Diagnostic Value of Chest Computed Tomography Scan for Identification of Foreign Body Aspiration in Children: A Systematic Review and Meta-analysis.

Introduction: Foreign body aspiration (FBA) is a common, life-threatening pediatric emergency and was shown to be associated with high risk of morbidity and mortality. This systematic review and meta-analysis aimed to investigate the diagnostic value of chest computed tomography (CT) scan for identification of FBA in children. Methods: From inception to May 2024, a systematic search was carried out across multiple databases including Medline, Scopus, and Web of Science, considering published papers in English language. Quality assessment of the included studies was performed using seven domains of Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Results: The systematic literature search yielded 7203 articles. The pooled sensitivity and specificity of chest CT scan for identification of FBA were 0.99 (95% CI: 0.98-0.99) and 0.97 (95% CI: 0.96-0.98), respectively. The pooled positive likelihood ratio was 10.12 (95% CI: 4.59-22.20), and pooled negative likelihood ratio was 0.05 (95% CI: 0.02-0.1). Furthermore, the area under the summarized receiver operating characteristic (SROC) curve was 0.98. Conclusion: Our meta-analysis revealed that despite high heterogeneity, in the diagnostic characteristics of chest CT scan among studies, it has high diagnostic value in identifying FBA in suspected pediatric cases.

Clinical assessment of computed tomography for detecting ingested blister packs: A single-center retrospective study.

Objectives: Blister pack (BP) ingestion poses serious risks, such as gastrointestinal perforation, and accurate localization by computed tomography (CT) is a common practice. However, while it has been reported in vitro that CT visibility varies with the material type of BPs, there have been no reports on this variability in clinical settings. In this study, we investigated the CT detection rates of different BPs in clinical settings. Methods: This single-center retrospective study from 2010 to 2022 included patients who underwent endoscopic foreign body removal for BP ingestion. The patients were categorized into two groups for BP components, the polypropylene (PP) and the polyvinyl chloride (PVC)/polyvinylidene chloride (PVDC) groups. The primary outcome was the comparison of CT detection rates between the groups. We also evaluated whether the BPs contained tablets and analyzed their locations. Results: This study included 61 patients (15 in the PP group and 46 in the PVC/PVDC group). Detection rates were 97.8% for the PVC/PVDC group compared to 53.3% for the PP group, a significant difference (p < 0.01). No cases of BPs composed solely of PP were detected by CT. Blister packs were most commonly found in the upper thoracic esophagus. Conclusions: Even in a clinical setting, the detection rates of PVC and PVDC were higher than that of PP alone. Identifying PP without tablets has proven challenging in clinical. Considering the risk of perforation, these findings suggest that esophagogastroduodenoscopy may be necessary, even if CT detection is negative.

ESR Essentials: imaging in fibrotic lung diseases-practice recommendations by the European Society of Thoracic Imaging.

Fibrotic lung diseases (FLDs) represent a subgroup of interstitial lung diseases (ILDs), which can progress over time and carry a poor prognosis. Imaging has increased diagnostic discrimination in the evaluation of FLDs. International guidelines have stated the role of radiologists in the diagnosis and management of FLDs, in the context of the interdisciplinary discussion. Chest computed tomography (CT) with high-resolution technique is recommended to correctly recognise signs, patterns, and distribution of individual FLDs. Radiologists may be the first to recognise the presence of previously unknown interstitial lung abnormalities (ILAs) in various settings. A systematic approach to CT images may lead to a non-invasive diagnosis of FLDs. Careful comparison of serial CT exams is crucial in determining either disease progression or supervening complications. This 'Essentials' aims to provide radiologists a concise and practical approach to FLDs, focusing on CT technical requirements, pattern recognition, and assessment of disease progression and complications. Hot topics such as ILAs and progressive pulmonary fibrosis (PPF) are also discussed. KEY POINTS: Chest CT with high-resolution technique is the recommended imaging modality to diagnose pulmonary fibrosis. CT pattern recognition is central for an accurate diagnosis of fibrotic lung diseases (FLDs) by interdisciplinary discussion. Radiologists are to evaluate disease behaviour by accurately comparing serial CT scans.

