Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features

(2020) Linyu Wu et al.   Frontiers in Oncology

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas

(2020) Bao Feng et al.   EUROPEAN RADIOLOGY

Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?

(2019) Xiang Wang et al.   EUROPEAN RADIOLOGY

Artificial intelligence in cancer imaging: Clinical challenges and applications

(2019) Wenya Linda Bi et al.   CA-A CANCER JOURNAL FOR CLINICIANS

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT

(2019) Johanna Uthoff et al.   MEDICAL PHYSICS

Genomic Landscape and Immune Microenvironment Features of Preinvasive and Early Invasive Lung Adenocarcinoma

(2019) Chao Zhang et al.   Journal of Thoracic Oncology

Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions

(2019) Ilke Tunali et al.   MEDICAL PHYSICS

The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

(2018) Yunlang She et al.   EUROPEAN RADIOLOGY

Highly accurate model for prediction of lung nodule malignancy with CT scans

(2018) Jason L. Causey et al.   Scientific Reports

Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT

(2018) Wei Li et al.   Chinese Journal of Cancer Research

Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients

(2018) Lifeng Yang et al.   EUROPEAN RADIOLOGY

Perinodular Radiomic Features to Assess Nodule Microenvironment: Does It Help to Distinguish Malignant versus Benign Lung Nodules?

(2018) Mizuki Nishino   RADIOLOGY

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

(2018) Niha Beig et al.   RADIOLOGY

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images

(2017) Sihong Chen et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

(2017) José Raniery Ferreira et al.   JOURNAL OF DIGITAL IMAGING

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

(2017) Heber MacMahon et al.   RADIOLOGY

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

(2016) Onno M. Mets et al.   EUROPEAN RADIOLOGY

The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer

(2016) Peter Goldstraw et al.   Journal of Thoracic Oncology

Microenvironmental changes in the progression from adenocarcinoma in situ to minimally invasive adenocarcinoma and invasive lepidic predominant adenocarcinoma of the lung

(2016) Masahito Naito et al.   LUNG CANCER

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

Prognostic impact of nomogram based on whole tumour size, tumour disappearance ratio on CT and SUVmax on PET in lung adenocarcinoma

(2015) So Hee Song et al.   EUROPEAN RADIOLOGY

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

(2015) M E J Callister et al.   THORAX

Immune and Inflammatory Cell Composition of Human Lung Cancer Stroma

(2015) G-Andre Banat et al.   PLoS One

Computerized Texture Analysis of Persistent Part-Solid Ground-Glass Nodules: Differentiation of Preinvasive Lesions from Invasive Pulmonary Adenocarcinomas

(2014) Hee-Dong Chae et al.   RADIOLOGY

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

Invasive Pulmonary Adenocarcinomas versus Preinvasive Lesions Appearing as Ground-Glass Nodules: Differentiation by Using CT Features

(2013) Sang Min Lee et al.   RADIOLOGY

Radiomics: Extracting more information from medical images using advanced feature analysis

(2012) Philippe Lambin et al.   EUROPEAN JOURNAL OF CANCER

A Margin Sharpness Measurement for the Diagnosis of Breast Cancer from Magnetic Resonance Imaging Examinations

(2011) Jacob E.D. Levman et al.   ACADEMIC RADIOLOGY

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

(2011)   NEW ENGLAND JOURNAL OF MEDICINE

Invasive Size is an Independent Predictor of Survival in Pulmonary Adenocarcinoma

(2009) Alain C. Borczuk et al.   AMERICAN JOURNAL OF SURGICAL PATHOLOGY

Whole Genome Comparison of Allelic Imbalance between Noninvasive and Invasive Small-Sized Lung Adenocarcinomas

(2009) Hirofumi Nakanishi et al.   CANCER RESEARCH