Development and evaluation of an integrated model based on a deep segmentation network and demography-added radiomics algorithm for segmentation and diagnosis of early lung adenocarcinoma

Cancer statistics, 2022

(2022) Rebecca L. Siegel et al.   CA-A CANCER JOURNAL FOR CLINICIANS

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

(2022) Harriet L. Lancaster et al.   LUNG CANCER

Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population

(2022) John T. Murchison et al.   PLoS One

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

(2021) Jordan Chamberlin et al.   BMC Medicine

Added Value of Deep Learning–based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study

(2021) Jinkyeong Sung et al.   RADIOLOGY

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

(2021) Chengdi Wang et al.   Translational Oncology

Development and clinical application of deep learning model for lung nodules screening on CT images

(2020) Sijia Cui et al.   Scientific Reports

Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics

(2020) Xiang Wang et al.   Translational Lung Cancer Research

Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

(2019) Wenjing Ye et al.   Biomedical Engineering Online

Dual Cross-Entropy Loss for Small-Sample Fine-Grained Vehicle Classification

(2019) Xiaoxu Li et al.   IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

Computed Tomography based Score Indicative of Lung Cancer Aggression (SILA) Predicts the Degree of Histological Tissue Invasion and Patient Survival in Lung Adenocarcinoma Spectrum

(2019) Cyril Varghese et al.   Journal of Thoracic Oncology

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

(2019) Jing Gong et al.   EUROPEAN RADIOLOGY

Software-based risk stratification of pulmonary adenocarcinomas manifesting as pure ground glass nodules on computed tomography

(2017) Ursula Nemec et al.   EUROPEAN RADIOLOGY

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

(2016) Varun Gulshan et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

(2016) Weimiao Wu et al.   Frontiers in Oncology

Noninvasive Computed Tomography–based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial

(2015) Fabien Maldonado et al.   AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE

Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping

(2015) Ji Ye Son et al.   EUROPEAN RADIOLOGY

Noninvasive Risk Stratification of Lung Adenocarcinoma using Quantitative Computed Tomography

(2014) Sushravya Raghunath et al.   Journal of Thoracic Oncology

Why Do Pathological Stage IA Lung Adenocarcinomas Vary from Prognosis?: A Clinicopathologic Study of 176 Patients with Pathological Stage IA Lung Adenocarcinoma Based on the IASLC/ATS/ERS Classification

(2013) Jie Zhang et al.   Journal of Thoracic Oncology

Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)—A Pilot Study

(2013) Fabien Maldonado et al.   Journal of Thoracic Oncology

International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma

(2011) William D. Travis et al.   Journal of Thoracic Oncology