CT-derived assessment of regional ventilation is possible with paired inspiration and expiration scans. Normative reference values for CT-derived ventilation remain undefined. The aim of this study is to establish reference values for pulmonary ventilation using CT, with pulmonary function tests (PFTs) as the clinical standard. In this prospective, single-center study (December 2022–April 2024), 103 healthy adults underwent spirometry-guided inspiratory and expiratory CT. Lobes were segmented automatically using TotalSegmentator. Voxel-wise ventilation was quantified as the relative air volume change between inspiration and expiration via nonlinear registration, normalized to inspiratory lung volume. CT-derived lung volumes were compared with PFT-derived total lung capacity (TLC), residual volume (RV), and vital capacity (VC). Reference intervals were reported as mean ± standard deviation and 5th–95th percentiles. Multivariable analyses assessed the effects of sex, age, height, lung region, and gravitational orientation. Ninety-one participants (mean age, 53 ± 12 years; 49 men) were included. Mean ventilation was 59.5

To evaluate whether distribution measures of CT-based attenuation histograms in inspiration and expiration can indicate alveolar collapse and serve as a predictive marker in patients with idiopathic pulmonary fibrosis (IPF). This single-center retrospective longitudinal study analyzed CT scans of IPF patients in inspiration and expiration. The patient population was divided into two subgroups based on their status 3 years after baseline CT (death or transplantation versus clinical surveillance). Attenuation histograms in inspiration and expiration were created and analyzed. A Mann-Whitney U test was conducted to assess the difference of CT-derived histogram measures (including skewness) between the two subgroups. Logistic regression was applied to model the ability to distinguish between subgroups using baseline forced vital capacity (FVC%) and CT-derived histogram measures. The study included 66 patients (mean age 69.5 ± 10.9 years, 58 males). After the individual three-year observation period, 37 patients were still alive while 29 had either died or received a transplantation. The two patient subgroups were significantly different in terms of all CT-derived histogram measures and the baseline FVC%. A logistic regression model that only included the CT-derived histogram measure skewness had a better predictive performance (AUC = 0.793, 95% CI = 0.685-0.900) compared to the FVC% model alone (0.708, 0.581-0.836). Whereas further evaluation is needed, paired inspiratory/expiratory attenuation histogram analysis offers a promising approach as a prognostic imaging marker to improve outcome prediction and assess alveolar collapse in IPF.

Abstract Objective Photon-counting CT (PCCT) combines improved dose efficiency with spectral imaging, enabling dynamic functional imaging at chest CT dose levels. Dual energy CT typically uses perfused blood volume (PBV) as a static perfusion surrogate. This study compared low-dose dynamic PCCT compared with reference-dose PCCT and static PBV imaging. Materials and methods Six minipigs with left lung transplants underwent dynamic perfusion imaging using PCCT at reference and low-dose settings, along with a static PBV scan. Perfusion metrics—Blood Flow Deconvolution (BFD), Mean Transit Time Deconvolution (MTTD), Flow Extraction Product (FEP), and Time to Start Deconvolution (TTSD)—were normalized and analyzed across six lung regions using Kruskal-Wallis tests and Bland-Altman analysis. Results Low-dose and reference-dose dynamic PCCT showed strong agreement across perfusion parameters (BVP bias: 0.03; BVD bias: 0.04), with no significant differences in BVP (p = 0.995) or BVD (p = 0.374). Kinetic metrics were stable across dose levels (all p > 0.2). While low-dose imaging showed slightly greater perfusion heterogeneity, BVP remained robust. Static PBV differed significantly from dynamic BVP (reference dose: p < 0.001; low-dose: p = 0.04). Left-right perfusion differences were detected in two animals by all methods. Estimated doses were 2.37 mSv (reference-dose) and 1.36 mSv (low-dose), comparable to chest CT (1.49 mSv) and below conventional CT perfusion (3–10 mSv). Conclusion Dynamic PCCT enables quantitative lung perfusion imaging at radiation doses comparable to standard chest CT. Low-dose dynamic PCCT shows strong agreement with reference-dose acquisitions, while dynamic parameters reveal functional differences not captured by static PBV imaging. Relevance statement Dynamic low-dose photon-counting computed tomography enables lung perfusion quantification at radiation doses comparable to standard chest CT, facilitating dose-efficient functional imaging in pulmonary disease. Key Points Low-dose PCCT (~ 1.36 mSv) is feasible, comparable to single chest CT (1.49 mSv). Strong agreement was seen between low- and reference-dose PCCT (BVP bias 0.03; BVD bias 0.04). Kinetic perfusion metrics remained stable across dose levels (all p > 0.2). Graphical Abstract

