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

[1]  Liu Yan-hui,et al.  Interpretation of Pathological Perspective——International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma , 2011 .

[2]  Bram van Ginneken,et al.  Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas , 2016, European Radiology.

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Ho Yun Lee,et al.  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, European Radiology.

[5]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[6]  Ho Yun Lee,et al.  Pure ground-glass opacity neoplastic lung nodules: histopathology, imaging, and management. , 2014, AJR. American journal of roentgenology.

[7]  Bingbing Ni,et al.  3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas. , 2018, Cancer research.

[8]  M. Oudkerk,et al.  Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons. , 2018, Clinical radiology.

[9]  Weijun Peng,et al.  3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT. , 2018, Quantitative imaging in medicine and surgery.

[10]  Solid component proportion is an important predictor of tumor invasiveness in clinical stage T1N0M0 (cT1N0M0) lung adenocarcinoma , 2018, Cancer Imaging.

[11]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[12]  Arie Ben-David,et al.  About the relationship between ROC curves and Cohen's kappa , 2008, Eng. Appl. Artif. Intell..

[13]  Jie Tian,et al.  Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule , 2018, European Radiology.

[14]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[15]  C. Zappa,et al.  Non-small cell lung cancer: current treatment and future advances. , 2016, Translational lung cancer research.

[16]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[17]  X. Li,et al.  CT quantitative parameters to predict the invasiveness of lung pure ground-glass nodules (pGGNs). , 2018, Clinical radiology.

[18]  Masahiro Tsuboi,et al.  International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma , 2011, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[19]  Y. Hua,et al.  CT features differentiating pre- and minimally invasive from invasive adenocarcinoma appearing as mixed ground-glass nodules: mass is a potential imaging biomarker. , 2018, Clinical radiology.

[20]  O. Abe,et al.  Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-enhanced Hepatobiliary Phase MR Images. , 2017, Radiology.

[21]  Jesper Holst Pedersen,et al.  Ground-Glass Opacity Lung Nodules in the Era of Lung Cancer CT Screening: Radiology, Pathology, and Clinical Management. , 2016, Oncology.

[22]  Jin Mo Goo,et al.  Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. , 2014, Radiology.

[23]  Jianrong Xu,et al.  Analysis of pulmonary pure ground-glass nodule in enhanced dual energy CT imaging for predicting invasive adenocarcinoma: comparing with conventional thin-section CT imaging. , 2017, Journal of thoracic disease.

[24]  Y. Hua,et al.  CT characterization of different pathological types of subcentimeter pulmonary ground-glass nodular lesions. , 2019, The British journal of radiology.

[25]  Lung Adenocarcinoma Invasiveness Risk in Pure Ground-Glass Opacity Lung Nodules Smaller than 2 cm. , 2018, The Thoracic and cardiovascular surgeon.

[26]  Qiang Lin,et al.  Multi‐slice computed tomography characteristics of solitary pulmonary ground‐glass nodules: Differences between malignant and benign , 2015, Thoracic cancer.

[27]  Charles E Metz,et al.  Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. , 2006, Journal of the American College of Radiology : JACR.

[28]  B. Zheng,et al.  Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules , 2018, Physics in medicine and biology.

[29]  Bin Zheng,et al.  Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study , 2018, Medical physics.

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