Search

Abstract Background To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED). Methods In the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared. Results During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0–2) AI mark per ULDCT. Conclusion The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8×) with a high trade-off in terms of false positives (42.9×). Relevance statement AI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities. Trial registration The OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl). Key Points An AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED. AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives). AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities. AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives. Graphical Abstract

Abstract Here we present a sample of 12 massive quiescent galaxy candidates at $$z\sim 3-4$$ z ∼ 3 - 4 observed with the James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec). These galaxies were pre-selected from the Hubble Space Telescope imaging and 10 of our sources were unable to be spectroscopically confirmed by ground based spectroscopy. By combining spectroscopic data from NIRSpec with multi-wavelength imaging data from the JWST Near Infrared Camera (NIRCam), we analyse their stellar populations and their formation histories. We find that all of our galaxies classify as quiescent based on the reconstruction of their star formation histories but show a variety of quenching timescales and ages. All our galaxies are massive ( $$\sim 0.1-1.2\times 10^{11}$$ ∼ 0.1 - 1.2 × 10 11 M $$_\odot$$ ⊙ ), with masses comparable to massive galaxies in the local Universe. We find that the oldest galaxy in our sample formed $$\sim 1.0\times 10^{11}$$ ∼ 1.0 × 10 11 M $$_\odot$$ ⊙ of mass within the first few hundred million years of the Universe and has been quenched for more than a billion years by the time of observation at $$z\sim 3.2$$ z ∼ 3.2 ( $$\sim 2$$ ∼ 2 billion years after the Big Bang). Our results point to very early formation of massive galaxies requiring a high conversion rate of baryons to stars in the early Universe.

Abstract Background Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting. Method We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists. Results On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1–98.8%), 96.9% (31/32, 95% CI: 91.7–100%), and 92.0% (104/113, 95% CI: 88.5–95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6). Conclusions The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.

Abstract Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.

Personalization of treatment offers the opportunity to treat patients more effectively based on their dominant disease-specific features. The increasing number and types of treatment, and the high costs associated with these treatments, however, demand new approaches that improve patient selection while reducing treatment-associated costs to ensure sustainable healthcare. The DEDICATION-1 trial has been designed to investigate the non-inferiority of lower dosing regimens when compared to standard of care dosing regimens as a potential effective treatment cost reduction strategy to reduce costs of treatment with expensive immune checkpoint inhibitors in non-small cell lung cancer. If non-inferiority is confirmed, lower dosing regimens could be implemented for all therapeutic indications of pembrolizumab. The cost savings obtained within the trial are partly reinvested in biomarker research to improve the personalization of pembrolizumab treatment. The implementation of these biomarkers will potentially lead to additional cost savings by preventing ineffective pembrolizumab exposure, thereby further reducing the financial pressure on healthcare systems. The concepts discussed within this perspective can be applied both to other anticancer agents, as well as to treatments prescribed outside the oncology field.

We use the GLASS-JWST Early Release Science NIRCam parallel observations to provide a first view of the UV continuum properties of NIRCam/F444W selected galaxies at 4 < z < 7. By combining multiwavelength NIRCam observations, we constrain the UV continuum slope for a sample of 401 galaxies with stringent quality controls. We find that >99% of the galaxies are blue star-forming galaxies with very low levels of dust ( Av _β ∼ 0.01 ± 0.33). We find no statistically significant correlation for UV slope with redshift or UV magnitude. However, we find that in general galaxies at higher redshifts and fainter UV magnitudes have steeper UV slopes. We find a statistically significant correlation for UV slope with stellar mass, with galaxies with higher stellar mass showing shallower UV slopes. Individual fits to some of our galaxies reach the bluest UV slopes of β ∼ −3.1 allowed by stellar population models used in this analysis. Therefore, it is likely that stellar population models with a higher amount of Lyman continuum leakage, active galactic nucleus effects, and/or Population III contributions are required to accurately reproduce the rest-UV and optical properties of some of our bluest galaxies. This dust-free early view confirms that our current cosmological understanding of gradual mass + dust buildup of galaxies with cosmic time is largely accurate to describe the ∼0.7–1.5 Gyr age window of the universe. The abundance of a large population of UV faint dust-poor systems may point to a dominance of low-mass galaxies at z > 6 playing a vital role in cosmic reionization.