A screening-trained deep learning (DL) model (DL1) for pulmonary nodule malignancy probability estimation on CT previously demonstrated good discrimination on a single-centre dataset of incidental nodules. An updated DL model (DL2) was trained on both screening and clinical data. We aimed to test the performance of both models in a multicentre dataset of incidental nodules. A retrospective, multicentre, case-control dataset of incidental nodules was collected, sampled across size buckets (5–10 mm, 10–15 mm, 15–30 mm), aiming for 10 malignant and 20 benign nodules per bucket and centre, resulting in 270 nodules. Performance was assessed using AUCs and specificity at a fixed sensitivity. AUCs were compared using the DeLong method. Both DL models were compared with the Brock model. Consistent discrimination was investigated by centre-stratified analyses. The multicentre dataset contained 269 nodules (89 malignant) from 231 patients. DL1 and DL2 achieved AUCs of 0.74 and 0.72, respectively, versus 0.63 for Brock (both p < 0.01). Using a 10

Purpose To compare the performance of an artificial intelligence (AI) system with that of radiologists for estimating malignancy risk of indeterminate-size nodules (5-15 mm) at low-dose CT (LDCT) within a standardized and transparent evaluation framework. Materials and Methods Teams participating in the AI study had access to a public dataset of 555 malignant and 5608 benign nodules on 4069 baseline LDCT scans from the National Lung Screening Trial to develop AI systems. External testing was performed on 156 malignant and 312 benign size-matched nodules, all of indeterminate size, from 463 baseline scans collected from three large European lung cancer screening trials, and the best-performing AI system (based on area under the receiver operating characteristic curve [AUC]) was selected. An observer study was conducted in which radiologists assessed 300 randomly selected nodules (100 malignant, 200 benign) from the external test set. Radiologists categorized nodules as low, intermediate, or high risk, and the threshold of intermediate or greater risk (intermediate or high-risk) was used to define a positive test result. The selected AI system was compared with radiologists on this subset using the AUC. Results The selected AI system demonstrated superior performance to the 65 radiologists' mean (AUC, 0.78 [95% CI: 0.73, 0.84] vs 0.70 [95% CI: 0.65, 0.74]; P = .001). With use of the intermediate risk or greater threshold, the AI system correctly classified 12% more malignant nodules at matched specificity and yielded 20% fewer false-positive results at matched sensitivity. Conclusion The selected AI system was superior to radiologists in estimating malignancy risk of indeterminate lung nodules at LDCT. Keywords: CT, Thorax, Lung, Observer Performance, Screening, Supervised Learning, Lung Cancer Screening, Radiologists, Artificial Intelligence, Benchmarking, Pulmonary Nodule Malignancy Risk, Deep Learning Supplemental material is available for this article. © RSNA, 2026 See also commentary by Júdice de Mattos Farina and Szarf in this issue.

Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repeatability of AI-based measurements of incidental findings using short-term repeat CT scan pairs. AI-Rad Companion Chest CT software was applied to low-dose non-contrast CT scans from the NELSON lung cancer screening trial to measure aorta diameters, coronary artery calcium volume (CACV), vertebral height and radiodensity, and emphysema (low attenuation area percentage, LAA

ANCA-assoziierte Vaskulitiden (AAV) wie Granulomatose mit Polyangiitis (GPA), eosinophile Granulomatose mit Polyangiitis (EGPA) und mikroskopische Polyangiitis (MPA) sind seltene, potenziell lebensbedrohliche Autoimmunerkrankungen mit oft pulmonaler Manifestation. Die Differenzierung ist aufgrund überlappender Symptome und Bildbefunde herausfordernd. Die hochauflösende Computertomographie (HRCT) ist das zentrale Verfahren zur Detektion pulmonaler Manifestationen wie Noduli, Milchglastrübungen und Konsolidierungen. Auch Röntgenaufnahmen und die und kontrastmittelverstärkte CT finden Anwendung. Neben der Standard-HRCT gewinnen Low-dose-CT, Perfusions-CT und strukturierte Verlaufsvergleiche an Bedeutung. Diese ermöglichen eine verbesserte Detektion diffuser alveolärer Hämorrhagien (DAH) und granulomatöser Veränderungen. Die HRCT zeigt bei pulmonaler Beteiligung eine Sensitivität von bis zu 90

Early detection of lung cancer through low-dose CT lung cancer screening in a high-risk population has proven to reduce lung cancer-specific mortality. Nodule management plays a pivotal role in early detection and further diagnostic approaches. The European Society of Thoracic Imaging (ESTI) has established a nodule management recommendation to improve the handling of pulmonary nodules detected during screening. For solid nodules, the primary method for assessing the likelihood of malignancy is to monitor nodule growth using volumetry software. For subsolid nodules, the aggressiveness is determined by measuring the solid part. The ESTI-recommendation enhances existing protocols but puts a stronger focus on lesion aggressiveness. The main goals are to minimise the overall number of follow-up examinations while preventing the risk of a major stage shift and reducing the risk of overtreatment. Question Assessment of nodule growth and management according to guidelines is essential in lung cancer screening. Findings Assessment of nodule aggressiveness defines follow-up in lung cancer screening. Clinical relevance The ESTI nodule management recommendation aims to reduce follow-up examinations while preventing major stage shift and overtreatment.

The European Society of Thoracic Imaging (ESTI) nodule management recommendation for lung cancer screening with low-dose CT builds on existing nodule management guidelines but puts a stronger focus on lesion aggressiveness and measurement error. Key objectives included finding a compromise between the overall number of follow-up examinations, avoiding a major stage shift, and reducing the risk for overtreatment. Nodule management categories at baseline are chosen depending on the size of a solid nodule or the solid component of a subsolid or cystic nodule, with suspicious morphology upgrading risk to the next higher category. Higher risk categories mandate shorter follow-up times or diagnostic workup. Volume is the preferred size measure, with diameter measurements as a fallback if segmentation for volumetry is inaccurate at visual control. Nodule aggressiveness at follow-up is estimated from growth rate, calculated as volume doubling time (VDT), or yearly diameter change. Calculation of growth rate, however, is strongly affected by measurement variability, with large error margins for short follow-up and slower growing lesions. Growth thresholds were therefore set so that rapidly growing lesions can be identified while still small, while unnecessary workups for benign or slow-growing lesions could be kept low. New lesions that are retrospectively visible on earlier scans are managed according to their growth rate. New nodules not visible on earlier scans are followed after 3 months if they have a volume of ≥ 30 mm3. Question This work strives to reduce follow-up examinations while preventing major stage shift and overtreatment. It provides nodule management based on estimated nodule aggressiveness. Findings Calculation of the growth rate of pulmonary nodules is strongly affected by measurement variability, with large error margins for short follow-up and slower growing lesions. Clinical relevance Growth thresholds that trigger management are adjusted to the follow-up time so that rapidly growing lesions can be identified while still being small while unnecessary workups for benign or slow-growing lesions can be reduced.

Radiologists are witnessing astonishing innovation and advancement of CT technologies and their clinical applications. This review highlights how photon-counting CT (PCCT), upright CT, and artificial intelligence (AI) may impact cardiothoracic CT applications for imaging and diagnosis. PCCT relies on new detectors that can bin the separate photon energies and allow for lower radiation dose and better spatial resolution. The clinical applications of PCCT in the coronary arteries are becoming the new standard for cardiac CT imaging. New upright CT has shown the benefits of imaging in the upright position and offers new insight into how the upright position affects biomechanics and physiology. Four-dimensional CT, which can be used to directly image perfusion, is challenging MRI and MR angiography for primacy in this area. The burgeoning role of AI and informatics is changing the way radiologists interpret and report many imaging examinations. The future is bright and promises lower radiation and intravenous contrast agent doses and higher spatial resolution, and will further incorporate deep learning to improve the effectiveness of CT.

CLINICAL/METHODICAL ISSUE:Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) such as granulomatosis with polyangiitis (GPA), eosinophilic granulomatosis with polyangiitis (EGPA), and microscopic polyangiitis (MPA) are rare autoimmune diseases that frequently involve the lungs and may present with life-threatening complications. Their differentiation can be challenging due to overlapping clinical and radiological findings. STANDARD RADIOLOGICAL METHODS:High-resolution computed tomography (HRCT) is the key imaging modality for detecting pulmonary changes such as nodules, ground-glass opacities, and consolidations. Chest X‑ray and contrast-enhanced computed tomography (CT) are also used. METHODICAL INNOVATIONS:Newer techniques such as low-dose CT, perfusion CT, and structured serial imaging offer improved detection of diffuse alveolar hemorrhage (DAH), granulomatous inflammation, and airway involvement. PERFORMANCE:HRCT achieves sensitivities up to 90% in pulmonary AAV manifestations like DAH. Specificity remains limited, requiring integration of clinical and serological findings. Advanced methods enhance lesion characterization and assessment of disease activity. ACHIEVEMENTS:Radiology enables early identification of distinct pulmonary patterns (e.g., cavitary nodules in GPA, peripheral migratory consolidations in EGPA, perihilar DAH in MPA). Although not pathognomonic, imaging is highly valuable when interpreted in context. PRACTICAL RECOMMENDATIONS:Diagnostic work-up is best conducted in interdisciplinary centers. HRCT should be performed in suspected pulmonary AAV. Contrast-enhanced CT is useful in suspected hemorrhage or necrosis. Radiological interpretation must be combined with clinical data, ANCA serology, and potentially histology.

