Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers, with survival rates influenced by a variety of factors, including early diagnosis, tumor profile, treatment regimen, and treatment response. The development of PDAC prognostic models is often compromised by incomplete clinical records that need to cover imaging, pathology, surgery, and treatment workflows. We propose an explanation-guided reconstruction framework (xRF) that combines an autoencoder with an ensemble of gradient- and perturbation-based explainability methods to identify and prioritize clinically relevant features during autoencoder training. Such a dual-module architecture ensures that the reconstruction process focuses on features most critical for downstream survival prediction instead of diluting its attention to less relevant but easy-to-reconstruct features. The framework was validated using a cohort of 1531 PDAC patients treated in the Danish Capital Region, with clinical features drawn from CT image readings, surgery and pathology protocols, and chemotherapy records. xRF was validated on four survival prediction tasks: post-diagnosis, post-metastasis, post-surgery, and post-chemotherapy survival. We conducted experiments with synthetic masking levels ranging from 10% to 80% and tested performance on unobserved but clinically meaningful features. The prediction performance of xRF drops by 1-2% for the moderate amount of missing data to 10-15% in the cases with high percentage of features missing when measured against the reference predictions without missing data. These results compare favorably against six alternative machine learning-based reconstruction algorithms.

BackgroundImaging is crucial for the detection of pancreatic diseases. Photon-counting computed tomography (PCCT) is a recent improvement in CT detector technology that may improve pancreatic imaging quality.PurposeTo compare the image quality in pancreatic imaging with PCCT versus conventional CT (EIDCT) both subjectively and quantitatively.Material and MethodsWe retrospectively identified 35 patients scanned with both EIDCT and PCCT in multiple contrast phases. Image quality over 11 peripancreatic areas was rated on a 5-point Likert scale. One reader made quantitative measurements of density and noise. Data analysis was performed using R Studio. Continuous data were compared using a paired t-test and ordinal data with a Wilcoxon signed-rank test.ResultsImage quality was rated significantly higher on PCCT for the pancreatic parenchyma in the late arterial phase (3.87 vs. 2.77; P <0.01), the pancreatic parenchyma in the portal venous phase (3.31 vs. 2.53; P <0.01), pancreatic ducts (2.88 vs. 2.62; P <0.01), superior mesenteric artery (4.10 vs. 2.74; P <0.01), coeliac axis (4.04 vs. 2.70; P <0.01), and portal vein (3.29 vs. 2.52; P <0.01). Noise levels were significantly lower with PCCT with a mean reduction of 5.8 HU across all areas. Dose-length-product was significantly reduced in both the late arterial phase (31.8%; P <0.01) and the portal venous phase (21.5%; P <0.01).ConclusionImage quality was subjectively and quantitatively significantly improved for all evaluated pancreatic and peripancreatic structures with PCCT compared to EIDCT. In addition, radiation dose was significantly reduced.

Abstract Objective Acute heart failure (AHF) is a common but underrecognized cause of dyspnea. Chest computed tomography (CT) can accurately assess pulmonary congestion, but radiologist reporting capacity may limit clinical utility. We hypothesized that an artificial intelligence (AI) model could automatically detect imaging signs of AHF and aimed to prospectively validate an AI model in an independent emergency department cohort, benchmarking its performance against radiologists and cardiologists. Materials and methods We prospectively validated a supervised machine-learning model in a single-center study of dyspneic patients undergoing low-dose, non-contrast chest CT and echocardiography. The primary analysis assessed diagnostic performance for CT-detected pulmonary congestion compatible with AHF, using radiologist-reported AHF as the reference and the area under the curve at receiver operating characteristic analysis (AUROC). Secondary analyses compared the AI model with blinded research radiologists and expert cardiologists. Results Of 234 patients (56% males), aged 74 ± 10 years (mean ± standard deviation), 61 (26%) had radiologist-reported AHF. The AI model achieved high diagnostic performance (AUROC 0.95 [95% confidence interval 0.93–0.98]), with 89% sensitivity [78–95] and 89% specificity [83–93]. At prespecified thresholds, rule-out maximized sensitivity (97% [89–100]) at the expense of specificity (74% [67–81]), whereas rule-in yielded high specificity (96% [92–98]) but lower sensitivity (66% [52–77]). In secondary analyses, the AI model achieved a median AUROC of 0.94 (range 0.91–0.96). Conclusion The AI model demonstrated high diagnostic performance for detecting AHF on chest CT in dyspneic patients. Integration into emergency workflows may support more consistent diagnosis, independent of clinician experience or time constraints. Relevance statement AI-based analysis of chest CT may enable earlier and more consistent detection of AHF, supporting timely triage and management, especially when specialist radiological expertise is limited or delayed. Key Points An AI model prospectively detected AHF on chest CT in dyspneic emergency department patients. In a prospective single-center cohort, AI achieved high diagnostic performance (AUROC 0.91–0.96), comparable to that of radiologists and cardiologists. AI-based chest CT interpretation may improve diagnostic consistency in the absence of standardized CT criteria for AHF. Graphical Abstract