We study the kinematics of the interstellar medium (ISM) viewed “down the barrel” in 20 gravitationally lensed galaxies during cosmic noon ( z = 1.5–3.5). We use moderate-resolution spectra ( R ∼ 4000) from Keck’s Echellette Spectrograph and Imager and Magellan/MagE to spectrally resolve the ISM absorption in these galaxies into ∼10 independent elements and use double Gaussian fits to quantify the velocity structure of the gas. We find that the bulk motion of gas in this galaxy sample is outflowing, with average velocity centroid $\left\langle {v}_{\mathrm{cent}}\right\rangle =-141$ km s ^−1 (±111 km s ^−1 scatter) measured with respect to the systemic redshift. A total of 16 out of the 20 galaxies exhibit a clear positive skewness, with a blueshifted tail extending to ∼ −500 km s ^−1 . We examine scaling relations in outflow velocities with galaxy stellar mass and star formation rate, finding correlations consistent with a momentum-driven wind scenario. Our measured outflow velocities are also comparable to those reported for FIRE-2 and TNG50 cosmological simulations at similar redshift and galaxy properties. We also consider implications for interpreting results from lower-resolution spectra. We demonstrate that while velocity centroids are accurately recovered, the skewness, velocity width, and probes of high-velocity gas (e.g., v _95 ) are subject to large scatter and biases at lower resolution. We find that R ≳ 1700 is required for accurate results for the gas kinematics of our sample. This work represents the largest available sample of well-resolved outflow velocity structure at z > 2 and highlights the need for good spectral resolution to recover accurate properties.

Gravitational lenses can magnify distant galaxies, allowing us to discover and characterize the stellar populations of intrinsically faint, quiescent galaxies that are otherwise extremely difficult to directly observe at high redshift from ground-based telescopes. Here, we present the spectral analysis of two lensed, quiescent galaxies at z ≳ 1 discovered by the ASTRO 3D Galaxy Evolution with Lenses survey: AGEL 1323 ( M _* ∼ 10 ^11.1 M _⊙ , z = 1.016, μ ∼ 14.6) and AGEL 0014 ( M _* ∼ 10 ^11.5 M _⊙ , z = 1.374, μ ∼ 4.3). We measured the age, [Fe/H], and [Mg/Fe] of the two lensed galaxies using deep, rest-frame-optical spectra (S/N ≳40 Å ^−1 ) obtained on the Keck I telescope. The ages of AGEL 1323 and AGEL 0014 are ${5.6}_{-0.8}^{+0.8}$ Gyr and ${3.1}_{-0.3}^{+0.8}$ Gyr, respectively, indicating that most of the stars in the galaxies were formed less than 2 Gyr after the Big Bang. Compared to nearby quiescent galaxies of similar masses, the lensed galaxies have lower [Fe/H] and [Mg/H]. Surprisingly, the two galaxies have comparable [Mg/Fe] to similar-mass galaxies at lower redshifts, despite their old ages. Using a simple analytic chemical evolution model connecting the instantaneously recycled element Mg with the mass-loading factors of outflows averaged over the entire star formation history, we found that the lensed galaxies may have experienced enhanced outflows during their star formation compared to lower-redshift galaxies, which may explain why they quenched early.

Purpose One of the main pathophysiological mechanisms of chronic obstructive pulmonary disease is inflammation, which has been associated with lymphadenopathy. Intrapulmonary lymph nodes can be identified on CT as perifissural nodules (PFN). We investigated the association between the number and size of PFNs and measures of COPD severity. Materials and Methods CT images were obtained from COPDGene. 50 subjects were randomly selected per GOLD stage (0 to 4), GOLD-unclassified, and never-smoker groups and allocated to either “Healthy,” “Mild,” or “Moderate/severe” groups. 26/350 (7.4%) subjects had missing images and were excluded. Supported by computer-aided detection, a trained researcher prelocated non-calcified opacities larger than 3 mm in diameter. Included lung opacities were classified independently by two radiologists as either “PFN,” “not a PFN,” “calcified,” or “not a nodule”; disagreements were arbitrated by a third radiologist. Ordinal logistic regression was performed as the main statistical test. Results A total of 592 opacities were included in the observer study. A total of 163/592 classifications (27.5%) required arbitration. A total of 17/592 opacities (2.9%) were excluded from the analysis because they were not considered nodular, were calcified, or all three radiologists disagreed. A total of 366/575 accepted nodules (63.7%) were considered PFNs. A maximum of 10 PFNs were found in one image; 154/324 (47.5%) contained no PFNs. The number of PFNs per subject did not differ between COPD severity groups (p = 0.50). PFN short-axis diameter could significantly distinguish between the Mild and Moderate/severe groups, but not between the Healthy and Mild groups (p = 0.021). Conclusions There is no relationship between PFN count and COPD severity. There may be a weak trend of larger intrapulmonary lymph nodes among patients with more advanced stages of COPD.

