Imaging protocols for CT chest: A recommendation

Lung fibrosis is uncommon in primary Sjögren's disease: A retrospective analysis of computed tomography features in 77 patients

PURPOSE

The purpose of this study was to describe lung abnormalities observed on computed tomography (CT) in patients meeting the 2016 American College of Rheumatology/European League Against Rheumatism (EULAR) classification criteria for primary Sjögren's disease (pSD).

MATERIALS AND METHODS

All patients with pSD seen between January 2009 and December 2020 in the day care centre of our National Reference Center for rare systemic autoimmune diseases, who had at least one chest CT examination available for review and for whom the cumulative EULAR Sjögren's Syndrome Disease Activity Index (cumESSDAI) could be calculated were retrospectively evaluated. CT examinations were reviewed, together with clinical symptoms and pulmonary functional results.

RESULTS

Seventy-seven patients (73 women, four men) with a median age of 51 years at pSD diagnosis (age range: 17-79 years), a median follow-up time of 6 years and a median cumESSDAI of 7 were included. Sixty-six patients (86%) had anti-SSA antibodies. Thirty-three patients (33/77; 43%) had respiratory symptoms, without significant alteration in pulmonary function tests. Forty patients (40/77; 52%) had abnormal lung CT findings of whom almost half of them had no respiratory symptoms. Abnormalities on chest CT were more frequently observed in patients with anti-SSA positivity and a history of lymphoma. Air cysts (28/77; 36%) and mosaic perfusion (35/77; 35%) were the predominant abnormalities, whereas lung fibrosis was observed in five patients (5/77; 6%).

CONCLUSION

More than half of patients with pSD have abnormal CT findings, mainly air cysts and mosaic perfusion, indicative of small airways disease, whereas lung fibrosis is rare, observed in less than 10% of such patients.

Crossed raised arm position improves the flow of contrast medium in torso contrast-enhanced computed Tomography

INTRODUCTION

This retrospective cohort study examined the effects of the crossed raised arm (CRA) position in contrast-enhanced computed tomography (CECT) on contrast medium influx and image quality relative to the conventional position.

METHODS

Contrast medium influx into the collateral veins on CECT images was evaluated in 92 participants. The CT values of the pulmonary artery, descending aorta, and spleen were obtained in both positions and compared. Anatomical changes in the diameters and area of the subclavian vein and costoclavicular distance were also analyzed.

RESULTS

Contras 27 and 6 patients in the conventional and CRA positions, respectively. The influx risk ratio in the CRA position versus that in the conventional position was 0.22 (95% confidence interval, 0.10-0.51). Elevations in the median CT value of the pulmonary artery, descending aorta, and spleen in the CRA position were 7.0% (p < .001), 7.4% (p < .001), and 9.8% (p < .001), respectively. Enlargements in the major and minor diameters of the subclavian vein, subclavian vein area, and costoclavicular distance in the CRA position versus those in the conventional position were 19.3% (p < .001), 28.1% (p < .001), 53.6%, and 30.0% (p < .001), respectively.

CONCLUSION

The CRA position effectively prevented contrast medium influx into the collateral veins due to SVS and increased CT values in the target organs in CECT. The diameters and area of the subclavian vein and costoclavicular distance were enlarged at the thoracic outlet, which improved the flow of the contrast medium into the targeted organs.

IMPLICATIONS FOR PRACTICE

The CRA position can contribute to obtaining better CECT images during common clinical assessments at no additional cost.

Prediction of Surgical Outcome by Tumor Volume Doubling Time via Stereo Imaging Software in Early Non-Small Cell Lung Cancer

BACKGROUND

Volume doubling time (VDT) has been proven to be a powerful predictor of lung cancer progression. In non-small cell lung cancer patients receiving sublobar resection, the discussion of correlation between VDT and surgery was absent. We proposed to investigate the surgical outcomes according to VDT.

METHODS

We retrospectively studied 96 cases post sublobar resection from 2012 to 2018, collecting two chest CT scans preoperatively of each case and calculating the VDT. The receiver operating characteristic curve was constructed to identify the optimal cut-off point of VDTs as 133 days. We divided patients into two groups: VDT < 133 days and VDT ≥ 133 days. Univariable and multivariable analyses were performed for comparative purposes.