BackgroundBeam-hardening artefacts and image noise are very common causes of reduced image quality with computed tomography (CT) imaging of the lower pelvis and perirenal area affecting precision of diagnoses in urogenital imaging.PurposeTo investigate whether photon-counting CT (PCCT) improves image quality and/or reduces beam-hardening artefacts in the lower pelvis and in the perirenal area compared to energy-integrating CT (EIDCT).Material and MethodsWe retrospectively identified 35 patients scanned using both EIDCT and PCCT. Four radiologists read both PCCT and EIDCT images. Readers evaluated image quality both subjectively and quantitatively over the left medial perirenal fat and the urinary bladder. Continuous data were compared with a paired t-test and ordinal data with a Wilcoxon signed rank test.ResultsImage quality ratings were higher with PCCT compared to EIDCT. Median scores for the left medial perirenal fat were 5 (interquartile range [IQR] = 4-5) for PCCT and 3 (IQR = 3-4) for EIDCT (P <0.001) and for the lower pelvis 5 (IQR = 4-5) for PCCT and 3 (IQR = 2-4) for EIDCT (P <0.001). Image noise was significantly lower in PCCT scans compared to EIDCT scans (urinary bladder 11.9 HU vs. 17.8 HU, P <0.001; left medial perirenal fat 15.6 HU vs. 22.5 HU, P <0.001). Mean dose-length-product was significantly lower in the PCCT scans with a dose reduction of reduction 21.2% (P <0.001).ConclusionBeam-hardening artefacts were considerable reduced and image noise was significantly lower with PCCT compared to EIDCT at significantly reduced radiation doses. This could have potential implications in the radiological assessment of urogenital diseases.

Die gewöhnliche interstitielle Pneumonie (UIP) ist durch die fortschreitende Lungenparenchymveränderung gekennzeichnet, die bei verschiedenen rheumatischen Autoimmunerkrankungen (ARD), einschließlich rheumatoider Arthritis und Bindegewebserkrankungen, beobachtet werden kann. Aus diagnostischer Sicht kann ein UIP-Muster im Zusammenhang mit ARD bildgebende und pathologische Merkmale aufweisen, mit denen es von dem Muster im Zusammenhang mit der idiopathischen Lungenfibrose (IPF) unterschieden werden kann, etwa das «Straight-Edge»-Zeichen in der HRCT und lymphoplasmazytäre Infiltrate bei histologischen Proben. Ein multidisziplinärer Ansatz (MDD), an dem zumindest Pneumologen, Rheumatologen und Radiologen beteiligt sind, ist für den Differenzialdiagnoseprozess von grundlegender Bedeutung. Der MDD ist jedoch auch für die Bewertung des Schweregrads, des Fortschreitens und des Ansprechens auf die Behandlung erforderlich, die auf der Kombination von Veränderungen der Symptome, Entwicklungen der Lungenfunktion und bei ausgewählten Patienten auf einer seriellen CT-Evaluierung basiert. Im Gegensatz zur IPF können bei Patienten mit ARD sowohl die funktionelle Bewertung als auch die von den Patienten berichteten Ergebnisse durch systemische Beteiligung und Komorbiditäten, einschließlich muskuloskelettaler Manifestationen der Erkrankung, beeinflusst werden. Im Hinblick auf die pharmakologische Behandlung wurden Immunsuppressiva trotz des Mangels an solider Evidenz in den meisten Fällen als Eckpfeiler der Therapie angesehen; in letzter Zeit wurden auch antifibrotische Medikamente für die Behandlung von progressiven fibrosierenden interstitiellen Lungenerkrankungen (ILD) außer IPF vorgeschlagen. Bei der ARD-ILD soll die therapeutische Wahl die Notwendigkeit der Kontrolle von systemischen und Lungenbeteiligungen mit dem Risiko unerwünschter Ereignisse durch Multimorbiditäten und -therapien in Einklang bringen. Ziel dieser Übersichtsarbeit ist es, die Definition, die radiologischen und die morphologischen Merkmale des UIP-Musters bei ARD zusammen mit den Risikofaktoren, diagnostischen Kriterien, einer prognostischen Bewertung und Überwachungs- und Behandlungsansätzen der UIP-ARD zusammenzufassen.

