Colin Jacobs
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View article: ESR Essentials: lung cancer screening with low-dose CT—practice recommendations by the European Society of Thoracic Imaging
ESR Essentials: lung cancer screening with low-dose CT—practice recommendations by the European Society of Thoracic Imaging Open
Low-dose CT screening for lung cancer reduces the risk of death from lung cancer by at least 21% in high-risk participants and should be offered to people aged between 50 and 75 with at least 20 pack-years of smoking. Iterative reconstruct…
View article: Artificial intelligence in radiology: 173 commercially available products and their scientific evidence
Artificial intelligence in radiology: 173 commercially available products and their scientific evidence Open
Objectives To assess changes in peer-reviewed evidence on commercially available radiological artificial intelligence (AI) products from 2020 to 2023, as a follow-up to a 2020 review of 100 products. Materials and methods A literature revi…
View article: Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules
Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules Open
View article: Lung cancer screening with low-dose CT: definition of positive, indeterminate, and negative screen results. A nodule management recommendation from the European Society of Thoracic Imaging
Lung cancer screening with low-dose CT: definition of positive, indeterminate, and negative screen results. A nodule management recommendation from the European Society of Thoracic Imaging Open
Early detection of lung cancer through low-dose CT lung cancer screening in a high-risk population has proven to reduce lung cancer-specific mortality. Nodule management plays a pivotal role in early detection and further diagnostic approa…
View article: Beneficial value of [<sup>18</sup>F]FDG PET/CT in the follow-up of patients with stage III non-small cell lung cancer (NVALT31-PET study): study protocol of a multicentre randomised controlled trial
Beneficial value of [<sup>18</sup>F]FDG PET/CT in the follow-up of patients with stage III non-small cell lung cancer (NVALT31-PET study): study protocol of a multicentre randomised controlled trial Open
Introduction Patients with stage III non-small cell lung cancer (NSCLC) are at high risk of developing post-treatment recurrences (50–78%) during follow-up. As more effective treatments are now available, especially for patients with oligo…
View article: Aggressiveness-guided nodule management for lung cancer screening in Europe—justification for follow-up intervals and definition of growth
Aggressiveness-guided nodule management for lung cancer screening in Europe—justification for follow-up intervals and definition of growth Open
The European Society of Thoracic Imaging (ESTI) nodule management recommendation for lung cancer screening with low-dose CT builds on existing nodule management guidelines but puts a stronger focus on lesion aggressiveness and measurement …
View article: Artificial intelligence for the detection of airway nodules in chest CT scans
Artificial intelligence for the detection of airway nodules in chest CT scans Open
Objectives Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizin…
View article: Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection
Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection Open
View article: An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study
An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study Open
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 OPTI…
View article: Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial
Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial Open
Low-dose CT screening for lung cancer depicts millions of mostly benign nodules; this requires a risk-stratification protocol using nodule volume, growth, density, and other features to assess nodule malignancy risk.
View article: Structure and position-aware graph neural network for airway labeling
Structure and position-aware graph neural network for airway labeling Open
We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch feat…
View article: Towards automatic forecasting of lung nodule diameter with tabular data and CT imaging
Towards automatic forecasting of lung nodule diameter with tabular data and CT imaging Open
Contains fulltext : 312309.pdf (Publisher’s version ) (Open Access)
View article: Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation
Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation Open
Objective To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. Methods and materials In this retrospective study, we integrated an unc…
View article: Estimating lung function from computed tomography at the patient and lobe level using machine learning
Estimating lung function from computed tomography at the patient and lobe level using machine learning Open
Background Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, w…
View article: Nodule detection and generation on chest X-rays: NODE21 Challenge
Nodule detection and generation on chest X-rays: NODE21 Challenge Open
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the dete…
View article: Transfer learning from a sparsely annotated dataset of 3D medical images
Transfer learning from a sparsely annotated dataset of 3D medical images Open
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance. Due to the lack of large datasets in the medical imaging …
View article: Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans
Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans Open
View article: Kidney abnormality segmentation in thorax-abdomen CT scans
Kidney abnormality segmentation in thorax-abdomen CT scans Open
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalities such as cysts, lesions, masses, metastases, and primary…
View article: Emphysema subtyping on thoracic computed tomography scans using deep neural networks
Emphysema subtyping on thoracic computed tomography scans using deep neural networks Open
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 …
View article: Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules
Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules Open
Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT…
View article: Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals
Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals Open
View article: Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans
Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans Open
215 thoraxabdomen CT scans with segmentations of the kidney and abnormalities in the kidney.
View article: Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans
Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans Open
215 thoraxabdomen CT scans with segmentations of the kidney and abnormalities in the kidney.
View article: Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial
Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial Open
View article: Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients
Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients Open
View article: The Liver Tumor Segmentation Benchmark (LiTS)
The Liver Tumor Segmentation Benchmark (LiTS) Open
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences o…
View article: The AGEL Survey: Spectroscopic Confirmation of Strong Gravitational Lenses in the DES and DECaLS Fields Selected Using Convolutional Neural Networks
The AGEL Survey: Spectroscopic Confirmation of Strong Gravitational Lenses in the DES and DECaLS Fields Selected Using Convolutional Neural Networks Open
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 usi…
View article: Innovations in thoracic imaging: <scp>CT</scp>, radiomics, <scp>AI</scp> and x‐ray velocimetry
Innovations in thoracic imaging: <span>CT</span>, radiomics, <span>AI</span> and x‐ray velocimetry Open
In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification…
View article: LUNA22-ISMI
LUNA22-ISMI Open
This is a dataset of 1176 lung nodules extracted from the LIDC-IDRI dataset for the educational LUNA22-ISMI challenge. This dataset only contains 3D patches of nodules (size 128 x 128 x 64 in x, y, and z directions) that had been annotated…
View article: LUNA22-ISMI
LUNA22-ISMI Open
This is a dataset of 1176 lung nodules extracted from the LIDC-IDRI dataset for the educational LUNA22-ISMI challenge. This dataset only contains 3D patches of nodules (size 128 x 128 x 64 in x, y, and z directions) that had been annotated…