The 2 stages of cartridge primer toolmark production and the implied impact of cartridge manufacturing tolerances.

Many methods of ballistic toolmark comparison rely upon comparison using 2D greyscale imaging. However, newly emerging analysis methods such as areal surface analysis now utilise an extra dimension of measurement allowing the surface heights/depths of unique toolmark features to be recorded in a densely populated (x,y,z) array for a 3D/areal quantitative comparative analysis. Due to this step change, the colloquialism in referring to the crater produced at the centre of the primer during firing as a "firing pin impression" has become a misnomer, leading some to believe that this toolmark is produced via a single process, where the critical variable is the condition of the firing pin. Furthermore, current forensic ballistic methodology relies on the microscopic differences between individual fired bullets and cartridge cases produced as a result of the manufacturing process of a particular firearm, in this case "matched toolmarks" confirm a ballistic match to a specific firearm. However, very rarely is it considered that the ammunition itself possesses minute differences produced during manufacture that could affect the ballistic match efficacy. This study examines the discharge process of conventional centrefire ammunition and concludes that the unique toolmarks upon the cartridge primer are definitively produced in two defined stages. This conclusion suggests that the factory loading and quality control tolerances of the cartridge itself should now be considered to be a more significant contributing factor to the production of cartridge primer toolmarks than has previously been accepted.

Investigating patellar motion using weight-bearing dynamic CT: normative values and morphological considerations for healthy volunteers.

BACKGROUND: Patellar instability is a well-known pathology in which kinematics can be investigated using metrics such as tibial tuberosity tracheal groove (TTTG), the bisect offset (BO), and the lateral patellar tilt (LPT). We used dynamic computed tomography (CT) to investigate the patellar motion of healthy subjects in weight-bearing conditions to provide normative values for TTTG, BO, and LPT, as well as to define whether BO and LPT are affected by the morphology of the trochlear groove. METHODS: Dynamic scanning was used to acquire images during weight-bearing in 21 adult healthy volunteers. TTTG, BO, and LPT metrics were computed between 0° and 30° of knee flexion. Sulcus angle, sulcus depth, and lateral trochlear inclination were calculated and used with the TTTG for simple linear regression models. RESULTS: All metrics gradually decreased during eccentric movement (TTTG, -6.9 mm; BO, -12.6%; LPT, -4.3°). No significant differences were observed between eccentric and concentric phases at any flexion angle for all metrics. Linear regression between kinematic metrics towards full extension showed a moderate fit between BO and TTTG (R2 0.60, ß 1.75) and BO and LPT (R2 0.59, ß 1.49), and a low fit between TTTG and LPT (R2 0.38, ß 0.53). A high impact of the TTTG distance over BO was shown in male participants (R2 0.71, ß 1.89) and patella alta individuals (R2 0.55, ß 1.91). CONCLUSION: We provided preliminary normative values of three common metrics during weight-bearing dynamic CT and showed the substantial impact of lateralisation of the patella tendon over patella displacement. RELEVANCE STATEMENT: These normative values can be used by clinicians when evaluating knee patients using TTTG, BO, and LPT metrics. The lateralisation of the patellar tendon in subjects with patella alta or in males significantly impacts the lateral displacement of the patella. KEY POINTS: Trochlear groove morphology had no substantial impact on motion prediction. The lateralisation of the patellar tendon seems a strong predictor of lateral displacement of the patella in male participants. Participants with patella alta displayed a strong fit between the patellar lateral displacement and tilt. TTTG, BO, and LPT decreased during concentric movement. Concentric and eccentric phases did not show differences for all metrics.