Introduction: Chest CT scans are increasingly used in dyspneic patients where acute heart failure (AHF) is a key differential diagnosis. Interpretation remains challenging and radiology reports are frequently delayed due to a radiologist shortage, although flagging such information for emergency physicians would have therapeutic implication. Artificial intelligence (AI) can be a complementary tool to enhance the diagnostic precision. We aim to develop an explainable AI model to detect radiological signs of AHF in chest CT with an accuracy comparable to thoracic radiologists. Methods: A single-center, retrospective study during 2016-2021 at Copenhagen University Hospital - Bispebjerg and Frederiksberg, Denmark. A Boosted Trees model was trained to predict AHF based on measurements of segmented cardiac and pulmonary structures from acute thoracic CT scans. Diagnostic labels for training and testing were extracted from radiology reports. Structures were segmented with TotalSegmentator. Shapley Additive explanations values were used to explain the impact of each measurement on the final prediction. Results: Of the 4,672 subjects, 49 Conclusion: We developed an explainable AI model with strong discriminatory performance, comparable to thoracic radiologists. The AI model's stepwise, transparent predictions may support decision-making.

To assess the prevalence of motion artifacts and the factors associated with them in a cohort of suspected stroke patients, and to determine their impact on diagnostic accuracy for both AI and radiologists. This retrospective cross-sectional study included brain MRI scans of consecutive adult suspected stroke patients from a non-comprehensive Danish stroke center between January and April 2020. An expert neuroradiologist identified acute ischemic, hemorrhagic, and space-occupying lesions as references. Two blinded radiology residents rated MRI image quality and motion artifacts. The diagnostic accuracy of a CE-marked deep learning tool was compared to that of radiology reports. Multivariate analysis examined associations between patient characteristics and motion artifacts. 775 patients (68 years ± 16, 420 female) were included. Acute ischemic, hemorrhagic, and space-occupying lesions were found in 216 (27.9

PURPOSE:To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute ischemic lesions (AIL) and examining patient, imaging, and lesion factors associated with these cases. MATERIALS AND METHODS:This single-center retrospective observational study included adults with brain MRIs for suspected stroke between January 2020 and April 2022. Diagnostic uncertain AIL were identified through reader disagreement or low certainty grading by a radiology resident, a neuroradiologist, and the original radiology report consisting of various neuroradiologists. A commercially available deep learning tool analyzing brain MRIs for AIL was evaluated to assess the impact of excluding uncertain cases on diagnostic odds ratios. Patient-related, MRI acquisition-related, and lesion-related factors were analyzed using the Wilcoxon rank sum test, χ2 test, and multiple logistic regression. The study was approved by the National Committee on Health Research Ethics. RESULTS:In 989 patients (median age 73 (IQR: 59-80), 53% female), certain AIL were found in 374 (38%), uncertain AIL in 63 (6%), and no AIL in 552 (56%). Excluding uncertain cases led to a four-fold increase in the diagnostic odds ratio (from 68 to 278), while a simulated case-control design resulted in a six-fold increase compared to the full disease spectrum (from 68 to 431). Independent factors associated with uncertain AIL were MRI artifacts, smaller lesion size, older lesion age, and infratentorial location. CONCLUSION:Excluding uncertain cases leads to a four-fold overestimation of the diagnostic odds ratio. MRI artifacts, smaller lesion size, infratentorial location, and older lesion age are associated with uncertain AIL and should be accounted for in validation studies.

