Artificial intelligence: A critical review of applications for lung nodule and lung cancer

Elements of a Good Radiology Artificial Intelligence Paper

(2022) Bo Gong et al.   CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma

(2022) Haozhe Huang et al.   MEDICAL PHYSICS

Mandating Limits on Workload, Duty, and Speed in Radiology

(2022) Robert Alexander et al.   RADIOLOGY

Does artificial intelligence surpass the radiologist?

(2022) Philippe Soyer et al.   Diagnostic and Interventional Imaging

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

(2022) Jong Hyuk Lee et al.   Translational Lung Cancer Research

Artificial intelligence in lung cancer: current applications and perspectives

(2022) Guillaume Chassagnon et al.   Japanese Journal of Radiology

Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning–based CT Section Thickness Reduction

(2021) Sohee Park et al.   RADIOLOGY

Intergroupe francophone de cancérologie thoracique, Société de pneumologie de langue française, and Société d’imagerie thoracique statement paper on lung cancer screening

(2021) Sébastien Couraud et al.   Diagnostic and Interventional Imaging

On the performance of lung nodule detection, segmentation and classification

(2021) Dongdong Gu et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography

(2021) Ramandeep Singh et al.   Quantitative Imaging in Medicine and Surgery

Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital

(2021) C.O. Martins Jarnalo et al.   CLINICAL RADIOLOGY

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

(2021) Hyunsuk Yoo et al.   EUROPEAN RADIOLOGY

Chest radiographs and machine learning – Past, present and future

(2021) Catherine M Jones et al.   Journal of Medical Imaging and Radiation Oncology

Deep learning applied to automatic disease detection using chest X‐rays

(2021) Daniel A. Moses   Journal of Medical Imaging and Radiation Oncology

Imaging of COVID-19: An update of current evidences

(2021) Shingo Kato et al.   Diagnostic and Interventional Imaging

Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study

(2021) Jing Gong et al.   Cancers

Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time: A Prospective Feasibility Study

(2021) Felix C. Müller et al.   ACADEMIC RADIOLOGY

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non–Small Cell Lung Cancer

(2021) Yifan Zhong et al.   RADIOLOGY

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

(2021) Trieu-Nghi Hoang-Thi et al.   Diagnostic and Interventional Imaging

An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

(2021) Fatemeh Homayounieh et al.   JAMA Network Open

Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review

(2020) Amitava Halder et al.   JOURNAL OF DIGITAL IMAGING

Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas

(2020) Hyungjin Kim et al.   RADIOLOGY

The Effect of Advances in Lung-Cancer Treatment on Population Mortality

(2020) Nadia Howlader et al.   NEW ENGLAND JOURNAL OF MEDICINE

Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs

(2020) Sowon Jang et al.   RADIOLOGY

Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation

(2020) Xianling Dong et al.   CANCER IMAGING

Approaches to lung nodule risk assessment: clinician intuition versus prediction models

(2020) Adam H. Fox et al.   Journal of Thoracic Disease

Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker

(2020) Bingxi He et al.   Journal for ImmunoTherapy of Cancer

Radiomics Improves Cancer Screening and Early Detection

(2020) Robert J. Gillies et al.   CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION

Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population

(2020) Jong Hyuk Lee et al.   RADIOLOGY

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

(2020) Constance de Margerie-Mellon et al.   Scientific Reports

A primer for understanding radiology articles about machine learning and deep learning

(2020) Takeshi Nakaura et al.   Diagnostic and Interventional Imaging

Artificial intelligence solution to classify pulmonary nodules on CT

(2020) D. Blanc et al.   Diagnostic and Interventional Imaging

Artificial intelligence: from challenges to clinical implementation

(2020) G. Chassagnon et al.   Diagnostic and Interventional Imaging

Radiology indispensable for tracking COVID-19

(2020) Jingwen Li et al.   Diagnostic and Interventional Imaging

Predicting EGFR Mutation Status in Lung Adenocarcinoma on CT Image Using Deep Learning

(2019) Shuo Wang et al.   EUROPEAN RESPIRATORY JOURNAL

Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening

(2019) Liting Mao et al.   QUANTITATIVE IMAGING IN MEDICINE AND SURGERY

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

(2019) Diego Ardila et al.   NATURE MEDICINE

Impact of the rise of artificial intelligence in radiology: What do radiologists think?

(2019) Q. Waymel et al.   Diagnostic and Interventional Imaging

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications

(2019) S. Ather et al.   CLINICAL RADIOLOGY

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

(2019) Dana Li et al.   Diagnostics

Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule

(2018) Li Fan et al.   EUROPEAN RADIOLOGY

Detection of Subsolid Nodules in Lung Cancer Screening

(2018) Mario Silva et al.   INVESTIGATIVE RADIOLOGY

Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer

(2018) Wookjin Choi et al.   MEDICAL PHYSICS

Effect of Shift, Schedule, and Volume on Interpretive Accuracy: A Retrospective Analysis of 2.9 Million Radiologic Examinations

(2018) Tarek N. Hanna et al.   RADIOLOGY

Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

(2018) Ju Gang Nam et al.   RADIOLOGY

A guide to deep learning in healthcare

(2018) Andre Esteva et al.   NATURE MEDICINE

Evaluating the performance of a deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists

(2018) Li Li et al.   Thoracic Cancer

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

(2018) Pranav Rajpurkar et al.   PLOS MEDICINE

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

(2018) Ahmed Hosny et al.   PLOS MEDICINE

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival

(2017) Mei Yuan et al.   EUROPEAN RADIOLOGY

Deep Learning: A Primer for Radiologists

(2017) Gabriel Chartrand et al.   RADIOGRAPHICS

Use of Volumetry for Lung Nodule Management: Theory and Practice

(2017) Anand Devaraj et al.   RADIOLOGY

Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017

(2017) Heber MacMahon et al.   RADIOLOGY

Missed lung cancer: when, where, and why?

(2017) Annemilia del Ciello et al.   Diagnostic and Interventional Radiology

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

(2016) Arnaud Arindra Adiyoso Setio et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

The Effects of Changes in Utilization and Technological Advancements of Cross-Sectional Imaging on Radiologist Workload

(2015) Robert J. McDonald et al.   ACADEMIC RADIOLOGY

Recent Trends in the Identification of Incidental Pulmonary Nodules

(2015) Michael K. Gould et al.   AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE

British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE

(2015) M E J Callister et al.   THORAX

Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening

(2014) Ernst Th. Scholten et al.   EUROPEAN RADIOLOGY

Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

(2013) Colin Jacobs et al.   MEDICAL IMAGE ANALYSIS

Screening by Chest Radiograph and Lung Cancer Mortality

(2011) Martin M. Oken et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

(2011) Samuel G. Armato et al.   MEDICAL PHYSICS

Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

(2011)   NEW ENGLAND JOURNAL OF MEDICINE

Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs

(2007) Russell C. Hardie et al.   MEDICAL IMAGE ANALYSIS