Low-dose high-resolution chest CT in adults with cystic fibrosis: intraindividual comparison between photon-counting and energy-integrating detector CT.

BACKGROUND: Regular disease monitoring with low-dose high-resolution (LD-HR) computed tomography (CT) scans is necessary for the clinical management of people with cystic fibrosis (pwCF). The aim of this study was to compare the image quality and radiation dose of LD-HR protocols between photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) in pwCF. METHODS: This retrospective study included 23 pwCF undergoing LD-HR chest CT with PCCT who had previously undergone LD-HR chest CT with EID-CT. An intraindividual comparison of radiation dose and image quality was conducted. The study measured the dose-length product, volumetric CT dose index, effective dose and signal-to-noise ratio (SNR). Three blinded radiologists assessed the overall image quality, image sharpness, and image noise using a 5-point Likert scale ranging from 1 (deficient) to 5 (very good) for image quality and image sharpness and from 1 (very high) to 5 (very low) for image noise. RESULTS: PCCT used approximately 42% less radiation dose than EID-CT (median effective dose 0.54 versus 0.93 mSv, p < 0.001). PCCT was consistently rated higher than EID-CT for overall image quality and image sharpness. Additionally, image noise was lower with PCCT compared to EID-CT. The average SNR of the lung parenchyma was lower with PCCT compared to EID-CT (p < 0.001). CONCLUSION: In pwCF, LD-HR chest CT protocols using PCCT scans provided significantly better image quality and reduced radiation exposure compared to EID-CT. RELEVANCE STATEMENT: In pwCF, regular follow-up could be performed through photon-counting CT instead of EID-CT, with substantial advantages in terms of both lower radiation exposure and increased image quality. KEY POINTS: Photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) were compared in 23 people with cystic fibrosis (pwCF). Image quality was rated higher for PCCT than for EID-CT. PCCT used approximately 42% less radiation dose and offered superior image quality than EID-CT.

Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images.

The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.

Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.

BACKGROUND: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice. OBJECTIVE: This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain. METHODS: A literature search was conducted in PubMed, EMBASE, Web of Science, and Scopus. Outcomes were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) or any other objective measure comparing MAR performance to uncorrected images. RESULTS: After screening, fourteen studies were selected that compared DL-based MAR-algorithms with uncorrected images. MAR-algorithms were categorised into the three domains. Thirteen MAR-algorithms showed a higher PSNR and SSIM value compared to the uncorrected images and to non-DL MAR-algorithms. One study showed statistically significant better MAR performance on clinical data compared to the uncorrected images and non-DL MAR-algorithms based on Hounsfield unit calculations. CONCLUSION: DL MAR-algorithms show promising results in reducing metal artefacts, but standardised methodologies are needed to evaluate DL-based MAR-algorithms on clinical data to improve comparability between algorithms. CLINICAL RELEVANCE STATEMENT: Recent studies highlight the effectiveness of supervised Deep Learning-based MAR-algorithms in improving CT image quality by reducing metal artefacts in the sinogram, image and dual domain. A systematic review is needed to provide an overview of newly developed algorithms.

3D Landmark scout imaging accurately assesses presence and extent of coronary calcification with lower radiation exposure.

