Thin-section CT Images Research Articles

Hypersensitivity Pneumonitis on Thin-Section Chest CT Scans: Diagnostic Performance of the ATS/JRS/ALAT versus ACCP Imaging Guidelines.

Purpose To compare the diagnostic performance of the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax (ATS/JRS/ALAT) versus the American College of Chest Physicians (ACCP) imaging classifications for hypersensitivity pneumonitis (HP). Materials and Methods Patients in the institutional review board-approved Interstitial Lung Disease (ILD) registry referred for multidisciplinary discussion (MDD) at the authors' institution (January 1, 2006-April 1, 2021) were included in this retrospective study when ILD was diagnosed at MDD. MDD diagnoses included HP, connective tissue disease-ILD, and idiopathic pulmonary fibrosis. Retrospective review of thin-section CT images was performed in consensus by two cardiothoracic radiologists blinded to the diagnosis. Diagnostic patterns were determined for thin-section CT images using both classifications. Discordance rates were determined. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were assessed using MDD diagnosis as the reference standard. Results A total of 297 patients were included in the study: 200 (67%) with HP, 49 (16%) with connective tissue disease-ILD, and 48 (16%) with idiopathic pulmonary fibrosis at MDD. The discordance rate between the two classifications was 21%. Assuming low HP prevalence (10%), ATS/JRS/ALAT classification outperformed ACCP classification, with greater accuracy (92.3% vs 87.6%) and greater positive predictive value (60.7% vs 42.9%). Assuming high prevalence (50%), accuracy and negative predictive value were superior using ACCP classification (81.7% vs 79.7% and 77.7% vs 72.6%, respectively), and positive predictive value was superior using ATS/JRS/ALAT classification (93.3% vs 87.1%). Conclusion Accuracy of the ATS/JRS/ALAT and ACCP HP classifications was greater in settings with low and high HP prevalence, respectively. Diagnostic performance of both classifications was discordant in a minority of cases. Keywords: CT, Thorax, Hypersensitivity Pneumonitis, Interstitial Lung Disease Supplemental material is available for this article. © RSNA, 2024.

Application of Synthetic Time-Of-Flight Magnetic Resonance Angiography-Computed Tomography Fusion Imaging in Preoperative Planning for Aneurysm Clipping Surgery: A Comparative Study with Three-Dimensional Computed Tomography Angiography

Some patients with intracranial aneurysms (IAs) cannot undergo three-dimensional computed tomography angiography (3D-CTA) or digital subtraction angiography due to contraindications to contrast agents or radiation. Time-of-flight magnetic resonance angiography (TOF-MRA) offers a contrast-free alternative but lacks cranial bone detail critical for surgical planning. This study evaluates the feasibility of using 3D Slicer to fuse TOF-MRA with thin-section CT images to generate synthetic images resembling CTA for surgical clipping planning. This prospective study included 22 patients with unruptured IAs and 8 with ruptured IAs undergoing aneurysm clipping surgery (≥3mm). TOF-MRA and CT/3D-CTA scans were fused using 3D Slicer. Neuroradiologists and neurosurgeons independently assessed 3D-CTA and synthetic TOF-MRA-CT images for aneurysm detection rates, morphology, and dimensions. Evaluation metrics included dice similarity coefficient and 95% Hausdorff distance. Evaluation of aneurysm detection rates, morphology, and dimensions showed no significant differences between synthetic TOF-MRA-CT fusion images and 3D-CTA (all P > 0.05). Neuroradiologist assessments revealed strong concordance in aneurysm morphology between synthetic TOF-MRA-CT fusion images and 3D-CTA (κ= 0.867, P < 0.001). The dice similarity coefficient (0.937±0.012) and Hausdorff distance (4.54±0.26) indicated a high degree of image overlap between synthetic TOF-MRA-CT fusion images and 3D-CTA. Surgeons rated the consistency of aneurysm morphology between synthetic TOF-MRA-CT fusion images and intraoperative findings as strongly concordant (κ= 0.873, P < 0.001). Synthetic TOF-MRA-CT fusion images closely match 3D-CTA for ≥3mm aneurysms, demonstrating comparable diagnostic and surgical clipping planning effectiveness. They represent a promising alternative for personalized preoperative planning, particularly when contrast agents are contraindicated.

Study on Electrical Characteristics and Formation Mechanism of Micro-Ion Capacitor in Shale Pore and Fracture Structure

Due to the micro/nanoscale and intricate pore structure, poor connectivity, complex pathways, the presence of microfractures, and the coexistence of organic and inorganic pores, shale and other tight reservoirs exhibit increasingly complex conductive characteristics. This paper mainly studied the electrical properties of shale based on experimental test data, including scanning electron microscope (SEM) thin-section and microtomography CT (μ-CT) images, and analyzed the shale pore type, pore structure characteristics, and development of fracture. Then, the distribution of pyrite, content, and graphitization of organics and their influence on the electrical properties are discussed. Furthermore, the double electrical layer and zeta potential in the shale zone were discussed in depth. The results revealed that, within the content of pyrite, organics, and its graphitization, the vitrinite maturity are inversely proportional to shale resistivity. It was also found that in the presence of an external electromagnetic field, the fluid in shale pores is subjected to the combined strength of pore pressure and external field potential difference. Thus, its response equation should be an improved Navier-Stokes equation, which considers pore pressure, zeta potential, and Coulomb force. When shale is subjected to an external electromagnetic field, due to the complex pores structure and organic and inorganic minerals, it will represent more of a dielectric-like property than electricity. So, it will form special microscopic ionic capacitors, which are different from common plate capacitors. There are three special kinds of microscopic ionic capacitors, they are (I) the intergranular pore microscopic ionic capacitor model, (II) the particle with isolated pore microscopic ionic capacitor model, and (III) the pyrite or graphite or other organics microscopic ionic capacitor model. Finally, the characteristics of microscopic ion capacitors are summarized: irregular polar area and varying distance between poles, varying charges with time, and salinity of the formation water.

Cone-Beam CT of the Extremities in Clinical Practice.

Cone-beam CT (CBCT) is a promising tool with increasing applications in musculoskeletal imaging due to its ability to provide thin-section CT images of the appendicular skeleton and introduce weight bearing, which accounts for loading forces that typically interact with and affect this anatomy. CBCT devices include an x-ray source directly opposite a digital silicon detector panel that performs a single rotation around an object of interest, obtaining thin-section images. Currently, the majority of research has been focused on the utility of CBCT with foot and ankle pathologic abnormalities, due to the complex architectural arrangement of the tarsal bones and weight-bearing nature of the lower extremities. Associated software can provide a variety of options for image reconstruction, including metal artifact reduction, three-dimensional biometric measurements, and digitally reconstructed radiographs. Advancements in this technology have allowed imaging of the knee, hip, hand, and elbow. As more data are published, it is becoming evident that CBCT provides many additional benefits, including fast imaging time, low radiation dose, lower cost, and small equipment footprint. These benefits allow placement of CBCT units outside of the traditional radiology department, including the orthopedic clinic setting. These technologic developments have motivated clinicians to define the scope of CBCT for diagnostics, surgical planning, and longitudinal imaging. As efforts are made to create standardized protocol and measurements, the current understanding and surgical approach for various orthopedic pathologic conditions will continue to shift, with the hope of improving outcomes. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images.

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence

An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5–1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0–I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.

A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan.

The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.

Establishing and Leading a 3D Postprocessing Radiology Lab: A Managerial and Leadership Perspective.

The rapid acquisition of larg volumes of thin-section CT images has created a considerable need and interest for 3D postprocessing during the interpretation of medical imaging. As a result of the increasing number of postprocessing applications, requiring diagnostic radiologists to perform postprocessing is no longer realistic. This article is a comprehensive review of medical resources regarding establishing a postprocessing radiology laboratory. Besides, leadership and managerial aspects have been covered through a professional business lens. In large-volume settings, a dedicated 3D postprocessing lab ensures the quality, reproducibility, and efficiency of images. Adequate staffing is necessary to fulfill the postprocessing requirements. Educational and experience requirements for 3D technologists may vary among different running laboratories. To evaluate the establishment and running of a 3D lab, it is beneficial to implement diagnostic radiology cost-effectiveness tools. Although establishing a 3D lab has many benefits, certain challenges should be considered. Outsourcing or offshoring may serve as alternatives for establishing a postprocessing laboratory. Building and operating a 3D lab is a significant change in healthcare facilities, and it is crucial for organizations to be aware of the strong resistance toward alternatives the status quo, known as the status quo trap. The change process has essential steps, and skipping the steps creates an illusion of speed but never produces satisfactory results. The organization should ensure the engagement of all interested parties in the whole process. Moreover, a clear vision and proper communication of the vision are vital, and it is crucial to value small wins and ensure expectation clarity in leading the lab during the process.

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality. To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V). This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR). Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001). DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V. Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

Reticulation Sign on Thin-Section CT: Utility for Predicting Invasiveness of Pure Ground-Glass Nodules.