We present spectroscopic confirmation of candidate strong gravitational lenses using the Keck Observatory and Very Large Telescope as part of our ASTRO 3D Galaxy Evolution with Lenses ( AGEL ) survey. We confirm that (1) search methods using convolutional neural networks (CNNs) with visual inspection successfully identify strong gravitational lenses and (2) the lenses are at higher redshifts relative to existing surveys due to the combination of deeper and higher-resolution imaging from DECam and spectroscopy spanning optical to near-infrared wavelengths. We measure 104 redshifts in 77 systems selected from a catalog in the DES and DECaLS imaging fields ( r ≤ 22 mag). Combining our results with published redshifts, we present redshifts for 68 lenses and establish that CNN-based searches are highly effective for use in future imaging surveys with a success rate of at least 88% (defined as 68/77). We report 53 strong lenses with spectroscopic redshifts for both the deflector and source ( z _src > z _defl ), and 15 lenses with a spectroscopic redshift for either the deflector ( z _defl > 0.21) or source ( z _src ≥ 1.34). For the 68 lenses, the deflectors and sources have average redshifts and standard deviations of 0.58 ± 0.14 and 1.92 ± 0.59 respectively, and corresponding redshift ranges of z _defl = 0.21–0.89 and z _src = 0.88–3.55. The AGEL systems include 41 deflectors at z _defl ≥ 0.5 that are ideal for follow-up studies to track how mass density profiles evolve with redshift. Our goal with AGEL is to spectroscopically confirm ∼100 strong gravitational lenses that can be observed from both hemispheres throughout the year. The AGEL survey is a resource for refining automated all-sky searches and addressing a range of questions in astrophysics and cosmology.

PurposeNormalized emphysema score is a protocol-robust CT biomarker of mortality. We aimed to improve mortality prediction by including the emphysema score progression rate-its change over time-into the models.Method and materialsCT scans from 6000 National Lung Screening Trial CT arm participants were included. Of these, 1810 died (445 lung cancer-specific). The remaining 4190 survivors were sampled with replacement up to 24432 to approximate the full cohort. Three overlapping subcohorts were formed which required participants to have images from specific screening rounds. Emphysema scores were obtained after resampling, normalization, and bullae cluster analysis of the original images. Base models contained solely the latest emphysema score. Progression models included emphysema score progression rate. Models were adjusted by including baseline age, sex, BMI, smoking status, smoking intensity, smoking duration, and previous COPD diagnosis. Cox proportional hazard models predicting all-cause and lung cancer mortality were compared by calculating the area under the curve per year follow-up.ResultsIn the subcohort of participants with baseline and first annual follow-up scans, the analysis was performed on 4940 participants (23227 after resampling). Area under the curve for all-cause mortality predictions of the base and progression models 6 years after baseline were 0.564 (0.564 to 0.565) and 0.569 (0.568 to 0.569) when unadjusted, and 0.704 (0.703 to 0.704) to 0.705 (0.704 to 0.705) when adjusted. The respective performances predicting lung cancer mortality were 0.638 (0.637 to 0.639) and 0.643 (0.642 to 0.644) when unadjusted, and 0.724 (0.723 to 0.725) and 0.725 (0.725 to 0.726) when adjusted.ConclusionIncluding emphysema score progression rate into risk models shows no clinically relevant improvement in mortality risk prediction. This is because scan normalization does not adjust for an overall change in lung density. Adjusting for changes in smoking behavior is likely required to make this a clinically useful measure of emphysema progression.

To evaluate whether, and to which extent, experienced radiologists are able to visually correctly differentiate transient from persistent subsolid nodules from a single CT examination alone and to determine CT morphological features to make this differentiation.We selected 86 transient and 135 persistent subsolid nodules from the National Lung Screening Trial (NLST) database. Four experienced radiologists visually assessed a predefined list of morphological features and gave a final judgment on a continuous scale (0-100). To assess observer performance, area under the receiver operating characteristic (ROC) curve was calculated. Statistical differences of morphological features between transient and persistent lesions were calculated using Chi-square. Inter-observer agreement of morphological features was evaluated by percentage agreement.Forty-nine lesions were excluded by at least 2 observers, leaving 172 lesions for analysis. On average observers were able to differentiate transient from persistent subsolid nodules ≥ 10 mm with an area under the curve of 0.75 (95% CI 0.67-0.82). Nodule type, lesion margin, presence of a well-defined border, and pleural retraction showed significant differences between transient and persistent lesions in two observers. Average pair-wise percentage agreement for these features was 81%, 64%, 47% and 89% respectively. Agreement for other morphological features varied from 53% to 95%.The visual capacity of experienced radiologists to differentiate persistent and transient subsolid nodules is moderate in subsolid nodules larger than 10 mm. Performance of the visual assessment of CT morphology alone is not sufficient to generally abandon a short-term follow-up for subsolid nodules.