BACKGROUND: Cardiac CT for coronary artery calcium (CAC) scoring exposes patients to 1 â€‹mSv of radiation. A new CT scout method utilizing ultra-low dose CT (3D Landmark) offers tomographic cross-sectional imaging, which provides axial images from which CAC can be estimated. The purpose of our study is to analyze the association between estimated CAC burden on 3D Landmark scout imaging vs dedicated ECG-gated CACS. METHODS: Consecutive patients over a 9-month period undergoing non-contrast ECG-gated CACS planned with 3D Landmark scout imaging were included. Extent of CAC on 3D Landmark scout imaging was scored from 0 to 3 (none, mild, moderate, severe). Agatston CACS was converted to an ordinal score from 0 to 3, corresponding to absent (0), mild (1-99), moderate (100-400), or severe (>400). Fischer's exact test, weighted kappa coefficient, and paired t-tests were used for analysis. RESULTS: Of 150 patients, 51.3% were female with mean age 49.0 â€‹± â€‹16.8 and BMI 28.6 â€‹± â€‹12.3. Sensitivity of 3D Landmark in identifying calcium was 96.2%, with specificity of 100%. There was strong interrater agreement between 3D Landmark calcium scoring and CACS, with weighted kappa coefficient 0.97 â€‹± â€‹0.01(CI 0.95-0.99). Radiation dose-length-product was significantly lower for 3D Landmark imaging vs. dedicated ECG-gated CACS (9.7 â€‹± â€‹3.6 vs 43.8 â€‹± â€‹26.4 â€‹mGy â€‹cm, p â€‹< â€‹0.001) despite longer scan length (465.0 â€‹± â€‹160.8 vs 123.0 â€‹± â€‹12.7 â€‹mm, respectively). CONCLUSION: Estimated coronary artery calcium on 3D Landmark scout images correlates strongly with Agatston CACS, demonstrating utility in assessing cardiovascular risk without introducing additional radiation or costs.

Microstructural Instability and Its Effects on Thermoelectric Properties of SnSe and Na-Doped SnSe.

Effects of thermal cycling on the microstructure and thermoelectric properties are studied for the undoped and Na-doped SnSe samples using X-ray computed tomography and property measurements. It is observed that thermal cycling causes significant cracks to develop, which decrease both the electrical and lattice thermal conductivities but do not affect the thermopower. The zT values are drastically reduced after the repeated heat treatment. It is important to account for density changes during cycling to obtain accurate values of the thermal conductivity. Even before thermal cycling, the spark-plasma sintered (SPS) samples have a significant number of microcracks. The orientation of cracks within the SPS pellets and their effect on the microstructure are influenced by the presence of a Na-rich impurity. The SnSe and Sn0.995Na0.005Se samples without the impurity develop cracks and exhibit grain growth parallel to the pellet surface, which is also the plane of the 2D SnSe layers. The Sn0.97Na0.03Se sample containing the impurity develops cracks that are orthogonal to the pellet surface. Such an orientation of cracks in Sn0.97Na0.03Se inhibits grain growth. All samples appear mechanically unstable after thermal cycling.

Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.

BACKGROUND: The intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNN), have shown promise for segmenting specific structures in medical imaging. This study aimed to train and externally validate an open-source U-net DL general model for automated segmentation of the inner ear from computed tomography (CT) scans, using quantitative and qualitative assessments. METHODS: In this multicenter study, we retrospectively collected a dataset of 271 CT scans to train an open-source U-net CNN model. An external set of 70 CT scans was used to evaluate the performance of the trained model. The model's efficacy was quantitatively assessed using the Dice similarity coefficient (DSC) and qualitatively assessed using a 4-level Likert score. For comparative analysis, manual segmentation served as the reference standard, with assessments made on both training and validation datasets, as well as stratified analysis of normal and pathological subgroups. RESULTS: The optimized model yielded a mean DSC of 0.83 and achieved a Likert score of 1 in 42% of the cases, in conjunction with a significantly reduced processing time. Nevertheless, 27% of the patients received an indeterminate Likert score of 4. Overall, the mean DSCs were notably higher in the validation dataset than in the training dataset. CONCLUSION: This study supports the external validation of an open-source U-net model for the automated segmentation of the inner ear from CT scans. RELEVANCE STATEMENT: This study optimized and assessed an open-source general deep learning model for automated segmentation of the inner ear using temporal CT scans, offering perspectives for application in clinical routine. The model weights, study datasets, and baseline model are worldwide accessible. KEY POINTS: A general open-source deep learning model was trained for CT automated inner ear segmentation. The Dice similarity coefficient was 0.83 and a Likert score of 1 was attributed to 42% of automated segmentations. The influence of scanning protocols on the model performances remains to be assessed.

