Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists.

Purpose: To determine whether deep learning algorithms developed in a public competition could identify lung cancer on low-dose CT scans with a performance similar to that of radiologists.

Materials And Methods: In this retrospective study, a dataset consisting of 300 patient scans was used for model assessment; 150 patient scans were from the competition set and 150 were from an independent dataset. Both test datasets contained 50 cancer-positive scans and 100 cancer-negative scans.

CT-Detected Subsolid Nodules: A Predictor of Lung Cancer Development at Another Location?

The purpose of this case-cohort study was to investigate whether the frequency and computed tomography (CT) features of pulmonary nodules posed a risk for the future development of lung cancer (LC) at a different location. Patients scanned between 2004 and 2012 at two Dutch academic hospitals were cross-linked with the Dutch Cancer Registry. All patients who were diagnosed with LC by 2014 and a random selection of LC-free patients were considered.

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

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected -between 2002 and 2004 from the National Lung Screening Trial.

Brock malignancy risk calculator for pulmonary nodules: validation outside a lung cancer screening population.

Objective: To assess the performance of the Brock malignancy risk model for pulmonary nodules detected in routine clinical setting.

Methods: In two academic centres in the Netherlands, we established a list of patients aged ≥40 years who received a chest CT scan between 2004 and 2012, resulting in 16 850 and 23 454 eligible subjects. Subsequent diagnosis of lung cancer until the end of 2014 was established through linking with the National Cancer Registry.

growth of 60 non-screening detected lung cancers: a computed tomography study.

Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.

Visual discrimination of screen-detected persistent from transient subsolid nodules: An observer study.

Purpose: 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.

Materials And Methods: 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).

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

Subsolid pulmonary nodules are commonly encountered in lung cancer screening and clinical routine. Compared to other nodule types, subsolid nodules are associated with a higher malignancy probability for which the size and mass of the nodule and solid core are important indicators. However, reliably measuring these characteristics on computed tomography (CT) can be hampered by the presence of vessels encompassed by the nodule, since vessels have similar CT attenuation as solid cores.

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.

Malignancy estimation of Lung-RADS criteria for subsolid nodules on CT: accuracy of low and high risk spectrum when using NLST nodules.

Purpose: Lung-RADS proposes malignancy probabilities for categories 2 (<1%) and 4B (>15%). The purpose of this study was to quantify and compare malignancy rates for Lung-RADS 2 and 4B subsolid nodules (SSNs) on a nodule base.

Methods: We identified all baseline SSNs eligible for Lung-RADS 2 and 4B in the National Lung Screening Trial (NLST) database.

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.

Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?

Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and Methods SSNs on all baseline computed tomographic (CT) scans from the National Lung Cancer Trial that would have been classified as Lung-RADS category 3 or higher were identified, resulting in 374 SSNs for analysis. An experienced screening radiologist volumetrically segmented all solid cores and located all malignant SSNs visible on baseline scans.

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.

Fleischner recommendations for the management of subsolid pulmonary nodules: high awareness but limited conformance - a survey study.

Objectives: The aim of this study was to assess awareness and conformance to the Fleischner society recommendations for the management of subsolid pulmonary nodules (SSN) in clinical practice.

Methods: An online questionnaire with four imaging cases was sent to 1579 associates from the European Respiratory Society and 757 from the European Society of Thoracic Imaging. Each respondent was asked to choose from several options which one they thought was the indicated management for the nodule presented.

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.

Impact on Patient Safety and Satisfaction of Implementation of an Outpatient Clinic in Interventional Radiology (IPSIPOLI-Study): A Quasi-Experimental Prospective Study.

Purpose: Interventional radiology (IR) procedures are associated with high rates of preparation and planning errors. In many centers, pre-procedural consultation and screening of patients is performed by referring physicians. Interventional radiologists have better knowledge about procedure details and risks, but often only get acquainted with the patient in the procedure room.

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.