BACKGROUND. Pure ground-glass nodules (pGGNs) may represent a diverse range of histologic entities of varying aggressiveness. OBJECTIVE. The purpose of this study was to evaluate the use of the reticulation sign on thin-section CT images for predicting the invasiveness of pGGNs. METHODS. This retrospective study included 795 patients (mean age, 53.4 ± 11.1 [SD] years; 254 men, 541 women) with a total of 876 pGGNs on thin-section CT that underwent resection between January 2015 and April 2022. Two fellowship-trained thoracic radiologists independently reviewed unenhanced CT images to assess the pGGNs for a range of features, including diameter, attenuation, location, shape, air bronchogram, bubble lucency, vascular change, lobulation, spiculation, margins, pleural indentation, and the reticulation sign (defined as multiple small linear opacities resembling a mesh or a net); differences were resolved by consensus. The relationship between the reticulation sign and lesion invasiveness on pathologic assessment was evaluated. RESULTS. On pathologic assessment, the 876 pGGNs included 163 nonneoplastic and 713 neoplastic pGGNs (323 atypical adenomatous hyperplasias [AAHs] or adenocarcinomas in situ [AISs], 250 minimally invasive adenocarcinomas [MIAs], and 140 invasive adenocarcinomas [IACs]). Interobserver agreement for the reticulation sign, expressed as kappa, was 0.870. The reticulation sign was detected in 0.0% of nonneoplastic lesions, 0.0% of AAHs/AISs, 6.8% of MIAs, and 54.3% of IACs. The reticulation sign had sensitivity of 24.0% and specificity of 100.0% for a diagnosis of MIA or IAC and sensitivity of 54.3% and specificity of 97.7% for a diagnosis of IAC. In multivariable regression analyses including all of the assessed CT features, the reticulation sign was a significant independent predictor of IAC (OR, 3.64; p = .001) but was not a significant independent predictor of MIA or IAC. CONCLUSION. The reticulation sign, when observed in a pGGN on thin-section CT, has high specificity (albeit low sensitivity) for invasiveness and is an independent predictor of IAC. CLINICAL IMPACT. Those pGGNs that show the reticulation sign should be strongly suspected to represent IAC; this suspicion may guide risk assessments and follow-up recommendations.

An analysis of the left top pulmonary vein and comparison with the right top pulmonary vein for lung resection by three-dimensional CT angiography and thin-section images

PurposeThe right top pulmonary vein (RTPV) is defined as an anomalous branch of the right superior PV (SPV) draining into the PV or left atrium (LA). Several previous reports have described the RTPV, but only a few have mentioned the left top PV (LTPV). The present study aimed to evaluate the branching patterns of the RTPV and LTPV using thin-section CT images and three-dimensional CT angiography (3D-CTA).Materials and methodsThis study included 1437 consecutive patients for evaluation of the right side and 1454 consecutive patients for the left side who were suspected of lung cancer and underwent CTA. We assessed the presence of each RTPV and LTPV and their branching patterns on the CTA images. When the RTPV or LTPV was identified, the maximum short-axis diameter was measured.ResultsRTPV was found in 9.1% (131/1437), whereas LTPV was found in 2.9% (42/1454) of the patients. RTPV was also observed in 17.1% (7/41) of LTPV cases, except for one case in which the right side could not be evaluated. The most common RTPV inflow site was the right inferior PV (IPV) in 64.9% (85/131) of the patients, whereas that of the LTPV was the left IPV in 100.0% (42/42) of the patients. The mean diameter of the RTPV and LTPV was 3.3 mm (range, 1.3–7.5 mm) and 2.4 mm (range, 0.9–6.3 mm), respectively (P < 0.01).ConclusionThe top PV branching pattern variations can be evaluated using thin-section CT and 3D-CTA images. RTPV is not a rare finding, and LTPV should also be identified in lung cancer cases scheduled for resection.

The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks

Background and Objectives.There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system.Methods.A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.0mm), and thin (1.0mm) section scans available. Pulmonary nodule detection is performed by human and artificial intelligence models for nodule detection developed using 3D convolutional neural networks (CNNs). The intra-sample consistency is evaluated with thick and thin scans, for both clinical doctor and NN (neural network) models. Free receiver operating characteristic (FROC) is used to measure the accuracy of humans and NNs.Results.Trained NNs outperform humans with small nodules < 6.0mm, which is a good complement to human ability. For nodules > 6.0mm, human and NNs perform similarly while human takes a fractional advantage. By allowing a few more FPs, a significant sensitivity improvement can be achieved with NNs.Conclusions.There is a performance gap between the thick and thin scans for pulmonary nodule detection regarding both false negatives and false positives. NNs can help reduce false negatives when the nodules are small and trade off the false negatives for sensitivity. A combination of human and trained NNs is a promising way to achieve a fast and accurate diagnosis.

Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests.

Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.

Investigation of the Relationship Between Idiopathic Intracranial Hypertension and Superior Semicircular Canal Dehiscence

Purpose: The etiology of superior semicircular canal dehiscence (SSCD) is not yet clear; however, several theories implicate cumulative erosive changes. Here, using thin-section temporal bone CT images, we aimed to investigate the relationship between the bone roof thickness overlying the SSC and idiopathic intracranial hypertension (IIH) which may cause repetitive microtrauma. Materıals and methods: Between January 2017-December 2019, temporal bone CT scans from 18 patients with a diagnosis of IIH, and 21 age- and sex-matched controls were analyzed retrospectively. Multiplanar reformatted images (Pöschl and Stenver) were reconstructed from the scans and the roof thickness of the SSC bone was measured and SSCD was classified in 4 grades. Results: A total of 78 thin-section temporal bone CT scans were evaluated. Although grade-II dehiscence was most frequent in both groups, grade-IV was only observed in both temporal bones of one patient in the IIH group. Grade-III and grade-IV dehiscence rates were significantly higher in the IIH group (p=0.006). There was a significant difference between IIH and control groups in terms of SSC bone roof thickness (p=0.03). Conclusıon: Increased CSF pressure in patients with IIH may cause chronic, progressive, and irreversible damage to the bone that covers the superior semicircular canal, and may play a role as a predisposing factor for SSCD.

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

To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease severity in patients with emphysema. 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). 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. The assessed AI-prototypes can predict the disease severity in patients with emphysema with the same predictive value as the radiologists.

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

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

Significance of Brain Imaging for Staging in Patients With Clinical Stage T1-2 N0 Non–Small-Cell Lung Cancer on Positron Emission Tomography/Computed Tomography

BackgroundRoutine positron emission tomography/computed tomography (PET/CT) has been recommended even for clinical stage I non–small-cell lung cancer (NSCLC). In spite of the progress in the screening procedure, and revisions to TNM classification, there is no evidence to support brain imaging screening of patients assessed with the current staging protocol including PET/CT. Materials and MethodsWe retrospectively investigated the frequency of extrathoracic metastasis in 466 consecutive patients with clinical stage T1-2 N0 NSCLC with the complete staging assessment comprised of thin-section CT, PET/CT, and brain contrast-enhanced magnetic resonance imaging between 2008 and 2016. All patients were reclassified according to the eighth edition of the tumor, node, and metastasis (TNM) classification. ResultsAmong all patients, 70% of the tumors were pure solid and 30% had part-solid ground-glass opacity on thin-section CT, and 388 (83%) and 78 (17%) were classified into clinical stages T1 and T2, respectively. Eight patients (1.7%) had extrathoracic metastasis, including 3 (0.6%) with brain metastasis, and all showed pure-solid tumors. The frequency of extrathoracic and brain metastasis was 1.0% and 0.5% in 388 T1 patients, and 5.0% and 3.0% in 78 T2 patients. Although brain metastases were detected in 2 of 7 patients (29%) with PET/CT detectable extrathoracic metastases and 1 of 459 patients (0.2%) without PET/CT detectable extrathoracic metastasis, there were no neurologically asymptomatic brain metastases in patients with early-stage NSCLC confirmed by PET/CT. ConclusionRoutine screening of brain imaging is unnecessary in patients with early-stage NSCLC, assessed with the current staging protocol including PET/CT.

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

PurposeOptimization 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.MethodsWith 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 (CTDIvol) 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).ResultsThe 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 CTDIvol 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, CTDIvol and DLP across the four facilities (p < 0.001).ConclusionsMis-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.

Noise Reduction Effect of Deep-learning-based Image Reconstruction Algorithms in Thin-section Chest CT

To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms. The chest CT scan raw data of 47 patients were included in this study. Images of 0.625 mm were reconstructed using six reconstruction methods, including FBP, ASIR hybrid reconstruction (ASIR50%, ASIR70%), and deep learning low, medium and high modes (DL-L, DL-M, and DL-H). After the regions of interest were outlined in the aorta, skeletal muscle and lung tissue of each group of images, the CT values, SD values and signal-to-noise ratio (SNR) of the regions of interest were measured, and two radiologists evaluated the image quality. CT values, SD values and SNR of the images obtained by the six reconstruction methods showed statistically significant difference ( P<0.001). There were statistically significant differences in the image quality scores of the six reconstruction methods ( P<0.001). Images reconstruced with DL-H have the lowest noise and the highest overall quality score. The model based on deep learning can effectively reduce the noise of thin-section chest CT images and improve the image quality. Among the three deep-learning models, DL-H showed the best noise reduction effect.

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.