Evaluating the Usability and Quality of a Clinical Mobile App for Assisting Physicians in Head Computed Tomography Scan Ordering: Mixed Methods Study.

BACKGROUND: Among the numerous factors contributing to health care providers' engagement with mobile apps, including user characteristics (eg, dexterity, anatomy, and attitude) and mobile features (eg, screen and button size), usability and quality of apps have been introduced as the most influential factors. OBJECTIVE: This study aims to investigate the usability and quality of the Head Computed Tomography Scan Appropriateness Criteria (HAC) mobile app for physicians' computed tomography scan ordering. METHODS: Our study design was primarily based on methodological triangulation by using mixed methods research involving quantitative and qualitative think-aloud usability testing, quantitative analysis of the Mobile Apps Rating Scale (MARS) for quality assessment, and debriefing across 3 phases. In total, 16 medical interns participated in quality assessment and testing usability characteristics, including efficiency, effectiveness, learnability, errors, and satisfaction with the HAC app. RESULTS: The efficiency and effectiveness of the HAC app were deemed satisfactory, with ratings of 97.8% and 96.9%, respectively. MARS assessment scale indicated the overall favorable quality score of the HAC app (82 out of 100). Scoring 4 MARS subscales, Information (73.37 out of 100) and Engagement (73.48 out of 100) had the lowest scores, while Aesthetics had the highest score (87.86 out of 100). Analysis of the items in each MARS subscale revealed that in the Engagement subscale, the lowest score of the HAC app was "customization" (63.6 out of 100). In the Functionality subscale, the HAC app's lowest value was "performance" (67.4 out of 100). Qualitative think-aloud usability testing of the HAC app found notable usability issues grouped into 8 main categories: lack of finger-friendly touch targets, poor search capabilities, input problems, inefficient data presentation and information control, unclear control and confirmation, lack of predictive capabilities, poor assistance and support, and unclear navigation logic. CONCLUSIONS: Evaluating the quality and usability of mobile apps using a mixed methods approach provides valuable information about their functionality and disadvantages. It is highly recommended to embrace a more holistic and mixed methods strategy when evaluating mobile apps, because results from a single method imperfectly reflect trustworthy and reliable information regarding the usability and quality of apps.

Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?

BACKGROUND AND PURPOSE: Computed tomography (CT) and biopsy may be insufficient for preoperative evaluation of the grade and outcome of patients with chondrosarcoma. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting histologic grade and prognosis in chondrosarcoma (CS). METHODS: A multicenter 211 (training cohort/ test cohort, 127/84) CS patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting the grade. Kaplan-Meier survival analysis was used to assess the association of the model-predicted grade with recurrence-free survival (RFS). Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and the Harrell's concordance index (C-index). RESULTS: The DLRM (AUC, 0.879; 95 % confidence interval [CI], 0.802-0.956) outperformed (z = 2.773, P=0.006) the RS (AUC, 0.715;95 % CI, 0.606-0.825) in predicting grade in the test cohort. RFS showed significant differences (log-rank test, P<0.05) between low-grade and high-grade patients stratified by DLRM. The DLRM achieved a higher C-index (0.805; 95 % CI, 0.694-0.916) than the RS (0.692, 95 % CI, 0.540-0.844) did in predicting RFS for CS patients in the test cohort. CONCLUSION: The DLRM can accurately predict the histologic grade and prognosis in CS.

To biopsy or not to biopsy: a retrospective review of presumed osteoid osteomas treated by radiofrequency ablation.

