meta-analyses published in general radiology journals between January 1, 2016 and December 31, 2020. The identified articles were assessed for completeness of reporting according to the PRISMA-DTA.

Computed tomography-based visual assessment of chronic obstructive pulmonary disease: comparison with pulmonary function test and quantitative computed tomography.

Background: Chronic obstructive pulmonary disease (COPD) has variable subtypes involving mixture of large airway inflammation, small airway disease, and emphysema. This study evaluated the relationship between visually assessed computed tomography (CT) subtypes and clinical/imaging characteristics.

Methods: In total, 452 participants were enrolled in this study between 2012 and 2017.

Computed tomographic findings of chest injuries following cardiopulmonary resuscitation: More complications for prolonged chest compressions?

Chest injuries are common and inevitable complications of chest compressions during cardiopulmonary resuscitation (CPR). This study aimed to investigate lung parenchymal and thoracic skeletal injuries after CPR by using computed tomography (CT) and to analyze the correlation between the duration of CPR and related complications.We examined 43 non-traumatic cardiac arrest patients who were successfully resuscitated after CPR and had chest CT scans within 24 hours of CPR.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise.

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality.

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.

Objective: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification.

Methods: A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels.

Association between thoracic fat measured using computed tomography and lung function in a population without respiratory diseases.

Background: Local fat distribution patterns and their local or systemic effects have recently attracted significant attention. The aim of this study was to assess the impact of thoracic adiposity on lung function in a population without respiratory diseases according to sex.

Methods: A total of 455 subjects (282 males and 173 females), who had undergone spirometry, and chest and abdominal computed tomography between June 2012 and June 2016 at medical healthcare center, were included.