PURPOSE:The introduction of optimised care bundles in emergency major abdominal surgery has reduced mortality. Core elements are fast diagnostic work-up with abdominal computed tomography (CT) and surgery without delay. Given the diagnostic challenges in patients with abdominal pain, we aimed to investigate if the addition of a full contrast-enhanced chest CT provides additional clinically significant diagnostic information in patients referred to an emergency abdominal CT. METHODS:This retrospective cohort study included patients with suspected major abdominal pathology referred to an emergency abdominal CT with complementary chest CT (extended CT) from 1 April 2020 to 9 September 2020. This population was compared to a historic cohort with a regular emergency abdominal CT. The primary outcome was chest CT findings leading to treatment or intervention during index admission. RESULTS:A total of 187 patients were scanned in the study period and compared to 170 historic controls. The two groups were comparable. The extended CT group had more clinically significant chest findings 28 (15.0%) compared to the standard group 9 (5.3%) (p = 0.002), of which pneumonia was found 18 versus 3 times, respectively. The extended CT group had more suspicious findings 22 versus 7 (p = 0.008) but did not result in significantly more out-patient referrals (8 versus 4, p = 0.38). CONCLUSION:In patients with suspected major abdominal pathology, an extended CT protocol which includes a full chest CT provides additional diagnostic information without reducing mortality.

BACKGROUND:Dyspnea is a common cause of hospitalization, posing diagnostic challenges among older adult patients with multimorbid conditions. Chest computed tomography (CT) scans are increasingly used in patients with dyspnea and offer superior diagnostic accuracy over chest radiographs but face limited use due to a shortage of radiologists. OBJECTIVE:This study aims to develop and validate artificial intelligence (AI) algorithms to enable automatic analysis of acute CT scans and provide immediate feedback on the likelihood of pneumonia, pulmonary embolism, and cardiac decompensation. This protocol will focus on cardiac decompensation. METHODS:We designed a retrospective method development and validation study. This study has been approved by the Danish National Committee on Health Research Ethics (1575037). We extracted 4672 acute chest CT scans with corresponding radiological reports from the Copenhagen University Hospital-Bispebjerg and Frederiksberg, Denmark, from 2016 to 2021. The scans will be randomly split into training (2/3) and internal validation (1/3) sets. Development of the AI algorithm involves parameter tuning and feature selection using cross validation. Internal validation uses radiological reports as the ground truth, with algorithm-specific thresholds based on true positive and negative rates of 90% or greater for heart and lung diseases. The AI models will be validated in low-dose chest CT scans from consecutive patients admitted with acute dyspnea and in coronary CT angiography scans from patients with acute coronary syndrome. RESULTS:As of August 2025, CT data extraction has been completed. Algorithm development, including image segmentation and natural language processing, is ongoing. However, for pulmonary congestion, the algorithm development has been completed. Internal and external validation are planned, with overall validation expected to conclude in 2025 and the final results to be available in 2026. CONCLUSIONS:The results are expected to enhance clinical decision-making by providing immediate, AI-driven insights from CT scans, which will be beneficial for both clinicians and patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/77030.

With the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors. Thirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up. A single target lesion at baseline and subsequent follow-up examinations were enclosed in the DCE-CT. Blood volume deconvolution (BVdecon), blood flow deconvolution (BFdecon), blood flow maximum slope (BFMax slope) and permeability were assessed using overall survival (OS) and progression-free survival (PFS) as endpoints in Kaplan Meier and Cox regression analyses. High baseline Blood Volume (BVdecon) (> 12.97 ml × 100 g−1) was associated with a favorable OS (26.7 vs 7.9 months; p = 0.050) and PFS (14.6 vs 2.5 months; p = 0.050). At early follow-up on day seven a higher relative increase in BFdecon (> 24.50