OBJECTIVE: First, to determine the frequency and spectrum of osteoid osteoma (OO)-mimicking lesions among presumed OO referred for radiofrequency ablation (RFA). Second, to compare patient sex and age, lesion location, and rates of primary treatment failure for OO based on histopathology results. MATERIALS AND METHODS: A retrospective review was performed of all first-time combined CT-guided biopsy/RFA for presumed OO at a single academic center between January 1990 and August 2023. Lesions were characterized as "biopsy-confirmed OO", "OO-mimicking", or "non-diagnostic" based on pathology results. Treatment failure was defined as residual or recurrent symptoms requiring follow-up surgery or procedural intervention. Variables of interest were compared between pathology groups using Kruskal-Wallis, Fisher's exact, and Wilcoxon rank sum tests. RESULTS: Of 643 included patients (median 18 years old, IQR: 13-24 years, 458 male), there were 445 (69.1%) biopsy-confirmed OO, 184 (28.6%) non-diagnostic lesions, and 15 (2.3%) OO-mimicking lesions. OO-mimicking lesions included chondroblastoma (n = 4), chondroma (n = 3), enchondroma (n = 2), non-ossifying fibroma (n = 2), Brodie's abscess (n = 1), eosinophilic granuloma (n = 1), fibrous dysplasia (n = 1), and unspecified carcinoma (n = 1). OO-mimicking lesions did not show male predominance (46.7% male) like biopsy-proven OO (74.1% male) (p = 0.033). Treatment failure occurred in 24 (5.4%) biopsy-confirmed OO, 8 (4.4%) non-diagnostic lesions, and 2 (13.3%) OO-mimicking lesions without a significant difference by overall biopsy result (p = 0.24) or pairwise group comparison. CONCLUSION: OO-mimicking pathology is infrequent, typically benign, but potentially malignant. OO-mimicking lesions do not exhibit male predominance. There was no significant difference in RFA treatment failure or lesion location among lesions with imaging appearances suggestive of OO. KEY POINTS: Question What is the frequency and spectrum of OO-mimicking lesions among presumed OO and what, if any, differences exist between these pathologies? Finding The study cohort included 69.1% OO, 28.6% lesions with non-diagnostic histopathology, and 2.3% OO-mimicking lesions. There was no difference in treatment failure or location among lesions. Clinical relevance Routine biopsy of presumed OO at the time of RFA identifies OO-mimicking lesions, which are rare and likely benign.

Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities.

OBJECTIVE: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. METHODS: This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. RESULTS: Radiologists' diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67-0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). CONCLUSION: Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. KEY POINTS: Question How good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? Findings Radiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevance Radiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.

Background: The incidence and mortality rate of lung cancer are the highest in the world among all malignant tumors. Accurate assessment of ground-glass nodules (GGNs) is crucial in reducing lung cancer mortality. This study aimed to explore the value of computed tomography (CT) features and quantitative parameters in predicting the invasiveness and degree of infiltration of GGNs. Methods: Lesions were classified into three groups based on pathological types: the precursor glandular lesion (PGL) group, including atypical adenomatoid hyperplasia and adenocarcinoma in situ; the minimally invasive adenocarcinoma group; and the invasive adenocarcinoma group. Quantitative and qualitative data of the nodules were compared, and receiver operating characteristic (ROC) curve analysis was performed for each quantitative parameter. Binary logistic regression analysis was used to evaluate independent predictors of GGN invasiveness. Results: There were significant differences in lesion size, morphology, nodule type, bronchial abnormality, internal vascular sign and pleural retraction among the three groups (P<0.05). There were significant differences in all CT quantitative parameters (CT attenuation value in the plain phase, CT attenuation value in the arterial phase, CT attenuation value in the venous phase, arterial phase enhancement difference, venous phase enhancement difference, arterial phase enhancement index and venous phase enhancement index) among the three groups (P<0.001). The ROC curve analysis showed that the CT attenuation value in the plain phase, CT attenuation value in each enhanced phase, enhancement difference and enhancement index had good discriminatory power. Binary logistic regression analysis revealed that nodule type and internal vascular sign were independent risk factors for GGN invasiveness. Conclusions: CT features combined with enhanced scanning and quantitative analysis have important value in predicting the invasiveness of GGNs. The type of pulmonary nodule detected on CT (pure GGN or mixed GGN) and the presence of internal vascular signs are independent risk factors for GGN invasiveness.

Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis.

PURPOSE: To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs. METHODS: Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC). RESULTS: The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement. CONCLUSION: Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.

The value of a nomogram model based on CT imaging features in differentiating duodenal gastrointestinal stromal tumors from pancreatic head neuroendocrine tumors.

OBJECTIVE: To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions. METHODS: A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ2 tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram. RESULTS: There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion long axis orientation, the relationship between tumor and CBD, shortest diameter, and the joint diagnostic model were 0.937 (0.902-0.972), 0.700(0.624-0.776), 0.717(0.631-0.802), 0.559 (0.473-0.644), 0.680 (0.603-0.758), and 0.991(0.982-0.999), respectively, with a sensitivity of 97.3% and a specificity of 93.0% for the joint diagnostic model. The nomogram model's AUC value was 0.985(0.973-0.996), with a sensitivity and specificity of 94.7% and 93.9%, respectively. The calibration curve indicated good agreement between predicted and actual risks. Decision curve analysis verified the clinical application value of the nomogram. CONCLUSION: The nomogram model based on CT imaging features effectively differentiates between duodenal GISTs and pancreatic head NENs, aiding in more precise clinical treatment decisions.

The prognostic value of lymphovascular invasion for stage I lung adenocarcinoma based on the presence of ground-glass opacity.

OBJECTIVES: There is still a debate regarding the prognostic implication of lymphovascular invasion (LVI) in stage I lung adenocarcinoma. Ground-glass opacity (GGO) on CT is known to correlate with a less invasive or lepidic component in adenocarcinoma, which may influence the strength of prognostic factors. This study aimed to explore the prognostic value of LVI in stage I lung adenocarcinoma based on the presence of GGO. MATERIALS AND METHODS: Stage I lung adenocarcinoma patients receiving lobectomy between 2010 and 2019 were retrospectively categorized as GGO-positive or GGO-negative (solid adenocarcinoma) on CT. Multivariable Cox regression analyses were performed for disease-free survival (DFS) and overall survival (OS) to evaluate the prognostic significance of pathologic LVI based on the presence of GGO. RESULTS: Of 924 patients included (mean age, 62.5 ± 9.2 years; 505 women), 525 (56.8%) exhibited GGO-positive adenocarcinoma and 116 (12.6%) were diagnosed with LVI. LVI was significantly more frequent in solid than GGO-positive adenocarcinoma (20.1% vs. 6.9%, p < 0.001). Multivariable analysis identified LVI and visceral pleural invasion (VPI) as significant prognostic factors for shorter DFS among solid adenocarcinoma patients (LVI, hazard ratio (HR): 1.89, p = 0.004; VPI, HR: 1.65, p = 0.003) but not GGO-positive patients (p = 0.76 and p = 0.87). In contrast, LVI was not a significant prognostic factor for OS in either group (p > 0.05). CONCLUSION: In stage I lung adenocarcinoma, pathologic LVI was associated with DFS only in patients with solid lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Lymphovascular invasion (LVI) significantly affects disease-free survival in solid-stage I lung adenocarcinoma patients, but not those with ground-glass opacity (GGO) adenocarcinoma. Risk stratification considering both GGO on CT and LVI may identify patients benefiting from increased surveillance. KEY POINTS: The presence of ground-glass opacity portends different prognoses for lung adenocarcinoma. In stage I lung adenocarcinoma, lymphovascular invasion (LVI) was significantly more frequent in solid adenocarcinomas than in ground-glass opacity (GGO)-positive adenocarcinomas. LVI was not associated with overall survival in patients with either solid adenocarcinomas or GGO adenocarcinomas.