Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features

Cancer statistics, 2020

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Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan

(2020) Xianwu Xia et al.   Frontiers in Oncology

Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis

(2020) Xianghua Hu et al.   BRITISH JOURNAL OF RADIOLOGY

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

(2019) Diego Ardila et al.   NATURE MEDICINE

Lung adenocarcinoma diagnosis in one stage

(2019) Pengyi Hao et al.   NEUROCOMPUTING

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

(2019) Jing Gong et al.   EUROPEAN RADIOLOGY

Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

(2019) Yongsik Sim et al.   RADIOLOGY

High-resolution Computed Tomography Features Distinguishing Benign and Malignant Lesions Manifesting as Persistent Solitary Subsolid Nodules

(2018) Wenjia Yang et al.   Clinical Lung Cancer

Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons

(2018) Y.P. Zhang et al.   CLINICAL RADIOLOGY

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

Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest

(2018) Xueyan Mei et al.   Journal of Thoracic Disease

3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT

(2018) Shengping Wang et al.   QUANTITATIVE IMAGING IN MEDICINE AND SURGERY

3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas

(2018) Wei Zhao et al.   CANCER RESEARCH

Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study

(2018) Jing Gong et al.   MEDICAL PHYSICS

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas

(2018) Niha Beig et al.   RADIOLOGY

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

(2018) Nicholas Bien et al.   PLOS MEDICINE

Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

(2017) Wei Shen et al.   PATTERN RECOGNITION

Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs

(2017) Nima Tajbakhsh et al.   PATTERN RECOGNITION

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

(2016) Wei Li et al.   Computational and Mathematical Methods in Medicine

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

(2016) Hoo-Chang Shin et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer

(2015) Hyungjin Kim et al.   INVESTIGATIVE RADIOLOGY

Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules: Differences between malignant and benign

(2015) Haiyang Hu et al.   Thoracic Cancer

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