Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database

Objectives: To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC’s four-fold double reading process. Methods: The LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system. Results: The updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study. Conclusions: On the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process

Trends in the incidence of pulmonary nodules in chest computed tomography:10-year results from two Dutch hospitals

Objective: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT. Methods: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule. Results: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017. Conclusion: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses. Clinical relevance statement: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice. Key Points: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.</p

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Distribution of emphysema in heavy smokers: Impact on pulmonary function

SummaryPurposeTo investigate impact of distribution of computed tomography (CT) emphysema on severity of airflow limitation and gas exchange impairment in current and former heavy smokers participating in a lung cancer screening trial.Materials and MethodsIn total 875 current and former heavy smokers underwent baseline low-dose CT (30mAs) in our center and spirometry and diffusion capacity testing on the same day as part of the Dutch–Belgian Lung Cancer Screening Trial (NELSON). Emphysema was quantified for 872 subjects as the number of voxels with an apparent lowered X-ray attenuation coefficient. Voxels attenuated <−950HU were categorized as representing severe emphysema (ES950), while voxels attenuated between −910HU and −950HU represented moderate emphysema (ES910). Impact of distribution on severity of pulmonary function impairment was investigated with logistic regression, adjusted for total amount of emphysema.ResultsFor ES910 an apical distribution was associated with more airflow obstruction and gas exchange impairment than a basal distribution (both p<0.01). The FEV1/FVC ratio was 1.6% (95% CI 0.42% to 2.8%) lower for apical predominance than for basal predominance, for Tlco/VA the difference was 0.12% (95% CI 0.076–0.15%). Distribution of ES950 had no impact on FEV1/FVC ratio, while an apical distribution was associated with a 0.076% (95% CI 0.038–0.11%) lower Tlco/VA (p<0.001).ConclusionIn a heavy smoking population, an apical distribution is associated with more severe gas exchange impairment than a basal distribution; for moderate emphysema it is also associated with a lower FEV1/FVC ratio. However, differences are small, and likely clinically irrelevant

Contribution of routine brain MRI to the differential diagnosis of parkinsonism: a 3-year prospective follow-up study

Various signs on routine brain MRI can help differentiate between Parkinson’s disease (PD) and the various forms of atypical parkinsonism (AP). Here, we evaluate what routine brain MRI contributes to the clinical diagnosis, in both early and advanced disease stages. We performed a prospective observational study in 113 patients with parkinsonism, but without definite diagnosis upon inclusion. At baseline, patients received a structured interview, comprehensive and standardized neurological assessment, and brain MRI. The silver standard diagnosis was made after 3 years of follow-up (PD n = 43, AP n = 57), which was based on disease progression, repeat standardized neurological examination and response to treatment. The clinical diagnosis was classified as having either ‘low certainty’ (lower than 80%) or ‘high certainty’ (80% or higher). The added diagnostic yield of baseline MRI results were then studied relative to clinical neurological evaluation at presentation, and at follow-up. Sensitivity and specificity for separating AP from PD were calculated for all potentially distinguishing MRI abnormalities described previously in the literature. MRI abnormalities showed moderate to high specificity but limited sensitivity for the diagnosis of AP. These MRI abnormalities contributed little over and above the clinically based diagnosis, except when the clinical diagnosis was uncertain. For these patients, presence of putaminal or cerebellar atrophy was particularly indicative of AP. Routine brain MRI has limited added value for differentiating between PD and AP when clinical certainty is already high, but has some diagnostic value when the clinical diagnosis is still uncertain

Computer-assisted detection of pulmonary embolism: evaluation of pulmonary CT angiograms performed in an on-call setting