RESULTS

Univariable and multivariable analyses revealed that the consolidation and tumor diameter ratio was the factor of overall survival (OS), and VDT was the only factor of disease-free survival (DFS). The five year OS rates of patients with VDTs ≥ 133 days and VDTs < 133 days, respectively, were 89.9% and 71.9%, and the five year DFS rates were 95.9% and 61.5%.

CONCLUSION

As VDT serves as a powerful prognostic predictor and provides an essential role in planning surgical procedures, the evaluation of VDT preoperatively is highly suggested.

Follow-Up Imaging Guidelines for Patients with Stage III Unresectable NSCLC: Recommendations Based on the PACIFIC Trial

The PACIFIC trial showed a survival benefit with durvalumab through five years in stage III unresectable non-small cell lung cancer (NSCLC). However, optimal use of imaging to detect disease progression remains unclearly defined for this population. An expert working group convened to consider available evidence and clinical experience and develop recommendations for follow-up imaging after concurrent chemotherapy and radiation therapy (CRT). Voting on agreement was conducted anonymously via online survey. Follow-up imaging was recommended for all suitable patients after CRT completion regardless of whether durvalumab is received. Imaging should occur every 3 months in Year 1, at least every 6 months in Year 2, and at least every 12 months in Years 3–5. Contrast computed tomography was preferred; routine brain imaging was not recommended for asymptomatic patients. The medical oncologist should follow-up during Year 1 of durvalumab therapy, with radiation oncologist involvement if pneumonitis is suspected; medical and radiation oncologists can subsequently alternate follow-up. Some patients can transition to the family physician/community primary care team at the end of Year 2. In Years 1–5, patients should receive information regarding smoking cessation, comorbidity management, vaccinations, and general follow-up care. These recommendations provide guidance on follow-up imaging for patients with stage III unresectable NSCLC whether or not they receive durvalumab consolidation therapy.

High resolution propagation-based lung imaging at clinically relevant X-ray dose levels

Absorption-based clinical computed tomography (CT) is the current imaging method of choice in the diagnosis of lung diseases. Many pulmonary diseases are affecting microscopic structures of the lung, such as terminal bronchi, alveolar spaces, sublobular blood vessels or the pulmonary interstitial tissue. As spatial resolution in CT is limited by the clinically acceptable applied X-ray dose, a comprehensive diagnosis of conditions such as interstitial lung disease, idiopathic pulmonary fibrosis or the characterization of small pulmonary nodules is limited and may require additional validation by invasive lung biopsies. Propagation-based imaging (PBI) is a phase sensitive X-ray imaging technique capable of reaching high spatial resolutions at relatively low applied radiation dose levels. In this publication, we present technical refinements of PBI for the characterization of different artificial lung pathologies, mimicking clinically relevant patterns in ventilated fresh porcine lungs in a human-scale chest phantom. The combination of a very large propagation distance of 10.7 m and a photon counting detector with [Formula: see text] pixel size enabled high resolution PBI CT with significantly improved dose efficiency, measured by thermoluminescence detectors. Image quality was directly compared with state-of-the-art clinical CT. PBI with increased propagation distance was found to provide improved image quality at the same or even lower X-ray dose levels than clinical CT. By combining PBI with iodine k-edge subtraction imaging we further demonstrate that, the high quality of the calculated iodine concentration maps might be a potential tool for the analysis of lung perfusion in great detail. Our results indicate PBI to be of great value for accurate diagnosis of lung disease in patients as it allows to depict pathological lesions non-invasively at high resolution in 3D. This will especially benefit patients at high risk of complications from invasive lung biopsies such as in the setting of suspected idiopathic pulmonary fibrosis (IPF).

Considerations for Imaging of Malignant Pleural Mesothelioma: A Consensus Statement from the International Mesothelioma Interest Group

Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and reproducible assessment of disease extent at staging and follow-up imaging. By optimizing and harmonizing technical approaches to imaging MPM, the best quality imaging can be achieved for individual patient care, clinical trials, and imaging research. This consensus statement represents agreement on harmonized, standard practices for routine multimodality imaging of MPM, including radiography, computed tomography, ¹⁸F-2-deoxy-D-glucose positron emission tomography, and magnetic resonance imaging, by an international panel of experts in the field of pleural imaging assembled by the International Mesothelioma Interest Group. In addition, modality-specific technical considerations and future directions are discussed. A bulleted summary of all technical recommendations is provided.