BACKGROUND:Respirable crystalline silica is a well-known cause of silicosis but may also be associated with other types of interstitial lung disease. We examined the associations between occupational exposure to respirable crystalline silica and the risk of idiopathic interstitial pneumonias, pulmonary sarcoidosis and silicosis. METHODS:The total Danish working population was followed 1977-2015. Annual individual exposure to respirable crystalline silica was estimated using a quantitative job exposure matrix. Cases were identified in the Danish National Patient Register. We conducted adjusted analyses of exposure-response relations between cumulative silica exposure and other exposure metrics and idiopathic interstitial pneumonias, pulmonary sarcoidosis and silicosis. RESULTS:Mean cumulative exposure was 125 µg/m3-years among exposed workers. We observed increasing incidence rate ratios with increasing cumulative silica exposure for idiopathic interstitial pneumonias, pulmonary sarcoidosis and silicosis. For idiopathic interstitial pneumonias and pulmonary sarcoidosis, trends per 50 µg/m3-years were 1.03 (95% CI 1.02 to 1.03) and 1.06 (95% CI 1.04 to 1.07), respectively. For silicosis, we observed the well-known exposure-response relation with a trend per 50 µg/m3-years of 1.20 (95% CI 1.17 to 1.23). CONCLUSION:This study suggests that silica inhalation may be related to pulmonary sarcoidosis and idiopathic interstitial pneumonias, though these findings may to some extent be explained by diagnostic misclassification. The observed exposure-response relations for silicosis at lower cumulative exposure levels than previously reported need to be corroborated in analyses that address the limitations of this study.

Identification and management of interstitial lung abnormalities Interstitial lung abnormalities (ILA) are incidentally observed specific CT findings in patients without clinical suspicion of interstitial lung disease (ILD). ILA with basal and peripheral predominance and features suggestive of fibrosis in more than 5% of any part of the lung should be referred for pulmonologist review. The strategy for monitoring as described in this review is based on clinical and radiological risk factors. ILA are associated with risk of progression to ILD and increased mortality. Early identification and assessment of risk factors for progression are essential to improve outcome.

Abstract Background Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians’ assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist’s assessments as reference standard. Methods This was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations. Results All clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians’ CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77–88), specificity = 70% (95%CI: 59–81), positive predictive value = 80% (95%CI: 74–84), negative predictive value = 78% (95%CI: 73–82). Conclusion This study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

Background: Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose: To estimate the proportion of unremarkable chest radiographs, where AI can correctly exclude pathology (ie, specificity) without increasing diagnostic errors. Materials and Methods: In this retrospective study, consecutive chest radiographs in unique adult patients (>= 18 years of age) were obtained January 1-12, 2020, at four Danish hospitals. Exclusion criteria included insufficient radiology reports or AI output error. Two thoracic radiologists, who were blinded to AI output, labeled chest radiographs as "remarkable" or "unremarkable" based on predefined unremarkable findings (reference standard). Radiology reports were classified similarly. A commercial AI tool was adapted to output a chest radiograph "remarkableness" probability, which was used to calculate specificity at different AI sensitivities. Chest radiographs with missed findings by AI and/or the radiology report were graded by one thoracic radiologist as critical, clinically significant, or clinically insignificant. Paired proportions were compared using the McNemar test. Results: A total of 1961 patients were included (median age, 72 years [IQR, 58-81 years]; 993 female), with one chest radiograph per patient. The reference standard labeled 1231 of 1961 chest radiographs (62.8%) as remarkable and 730 of 1961 (37.2%) as unremarkable. At 99.9%, 99.0%, and 98.0% sensitivity, the AI had a specificity of 24.5% (179 of 730 radiographs [95% CI: 21, 28]), 47.1% (344 of 730 radiographs [95% CI: 43, 51]), and 52.7% (385 of 730 radiographs [95% CI: 49, 56]), respectively. With the AI fixed to have a similar sensitivity as radiology reports (87.2%), the missed findings of AI and reports had 2.2% (27 of 1231 radiographs) and 1.1% (14 of 1231 radiographs) classified as critical (P = .01), 4.1% (51 of 1231 radiographs) and 3.6% (44 of 1231 radiographs) classified as clinically significant (P = .46), and 6.5% (80 of 1231) and 8.1% (100 of 1231) classified as clinically insignificant (P = .11), respectively. At sensitivities greater than or equal to 95.4%, the AI tool exhibited less than or equal to 1.1% critical misses. Conclusion: A commercial AI tool used off-label could correctly exclude pathology in 24.5%-52.7% of all unremarkable chest radiographs at greater than or equal to 98% sensitivity. The AI had equal or lower rates of critical misses than radiology reports at sensitivities greater than or equal to 95.4%. These results should be confirmed in a prospective study.