The purpose of this study is to develop a computed tomography (CT) biomarker of emphysema that is robust across reconstruction settings, and evaluate its ability to predict mortality in patients at high risk for lung cancer. Data included baseline CT scans acquired between August 2002 and April 2004 from 1737 deceased subjects and 5740 surviving controls taken from the National Lung Screening Trial. Emphysema scores were computed in the original scans (origES) and after applying resampling, normalization and bullae analysis (normES). We compared the prognostic value of normES versus origES for lung cancer and all-cause mortality by computing the area under the receiver operator characteristic curve (AUC) and the net reclassification improvement (NRI) for follow-up times of 1-7 years. normES was a better predictor of mortality than origES. The 95% confidence intervals for the differences in AUC values indicated a significant difference for all-cause mortality for 2 through 6 years of follow-up, and for lung cancer mortality for 1 through 7 years of follow-up. 95% confidence intervals in NRI values showed a statistically significant improvement in classification for all-cause mortality for 2 through 7 years of follow-up, and for lung cancer mortality for 3 through 7 years of follow-up. Contrary to conventional emphysema score, our normalized emphysema score is a good predictor of all-cause and lung cancer mortality in settings where multiple CT scanners and protocols are used.

In 2009 the NW and SE flanks of Anton Dohrn Seamount were surveyed using multibeam echosounder and video ground-truthing to characterise megabenthic biological assemblages (biotopes) and assess those which clearly adhere to the definition of Vulnerable Marine Ecosystems, for use in habitat mapping. A combination of multivariate analysis of still imagery and video ground-truthing defined 13 comprehensive descriptions of biotopes that function as mapping units in an applied context. The data reveals that the NW and SE sides of Anton Dohrn Seamount (ADS) are topographically complex and harbour diverse biological assemblages, some of which agree with current definitions of 'listed' habitats of conservation concern. Ten of these biotopes could easily be considered Vulnerable Marine Ecosystems; three coral gardens, four cold-water coral reefs, two xenophyophore communities and one sponge dominated community, with remaining biotopes requiring more detailed assessment. Coral gardens were only found on positive geomorphic features, namely parasitic cones and radial ridges, found both sides of the seamount over a depth of 1311-1740 m. Two cold-water coral reefs (equivalent to summit reef) were mapped on the NW side of the seamount; Lophelia pertusa reef associated with the cliff top mounds at a depth of 747-791 m and Solenosmilia variabilis reef on a radial ridge at a depth of 1318-1351 m. Xenophyophore communities were mapped from both sides of the seamount at a depth of 1099-1770 m and were either associated with geomorphic features or were in close proximity (< 100 m) to them. The sponge dominated community was found on the steep escarpment either side of the seamount over at a depth of 854-1345 m. Multivariate diversity revealed the xenophyophore biotopes to be the least diverse, and a hard substratum biotope characterised by serpulids and the sessile holothurian, Psolus squamatus, as the most diverse.

RATIONALE: Accurate measurement of subsolid pulmonary nodules (SSN) is becoming increasingly important in the management of these nodules. SSNs were previously quantified with time-consuming manual measurements. The aim of the present study is to test the feasibility of semi-automatic SSNs measurements and to compare the results to the manual measurements. METHODS: In 33 lung cancer screening participants with 33 SSNs, the nodules were previously quantified by two observers manually. In the present study two observers quantified these nodules by using semi-automated nodule volumetry software. Nodules were quantified for effective diameter, volume and mass. The manual and semi-automatic measurements were compared using Bland-Altman plots and paired T tests. Observer agreement was calculated as an intraclass correlation coefficient. Data are presented as mean (SD). RESULTS: Semi-automated measurements were feasible in all 33 nodules. Nodule diameter, volume and mass were 11.2 (3.3) mm, 935 (691) ml and 379 (311) milligrams for observer 1 and 11.1 (3.7) mm, 986 (797) ml and 399 (344) milligrams for observer 2, respectively. Agreement between observers and within observer 1 for the semi-automatic measurements was good with an intraclass correlation coefficient >0.89. For observer 1 and observer 2, measured diameter was 8.8% and 10.3% larger (p