Low Tube Voltage Chest Computed Tomography With Enhancement Using Low-Concentration Iodinated Contrast Media: Comparison of 240 mg/mL Versus 300 mg/mL Iodinated Contrast Media

Purpose: To evaluate the image quality of low voltage chest computed tomography with enhancement (CECT) using low-concentration-iodine contrast media (LCCM). Method: From 9 December to 19 December 2019, three different protocols were used for 263 patients undergoing chest CECT. Chest CECT was done using routine (300 mgI/ml contrast media with 100 kVp) protocol (group 1), LCCM (240 mgI/ml contrast media)-100 kVp protocol (group 2) and LCCM-80 kVp protocol (group 3) in 91, 97 and 75 patients, respectively. The overall diagnostic acceptability, anatomical depiction, noise and contrast-related artifacts were assessed. Additionally, the mean attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and figure of merit (FOM) in the aorta and the main pulmonary trunk were measured. Results: The overall diagnostic acceptability scores were not significantly different between groups 1 and 2 (P = .261); group 3 demonstrated significantly lower overall diagnostic acceptability score compared with group 1 (P = .011) or group 2 (P < .001). However, in CECT with iterative reconstruction (IR), the overall diagnostic acceptability scores did not show significant difference among 3 groups. Group 3 showed significantly lower effective radiation dose compared with group 1 (2.33 vs 1.22 mSv, P < .001) or group 2 (2.28 vs .22 mSv, P < .001). Conclusions: In 100 kVp chest CECT, the image quality of using 240 mg/mL iodinated contrast media is comparable to that using 300 mg/mL iodine contrast media, regardless of application of IR; with IR, chest CECT using 80 kVp and 240 mg/mL iodinated contrast media results in acceptable image quality and lower radiation dose.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

Pediatric COVID-19: Correlations between Clinical and Imaging Perspectives

Background

Although SARS-CoV-2 infection primarily affects adults, the increasing emergence of infected pediatric patients has been recently reported. However, there is a paucity of data regarding the value of imaging in relation to the clinical severity of this pandemic emergency.

Objectives

To demonstrate the relationships between clinical and radiological COVID-19 findings and to determine the most effective standardized pediatric clinical and imaging strategies predicting the disease severity. Patients and Methods. This observational study enrolled eighty pediatric patients with confirmed COVID-19 infection. The studied patients were categorized according to the disease severity and the presence of comorbidities. Patients' clinical findings, chest X-ray, and CT imaging results were analyzed. Patients' evaluations using several clinical and radiological severity scores were recorded. The relations between clinical and radiological severities were examined.

Results

Significant associations were found between severe-to-critical illness and abnormal radiological findings (p = 0.009). In addition, chest X-ray score, chest CT severity score, and rapid evaluation of anamnesis, PO2, imaging disease, and dyspnea-COVID (RAPID-COVID) score were significantly higher among patients with severe infection (p < 0.001, <0.001, and 0.001) and those with comorbidities (p = 0.005, 0.002, and <0.001).

Conclusions

Chest imaging of pediatric patients with COVID-19 infection may be of value during the evaluation of severe cases of infected pediatric patients and in those with underlying comorbid conditions, especially during the early stage of infection. Moreover, the combined use of specific clinical and radiological COVID-19 scores are likely to be a successful measure of the extent of disease severity.

Performance of 1-mm non-gated low-dose chest computed tomography using deep learning-based noise reduction for coronary artery calcium scoring

OBJECTIVE

To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)-based denoising technique.

METHODS

This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1-10, 11-100, 101-400, and > 400. We compared CACS from LDCTs with that from calcium CT.

RESULTS

Mean CACS was 104.8 ± 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 ± 249.6, p = 0.002), 1-mm LDCT with IR (94.7 ± 249.9, p < 0.001), and 3-mm LDCT (90.3 ± 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945-0.972) for DL, 0.969 (95% CI, 0.956-0.978) for IR, and 0.952 (95% CI, 0.932-0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893-0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866-0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780-0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10).

CONCLUSION

DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel.

KEY POINTS

• Deep learning (DL)-based noise reduction enables calcium scoring at 1-mm, sharp kernel reconstructed low-dose chest CT (LDCT). • Both iterative reconstruction and DL-based noise reduction underestimated calcium score, but agreement were excellent with those from calcium CT. • Accuracy of categorical classification of calcium scoring tended to be highest in 1-mm LDCT with DL compared to 1-mm LDCT with IR and 3-mm LDCT (90%, 87%, and 84.7%, p = 0.10).

Anatomical Variations in Pulmonary Fissures on Computed Tomography (CT)

Objective To determine the frequency of anatomical variations in lung fissures using computed tomography (CT) at a tertiary care hospital in Karachi, Pakistan. Methods A cross-sectional study was conducted in the department of Radiology and Imaging Services at Memon Medical Institute Hospital, Karachi, between November 2021 to April 2022. Patients aged between 15 to 92 years with a completed high-resolution CT scan chest were included. Subjects with no significant structural lung disease that could alter the anatomy were analyzed. Baseline data was gathered using a pre-designed questionnaire, and two qualified radiologists assessed the CT chest images. Results A total of 382 subjects participated in this study, out of which 57.1% were males whilst 42.9% were females. The right horizontal fissure was absent in 10 (2.6%) cases. Accessory fissures were seen in 7.33%. The most common fissural variation was azygos fissure (14; 3.7%), followed by superior accessory fissure (six; 1.6%), inferior accessory fissures (four; 1%), and left horizontal fissure (four; 1%). These variations were more common in males. The significant difference was only seen in the superior accessory fissures with respect to gender (P-value<0.05). Conclusion This study showed the presence of accessory fissures in 7.33% of patients, the most common being the azygos fissure, irrespective of gender. The absence of normal right horizontal fissures was observed in 2.6% of cases.

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction:

Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC.

Methods:

Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC.

Results:

In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively).

Conclusion:

CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.

Does Dose Matter? Ionising radiation exposure of the veterinary patient from Computed Tomography: A discussion

Advanced imaging techniques such as Computed Tomography (CT) are commonplace in human medicine and increasingly, are being utilised by the veterinary profession as scanners become more available and affordable. The benefit of CT imaging is the provision of detailed cross-sectional imaging and 3D reconstruction, but this incurs higher doses of ionising radiation to the patient. This paper reviews risks and effects associated with ionising radiation, making comparisons to human models, and a hypothetical scenario is discussed using the human risk model for age at time of exposure and increased lifetime risk of cancer with a dog to human years formula. Various issues are considered with respect to dose reduction, training, equipment and the reported 'greater need for guidance and the establishment of best practice' which may lead to future guidance from the International Commission on Radiological Protection (ICRP).

Contrast medium protocols in routine chest CT: a survey study

BACKGROUND

Administration of contrast medium (CM) is an important image quality factor in computed tomography (CT) of the chest. There is no clear evidence or guidelines on CM strategies for chest CT, thus a consensus approach is needed.

PURPOSE

To survey the potential impact on differences in chest CT protocols, with emphasis on strategies for the administration of CM.

MATERIAL AND METHODS

A total of 170 respondents were included in this survey, which used two different approaches: (i) an online survey was sent to the members of the European Society of Thoracic Imaging (ESTI); and (ii) an email requesting a copy of their CT protocol was sent to all hospitals in Norway, and university hospitals in Sweden and Denmark. The survey focused on factors affecting CM protocols and enhancement in chest CT.

RESULTS

The overall response rate was 24% (n = 170): 76% of the respondents used a CM concentration of ≥350 mgI/mL; 52% of the respondents used a fixed CM volume strategy. Fixed strategies for injection rate and delay were also the most common approach, practiced by 73% and 57% of the respondents, respectively. The fixed delay was in the range of 20-90 s. Of the respondents, 56% used flexible tube potential strategies (kV).

CONCLUSION

The chest CT protocols and CM administration strategies employed by the respondents vary widely, affecting the image quality. The results of this study underline the need for further research and consensus guidelines related to chest CT.