Abstract This chapter provides a review of pulmonary manifestations associated with systemic diseases, particularly focusing on sarcoidosis, connective tissue diseases (CTDs), and vasculitis. The role of imaging, especially high-resolution computed tomography (HRCT), is highlighted as a key tool in diagnosing and managing these conditions. Pulmonary sarcoidosis, the most common manifestation in systemic diseases, presents varied CT findings from typical perilymphatic nodules to complex fibrotic changes, with prognosis often related to the extent of pulmonary involvement. The chapter also covers CTD-related lung disease, including conditions such as rheumatoid arthritis, systemic sclerosis, and polymyositis/dermatomyositis, where the extent and type of lung involvement often dictate prognosis. It emphasizes that while imaging findings can suggest CTD, diagnosis is primarily reliant on serological and clinical criteria, highlighting the multidisciplinary approach needed for accurate diagnosis and management. Furthermore, pulmonary vasculitis, including ANCA-associated vasculitis and Goodpasture’s syndrome, presents with distinct imaging features that may guide treatment decisions. The chapter underscores the importance of early detection and consistent monitoring of lung disease in patients with systemic conditions to improve patient outcomes, with specific attention to the prognostic value of imaging patterns in these diseases.

To test the performance of a DL model developed and validated for screen-detected pulmonary nodules on incidental nodules detected in a clinical setting. A retrospective dataset of incidental pulmonary nodules sized 5–15 mm was collected, and a subset of size-matched solid nodules was selected. The performance of the DL model was compared to the Brock model. AUCs with 95

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.

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article. See also the editorials by Bhalla and Powell in this issue.

Background Multiple commercial artificial intelligence (AI) products exist for assessing radiographs; however, comparable performance data for these algorithms are limited. Purpose To perform an independent, stand-alone validation of commercially available AI products for bone age prediction based on hand radiographs and lung nodule detection on chest radiographs. Materials and Methods This retrospective study was carried out as part of Project AIR. Nine of 17 eligible AI products were validated on data from seven Dutch hospitals. For bone age prediction, the root mean square error (RMSE) and Pearson correlation coefficient were computed. The reference standard was set by three to five expert readers. For lung nodule detection, the area under the receiver operating characteristic curve (AUC) was computed. The reference standard was set by a chest radiologist based on CT. Randomized subsets of hand (n = 95) and chest (n = 140) radiographs were read by 14 and 17 human readers, respectively, with varying experience. Results Two bone age prediction algorithms were tested on hand radiographs (from January 2017 to January 2022) in 326 patients (mean age, 10 years ± 4 [SD]; 173 female patients) and correlated strongly with the reference standard (r = 0.99; P < .001 for both). No difference in RMSE was observed between algorithms (0.63 years [95% CI: 0.58, 0.69] and 0.57 years [95% CI: 0.52, 0.61]) and readers (0.68 years [95% CI: 0.64, 0.73]). Seven lung nodule detection algorithms were validated on chest radiographs (from January 2012 to May 2022) in 386 patients (mean age, 64 years ± 11; 223 male patients). Compared with readers (mean AUC, 0.81 [95% CI: 0.77, 0.85]), four algorithms performed better (AUC range, 0.86-0.93; P value range, <.001 to .04). Conclusions Compared with human readers, four AI algorithms for detecting lung nodules on chest radiographs showed improved performance, whereas the remaining algorithms tested showed no evidence of a difference in performance. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Omoumi and Richiardi in this issue.

OBJECTIVE:To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. METHODS AND MATERIALS:In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty. RESULTS:The DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group. CONCLUSION:The integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance. CLINICAL RELEVANCE STATEMENT:Deep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction. KEY POINTS:• Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm. • Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse. • Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness.

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15–20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40–50%.