The diagnostic accuracy of handheld ultrasound (HHUS) devices operated by newly certified operators for pneumonia is unknown. This multicenter diagnostic accuracy study included patients prospectively suspected of pneumonia from February 2021 to February 2022 in four emergency departments. The index test was a 14-zone focused lung ultrasound (FLUS) examination, with consolidation with air bronchograms as diagnostic criteria for pneumonia. FLUS examinations were performed by newly certified operators using HHUS. The reference standard was computed tomography (CT) and expert diagnosis using all medical records. The sensitivity and specificity of FLUS and chest X-ray (CXR) were compared using McNemar’s test. Of the 324 scanned patients, 212 (65%) had pneumonia, according to the expert diagnosis. FLUS had a sensitivity of 31% (95% CI 26–36) and a specificity of 82% (95% CI 78–86) compared with the experts’ diagnosis. Compared with CT, FLUS had a sensitivity of 32% (95% CI 27–37) and specificity of 81% (95% CI 77–85). CXR had a sensitivity of 66% (95% CI 61–72) and a specificity of 76% (95% CI 71–81) compared with the experts’ diagnosis. Compared with CT, CXR had a sensitivity of 69% (95% CI 63–74) and a specificity of 68% (95% CI 62–72). Compared with the experts’ diagnosis and CT diagnosis, FLUS performed by newly certified operators using HHUS devices had a significantly lower sensitivity for pneumonia when compared to CXR (p < 0.001). FLUS had a significantly higher specificity than CXR using CT diagnosis as a reference standard (p = 0.02). HHUS exhibited low sensitivity for pneumonia when used by newly certified operators.

OBJECTIVES:The association between asbestos exposure and asbestosis in high-exposed industrial cohorts is well-known, but there is a lack of knowledge about the exposure-response relationship for asbestosis in a general working population setting. We examined the exposure-response relationship between occupational asbestos exposure and asbestosis in asbestos-exposed workers of the Danish general working population. METHODS:We followed all asbestos-exposed workers from 1979 to 2015 and identified incident cases of asbestosis using the Danish National Patient Register. Individual asbestos exposure was estimated with a quantitative job exposure matrix (SYN-JEM) from 1976 onwards and back-extrapolated to age 16 for those exposed in 1976. Exposure-response relations for cumulative exposure and other exposure metrics were analyzed using a discrete time hazard model and adjusted for potential confounders. RESULTS:The range of cumulative exposure in the population was 0.001 to 18 fibers per milliliter-year (f/ml-year). We found increasing incidence rate ratios (IRR) of asbestosis with increasing cumulative asbestos exposure with a fully adjusted IRR per 1 f/ml-years of 1.18 [95% confidence interval (CI) 1.15- -1.22]. The IRR was 1.94 (95% CI 1.53-2.47) in the highest compared to the lowest exposure tertile. We similarly observed increasing risk with increasing cumulative exposure in the inception population. CONCLUSIONS:This study found exposure-response relations between cumulative asbestos exposure and incident asbestosis in the Danish general working population with mainly low-level exposed occupations, but there is some uncertainty regarding the exposure levels.

Purpose:Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis. Approach:We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action. With the help of this mechanism, the agent can probe along the pancreatic duct centerline using the best possible navigational path. To enhance the tracing accuracy, we employ landmark-based registration, which enables the generation of a probability map of the pancreatic duct. Subsequently, we utilize a gradient-based method on the registered data to extract a probability map specifically indicating the centerline of the pancreatic duct. Results:Three datasets with a total of 115 CT images were used to evaluate the proposed method. Using image hold-out from the first two datasets, the method performance was 2.0, 4.0, and 2.1 mm measured in terms of the mean detection error, Hausdorff distance (HD), and root mean squared error (RMSE), respectively. Using the first two datasets for training and the third one for testing, the method accuracy was 2.2, 4.9, and 2.6 mm measured in terms of the mean detection error, HD, and RMSE, respectively. Conclusions:We present an algorithm for automated pancreatic duct centerline tracing using deep reinforcement learning. We observe that validation on an external dataset confirms the potential for practical utilization of the presented method.