Inter-Observer Agreement between Low-Dose and Standard-Dose CT with Soft and Sharp Convolution Kernels in COVID-19 Pneumonia

Computed tomography (CT) has been an essential diagnostic tool during the COVID-19 pandemic. The study aimed to develop an optimal CT protocol in terms of safety and reliability. For this, we assessed the inter-observer agreement between CT and low-dose CT (LDCT) with soft and sharp kernels using a semi-quantitative severity scale in a prospective study (Moscow, Russia). Two consecutive scans with CT and LDCT were performed in a single visit. Reading was performed by ten radiologists with 3–25 years’ experience. The study included 230 patients, and statistical analysis showed LDCT with a sharp kernel as the most reliable protocol (percentage agreement 74.35 ± 43.77%), but its advantage was marginal. There was no significant correlation between radiologists’ experience and average percentage agreement for all four evaluated protocols. Regarding the radiation exposure, CTDI_vol was 3.6 ± 0.64 times lower for LDCT. In conclusion, CT and LDCT with soft and sharp reconstructions are equally reliable for COVID-19 reporting using the “CT 0-4” scale. The LDCT protocol allows for a significant decrease in radiation exposure but may be restricted by body mass index.

Aortic Arch Variants and Anomalies: Embryology, Imaging Findings, and Clinical Considerations

There is a wide spectrum of congenital anomalies or variations of the aortic arch, ranging from non-symptomatic variations that are mostly detected incidentally to clinically symptomatic variations that cause severe respiratory distress or esophageal compression. Some of these may be accompanied by other congenital heart diseases or chromosomal anomalies. The widespread use of multidetector computed tomography (CT) in clinical practice has resulted in incidental detection of several variations of the aortic arch in adults. Thus, radiologists and clinicians should be aware of the classification of aortic arch anomalies and carefully look for imaging features associated with a high risk of clinical symptoms. Understanding the embryological development of the aortic arch aids in the classification of various subtypes of aortic arch anomalies and variants. For accurate diagnosis and precise evaluation of aortic arch anomalies, cross-sectional imaging modalities, such as multidetector CT or magnetic resonance imaging, play an important role by providing three-dimensional reconstructed images. In this review, we describe the embryological development of the thoracic aorta and discuss variations and anomalies of the aortic arch along with their clinical implications.

Optimising CT-chest protocols and the added value of venous-phase contrast timing; Observational case-control

INTRODUCTION

To optimize CT chest protocol by comparing venous contrast timing with arterial timing for contrast opacification in vessels, qualitative image quality and radiologists' satisfaction and diagnostic confidence in assessing for potential nodal, pleural and pulmonary disease in general oncology outpatients.

METHOD

Matched case-control study performed following CT protocol update. 92 patients with a range of primary malignancies with 2 CT chests in a 2-year period, one with an arterial phase protocol and the second in the 60 second venous phase, were included. Contrast attenuation in aorta, pulmonary artery and liver were measured. Subjective measurements assessed perivenous artefact, confidence in nodal pleural and pulmonary assessment and presence of pulmonary emboli. Statistical analysis was performed using paired and unpaired t-tests.

RESULTS

Venous-phase CT demonstrated more consistent enhancement of the vessels, with higher attenuation of the nodes, pulmonary and pleural lesions. There was a significant reduction in perivenous beam hardening artefact on venous-phase CT (P < 0.001). Diagnostic confidence was significantly higher for nodal assessment and pleural abnormality visibility (P < 0.001) and pleural assessment (P < 0.05). There was no significant difference in pulmonary mass visibility. There was adequate enhancement to diagnose significant pulmonary emboli (PE) with 4 incidental PEs detected on the venous phase, extending to segmental vessels.

CONCLUSION

Venous-phase CT chest performs better than arterial-phase on all fronts, without compromising assessment of incidental pulmonary emboli. When intravenous contrast is indicated in a routine chest CT (excluding a CT-angiogram), the default timing should be a venous or 60s phase.

Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

BACKGROUND

The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT).

PATIENTS AND METHODS

Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic ¹⁸F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUV_max) was measured on conventional ¹⁸F-FDG PET/CT images. The influx constant (K_i ) was calculated from the dynamic ¹⁸F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test.

RESULTS

Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUV_max values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average K_i values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min^-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613).

CONCLUSIONS

Dynamic ¹⁸F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.

Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach

PURPOSE

To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).