Item does not contain fulltextPURPOSE: The purpose of the study was to assess the stand-alone performance of computer-assisted detection (CAD) for evaluation of pulmonary CT angiograms (CTPA) performed in an on-call setting. METHODS: In this institutional review board-approved study, we retrospectively included 292 consecutive CTPA performed during night shifts and weekends over a period of 16 months. Original reports were compared with a dedicated CAD system for pulmonary emboli (PE). A reference standard for the presence of PE was established using independent evaluation by two readers and consultation of a third experienced radiologist in discordant cases. RESULTS: Original reports had described 225 negative studies and 67 positive studies for PE. CAD found PE in seven patients originally reported as negative but identified by independent evaluation: emboli were located in segmental (n = 2) and subsegmental arteries (n = 5). The negative predictive value (NPV) of the CAD algorithm was 92% (44/48). On average there were 4.7 false positives (FP) per examination (median 2, range 0-42). In 72% of studies or=10 FP. CONCLUSION: CAD identified small emboli originally missed under clinical conditions and found 93% of the isolated subsegmental emboli. On average there were 4.7 FP per examination.1 april 201

Practices to support co-design processes: A case-study of co-designing a program for children with parents with a mental health problem in the Austrian region of Tyrol

Forms of collaborative knowledge production, such as community-academic partnerships (CAP), have been increasingly used in health care. However, instructions on how to deliver such processes are lacking. We aim to identify practice ingredients for one element within a CAP, a 6-month co-design process, during which 26 community- and 13 research-partners collaboratively designed an intervention programme for children whose parent have a mental illness. Using 22 published facilitating and hindering factors for CAP as the analytical framework, eight community-partners reflected on the activities which took place during the co-design process. From a qualitative content analysis of the data, we distilled essential practices for each CAP factor. Ten community- and eight research-partners revised the results and co-authored this article. We identified 36 practices across the 22 CAP facilitating or hindering factors. Most practices address more than one factor. Many practices relate to workshop design, facilitation methods, and relationship building. Most practices were identified for facilitating ‘trust among partners’, ‘shared visions, goals and/or missions’, ‘effective/frequent communication’, and ‘well-structured meetings’. Fewer practices were observed for ‘effective conflict resolution’, ‘positive community impact’ and for avoiding ‘excessive funding pressure/control struggles’ and ‘high burden of activities’. Co-designing a programme for mental healthcare is a challenging process that requires skills in process management and communication. We provide practice steps for delivering co-design activities. However, practitioners may have to adapt them to different cultural contexts. Further research is needed to analyse whether co-writing with community-partners results in a better research output and benefits for participants

Prospective ECG triggering reduces prosthetic heart valve-induced artefacts compared with retrospective ECG gating on 256-slice CT

Item does not contain fulltextOBJECTIVES: Multidetector computed tomography (MDCT) has diagnostic value for the evaluation of prosthetic heart valve (PHV) dysfunction but it is hampered by artefacts. We hypothesised that image acquisition using prospective triggering instead of retrospective gating would reduce artefacts related to pulsating PHV. METHODS: In a pulsatile in vitro model, a mono- and bileaflet PHV were imaged using 256 MDCT at 60, 75 and 90 beats per minute (BPM) with either retrospective gating (120 kV, 600 mAs, pitch 0.2, CTDI(vol) 39.8 mGy) or prospective triggering (120 kV, 200 mAs, CTDI(vol) 13.3 mGy). Two thresholds (>175 and <-45HU), derived from the density of surrounding structures, were used for quantification of hyper- and hypodense artefacts. Image noise and artefacts were compared between protocols. RESULTS: Prospective triggering reduced hyperdense artefacts for both valves at every BPM (P = 0.001 all comparisons). Hypodense artefacts were reduced for the monoleaflet valve at 60 (P = 0.009), 75 (P = 0.016) and 90 BPM (P = 0.001), and for the bileaflet valves at 60 (P = 0.001), 90 (P = 0.001) but not at 75 BPM (P = 0.6). Prospective triggering reduced image noise at 60 (P = 0.001) and 75 (P < 0.03) but not at 90 BPM. CONCLUSIONS: Compared with retrospective gating, prospective triggering reduced most artefacts related to pulsating PHV in vitro. KEY POINTS: * Computed tomographic images are often degraded by prosthetic heart valve-induced artefacts * Prospective triggering reduces prosthetic heart valve-induced artefacts in vitro * Artefact reduction at 90 beats per minute occurs without image noise reduction * Prospective triggering may improve CT image quality of moving hyperdense structures.1 juni 201