Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT

[1]  A. Çırak,et al.  Comparison of Brock University, Mayo Clinic and Herder models for pretest probability of cancer in solid pulmonary nodules , 2022, The clinical respiratory journal.

[2]  S. Kido,et al.  Radiologists with and without deep learning–based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses , 2022, European Radiology.

[3]  Jianbo Gao,et al.  Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study , 2022, BMC Medical Imaging.

[4]  A. Rahmim,et al.  Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer , 2020, Molecular Imaging and Biology.

[5]  F. Zhang,et al.  Histologic subtype classification of non-small cell lung cancer using PET/CT images , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  Yong Yin,et al.  Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor , 2020, BMC Medical Imaging.

[7]  M. Oudkerk,et al.  Clinical characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital , 2020, Cancer biology & medicine.

[8]  F. Gilbert,et al.  Dynamic contrast–enhanced computed tomography for the diagnosis of solitary pulmonary nodules: a systematic review and meta-analysis , 2020, European Radiology.

[9]  H. Groen,et al.  A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma. , 2020, Clinical lung cancer.

[10]  H. Groen,et al.  Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population. , 2019, Translational lung cancer research.

[11]  Zhou Liu,et al.  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, Thoracic cancer.

[12]  B. van Ginneken,et al.  Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management , 2018, European Radiology.

[13]  Ho Yun Lee,et al.  Comparison of four models predicting the malignancy of pulmonary nodules: A single-center study of Korean adults , 2018, PLoS ONE.

[14]  C. la Vecchia,et al.  Projections in Breast and Lung Cancer Mortality among Women: A Bayesian Analysis of 52 Countries Worldwide. , 2018, Cancer research.

[15]  M. Lou,et al.  Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network , 2018, Front. Aging Neurosci..

[16]  J. Ryu.  Faculty Opinions recommendation of The 2015 world health organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. , 2018, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[17]  Lijie Wang,et al.  Nomogram for Predicting the Risk of Invasive Pulmonary Adenocarcinoma for Pure Ground-Glass Nodules. , 2018, The Annals of thoracic surgery.

[18]  Caroline Dive,et al.  Progress and prospects of early detection in lung cancer , 2017, Open Biology.

[19]  N. Tanner,et al.  Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation , 2017, Chest.

[20]  A. Bankier,et al.  Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. , 2017, Radiology.

[21]  A. Jemal,et al.  Cancer statistics in China, 2015 , 2016, CA: a cancer journal for clinicians.

[22]  J. Austin,et al.  The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. , 2015, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[23]  Xue-Ning Yang,et al.  Comparison of three mathematical prediction models in patients with a solitary pulmonary nodule. , 2014, Chinese journal of cancer research = Chung-kuo yen cheng yen chiu.

[24]  S. Iwano,et al.  Therapeutic surgery without a definitive diagnosis can be an option in selected patients with suspected lung cancer. , 2014, Interactive cardiovascular and thoracic surgery.

[25]  S. Lam,et al.  Probability of cancer in pulmonary nodules detected on first screening CT. , 2013, The New England journal of medicine.

[26]  J. Shrager,et al.  Indications for surgery in patients with localized pulmonary infection. , 2012, Thoracic surgery clinics.

[27]  C. Gatsonis,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[28]  Min-Ying Su,et al.  Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. , 2009, Academic radiology.

[29]  M. Atri,et al.  Imaging features of sclerosed hemangioma. , 2007, AJR. American journal of roentgenology.

[30]  M. Gould,et al.  A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. , 2007, Chest.

[31]  O. Hoekstra,et al.  Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. , 2005, Chest.

[32]  Antoine Pariente,et al.  Factors influencing accuracy of CT-guided percutaneous biopsies of pulmonary lesions , 2004, European Radiology.

[33]  S. Swensen,et al.  The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. , 1997, Archives of internal medicine.

[34]  M. Hammer,et al.  Limited Utility of Pulmonary Nodule Risk Calculators for Managing Large Nodules. , 2018, Current problems in diagnostic radiology.