METHODS

We develop a DL-based model to learn a compact representation of a subject, which is predictive of COPD physiologic severity and other outcomes. Our DL model learned: (a) to extract informative regional image features from HRCT; (b) to adaptively weight these features and form an aggregate patient representation; and finally, (c) to predict several COPD outcomes. The adaptive weights correspond to the regional lung contribution to the disease. We evaluate the model on 10 300 participants from the COPDGene cohort.

RESULTS

Our model was strongly predictive of spirometric obstruction ( r 2  =  0.67) and grouped 65.4% of subjects correctly and 89.1% within one stage of their GOLD severity stage. Our model achieved an accuracy of 41.7% and 52.8% in stratifying the population-based on centrilobular (5-grade) and paraseptal (3-grade) emphysema severity score, respectively. For predicting future exacerbation, combining subjects' representations from our model with their past exacerbation histories achieved an accuracy of 80.8% (area under the ROC curve of 0.73). For all-cause mortality, in Cox regression analysis, we outperformed the BODE index improving the concordance metric (ours: 0.61 vs BODE: 0.56).

CONCLUSIONS

Our model independently predicted spirometric obstruction, emphysema severity, exacerbation risk, and mortality from CT imaging alone. This method has potential applicability in both research and clinical practice.

[CT image quality assessment based on prior information of pre-restored images]

OBJECTIVE

We propose a CT IQA strategy based on the prior information of pre-restored images (PR-IQA) to improve the performance of IQA models.

OBJECTIVE

We propose a CNN-based no-reference CT IQA strategy using the prior information of image quality features in the image restoration algorithm, which is combined with the original distorted image information into the two CNNs through the pre-restored image and the residual image. Multi-information fusion was used to improve the feature extraction ability and prediction performance of CNN. We built a CT IQA dataset based on spiral CT data published by Mayo Clinic. The performance of PR- IQA was evaluated by calculating the quantitative metrics and statistical tests. The influence of different hyperparameter settings for PR-IQA was analyzed. We then compared PR-IQA with the BASELINE model based on the single CNN to evaluate the original distorted image without reference image and other eight IQA algorithms.

OBJECTIVE

The comparative experiment results showed that the PR-IQA model based on the prior information of 3 different image restoration algorithms (BF, NLM and BM3D) was better than all the tested IQA algorithms. Compared with the BASELINE method, the proposed method showed significantly improved performance, and the mean PLCC was increased by 12.56% and SROCC by 19.95%, and RMSE was decreased by 22.77%.

OBJECTIVE

The proposed PR-IQA method can make full use of the prior information of the image restoration algorithm to effectively predict the quality of CT images.

On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise

Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A deep neural network (DNN) was established to classify 3D CT images of lung nodules into malignant or benign groups. The established DNN was able to predict malignancy rate of lung nodules based on CT images, achieving the area under the curve of 0.91 for the testing dataset in a tenfold cross validation as compared to radiologists' prediction. We then evaluated its robustness against noise perturbations. We added to the input CT images noise signals generated randomly or via an optimization scheme using a realistic noise model based on a noise power spectrum for a given mAs level, and monitored the DNN's output. The results showed that the CT noise was able to affect the prediction results of the established DNN model. With random noise perturbations at 100 mAs, DNN's predictions for 11.2% of training data and 17.4% of testing data were successfully altered by at least once. The percentage increased to 23.4% and 34.3%, respectively, for optimization-based perturbations. We further evaluated robustness of models with different architectures, parameters, number of output labels, etc, and robustness concern was found in these models to different degrees. To improve model robustness, we empirically proposed an adaptive training scheme. It fine-tuned the DNN model by including perturbations in the training dataset that successfully altered the DNN's perturbations. The adaptive scheme was repeatedly performed to gradually improve DNN's robustness. The numbers of perturbations at 100 mAs affecting DNN's predictions were reduced to 10.8% for training and 21.1% for testing by the adaptive training scheme after two iterations. Our study illustrated that robustness may potentially be a concern for an exemplary DL-based lung-nodule classification model for CT images, indicating the needs for evaluating and ensuring model robustness when developing similar models. The proposed adaptive training scheme may be able to improve model robustness.

Pseudoaneurysm after total arch replacement mimicking malignant lymphadenopathy

Pseudoaneurysm should be considered in the differential diagnosis when the computed tomography (CT) findings show a mediastinal mass in patients with a history of cardiovascular surgery even if such surgery occurred over two decades previously.