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

Histological characteristics of singleton placentas delivered before the 28th week of gestation

The placenta is a record of the fetal environment and its examination may provide information about the baby’s subsequent growth and development. We describe the histological characteristics of 947 singleton placentas from infants born between 23 and 27 weeks gestation

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Association of varicosities and concomitant deep venous thrombosis in patients with superficial venous thrombosis, a systematic review

BACKGROUND: In patients with superficial venous thrombosis (SVT) co-existence of deep venous thrombosis (DVT) can be present. Varicosities are considered as a risk factor for both SVT and DVT separately. However, current evidence is contradictory whether varicosities are associated with an increased or reduced prevalence of concomitant DVT in patients with SVT. OBJECTIVES: To determine the diagnostic value of both presence and absence of varicosities in the detection of concomitant DVT in non-hospitalized patients with SVT. METHODS: In MEDLINE and EMBASE, a systematic search was performed to collect all published studies on this topic. The selected papers were critically appraised. By diagnostic 2 × 2 tables prior probabilities and predictive values were computed. RESULTS: Six relevant articles were identified. The prior probability of concomitant DVT in patients referred from primary care to the outpatient clinic varied between 13 and 34%. In five studies, absence of varicosities was related to a higher probability of concomitant DVT (33-44%) compared to a presence of varicosities (3-23%). The sixth study showed an inversed, non-significant association: DVT was present in 21% of patients with SVT on non-varicose veins versus in 35% of patients with SVT on varicose veins. CONCLUSION: In five out of six studies on patients with SVT in outpatient settings, absence of varicosities was related to a higher probability of concomitant DVT. Further research is needed to determine whether an assessment of varicosities in general practice could result in an improved selection of patients who require additional imaging to detect or exclude DVT

Subsolid pulmonary nodule morphology and associated patient characteristics in a routine clinical population

Objectives: To determine the presence and morphology of subsolid pulmonary nodules (SSNs) in a non-screening setting and relate them to clinical and patient characteristics. Methods: A total of 16,890 reports of clinically obtained chest CT (06/2011 to 11/2014, single-centre) were searched describing an SSN. Subjects with a visually confirmed SSN and at least two thin-slice CTs were included. Nodule volumes were measured. Progression was defined as volume increase exceeding the software interscan variation. Nodule morphology, location, and patient characteristics were evaluated. Results: Fifteen transient and 74 persistent SSNs were included (median follow-up 19.6 [8.3–36.8] months). Subjects with an SSN were slightly older than those without (62 vs. 58 years; p = 0.01), but no gender predilection was found. SSNs were mostly located in the upper lobes. Women showed significantly more often persistent lesions than men (94 % vs. 69 %; p = 0.002). Part-solid lesions were larger (1638 vs. 383 mm3; p <0.001) and more often progressive (68 % vs. 38 %; p = 0.02), compared to pure ground-glass nodules. Progressive SSNs were rare under the age of 50 years. Logistic regression analysis did not identify additional nodule parameters of future progression, apart from part-solid nature. Conclusions: This study confirms previously reported characteristics of SSNs and associated factors in a European, routine clinical population. Key Points: • SSNs in women are significantly more often persistent compared to men. • SSN persistence is not associated with age or prior malignancy. • The majority of (persistent) SSNs are located in the upper lung lobes. • A part-solid nature is associated with future nodule growth. • Progressive solitary SSNs are rare under the age of 50 years

Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules

Contains fulltext : 183711.pdf (publisher's version ) (Open Access

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Contains fulltext : 236812.pdf (Publisher’s version ) (Closed access

Incidental perifissural nodules on routine chest computed tomography : lung cancer or not?

Objectives: Perifissural nodules (PFNs) are a common finding on chest CT, and are thought to represent non-malignant lesions. However, data outside a lung cancer-screening setting are currently lacking. Methods: In a nested case-control design, out of a total cohort of 16,850 patients ≥ 40 years of age who underwent routine chest CT (2004-2012), 186 eligible subjects with incident lung cancer and 511 controls without were investigated. All non-calcified nodules ≥ 4 mm were semi-automatically annotated. Lung cancer location and subject characteristics were recorded. Results: Cases (56 % male) had a median age of 64 years (IQR 59–70). Controls (60 % male) were slightly younger (p<0.01), median age of 61 years (IQR 51–70). A total of 262/1,278 (21 %) unique non-calcified nodules represented a PFN. None of these were traced to a lung malignancy over a median follow-up of around 4.5 years. PFNs were most often located in the lower lung zones (72 %, p<0.001). Median diameter was 4.6 mm (range: 4.0–8.1), volume 51 mm3 (range: 32–278). Some showed growth rates < 400 days. Conclusions: Our data show that incidental PFNs do not represent lung cancer in a routine care, heterogeneous population. This confirms prior screening-based results. Key Points: • One-fifth of non-calcified nodules represented a perifissural nodule in our non-screening population.• PFNs fairly often show larger size, and can show interval growth.• When morphologically resembling a PFN, nodules are nearly certainly not a malignancy.• The assumed benign aetiology of PFNs seems valid outside the screening setting

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts