Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging
暂无分享,去创建一个
[1] Elaine M Caoili,et al. Renal mass core biopsy: accuracy and impact on clinical management. , 2007, AJR. American journal of roentgenology.
[2] M. Stöckle,et al. Survival outcomes in patients with large (≥7cm) clear cell renal cell carcinomas treated with nephron-sparing surgery versus radical nephrectomy: Results of a multicenter cohort with long-term follow-up , 2018, PloS one.
[3] Junhua Zheng,et al. Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma , 2020, International journal of cancer.
[4] C. Porta,et al. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2012, Annals of oncology : official journal of the European Society for Medical Oncology.
[5] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[6] Jonathan Scalera,et al. Texture analysis as a radiomic marker for differentiating renal tumors , 2017, Abdominal Radiology.
[7] E. S. Durmaz,et al. Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade. , 2019, AJR. American journal of roentgenology.
[8] Min Ju Kim,et al. Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma , 2017, Acta radiologica.
[9] H. Huhdanpaa,et al. CT prediction of the Fuhrman grade of clear cell renal cell carcinoma (RCC): towards the development of computer-assisted diagnostic method , 2015, Abdominal Imaging.
[10] N. Takahashi,et al. CT and MR imaging for solid renal mass characterization. , 2018, European journal of radiology.
[11] John L. Gore,et al. Long-term survival following partial vs radical nephrectomy among older patients with early-stage kidney cancer. , 2012, JAMA.
[12] R. Thornhill,et al. Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT , 2020, Abdominal Radiology.
[13] Changhee Han,et al. Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection , 2019, 2019 International Conference on 3D Vision (3DV).
[14] A. Molina,et al. Validation of risk factors for recurrence of renal cell carcinoma: Results from a large single-institution series , 2019, PloS one.
[15] Zhan Feng,et al. CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma , 2019, Cancer Imaging.
[16] Lei Wang,et al. 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[17] Fabien Scalzo,et al. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT , 2019, Abdominal Radiology.
[18] Paul F. Whelan,et al. Using filter banks in Convolutional Neural Networks for texture classification , 2016, Pattern Recognit. Lett..
[19] Ghassan Hamarneh,et al. Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification , 2019, MICCAI.
[20] Choung-Soo Kim,et al. Percutaneous Kidney Biopsy for a Small Renal Mass: A Critical Appraisal of Results. , 2016, The Journal of urology.
[21] Fan Lin,et al. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma , 2019, Abdominal Radiology.
[22] H. Pyo,et al. Body fat measurement in computed tomography image. , 1999, Biomedical sciences instrumentation.
[23] M. Janghorbani,et al. The comparative survey of Hounsfield units of stone composition in urolithiasis patients , 2014, Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences.
[24] Xiaogang Wang,et al. Learnable Histogram: Statistical Context Features for Deep Neural Networks , 2016, ECCV.
[25] C. Sundaram,et al. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade , 2019, European Radiology.
[26] D. Holanda,et al. Tumor grade of clear cell renal cell carcinoma assessed by contrast-enhanced computed tomography , 2014, SpringerPlus.
[27] Stephen M. Moore,et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.
[28] M. Kattan,et al. A preoperative prognostic nomogram for solid enhancing renal tumors 7 cm or less amenable to partial nephrectomy. , 2007, The Journal of urology.
[29] Jiule Ding,et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. , 2018, European journal of radiology.
[30] J. Patard,et al. Can renal mass biopsy assessment of tumor grade be safely substituted for by a predictive model? , 2009, The Journal of urology.
[31] Qi Cheng,et al. Enhanced Computed Tomography–Based Radiomics Signature Combined With Clinical Features in Evaluating Nuclear Grading of Renal Clear Cell Carcinoma , 2020, Journal of computer assisted tomography.
[32] S. Fuhrman,et al. Prognostic significance of morphologic parameters in renal cell carcinoma , 1982, The American journal of surgical pathology.
[33] B. Iványi,et al. Prognostic Factors for Renal Cell Carcinoma Subtypes Diagnosed According to the 2016 WHO Renal Tumor Classification: a Study Involving 928 Patients , 2017, Pathology & Oncology Research.
[34] Mohammad Reza Deevband,et al. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning , 2019, La radiologia medica.
[35] P. Tan,et al. Differential radiologic characteristics of renal tumours on multiphasic computed tomography. , 2017, Singapore medical journal.
[36] S. Meystre,et al. Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.
[37] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[38] V. Ramani,et al. Accuracy of preoperative CT T staging of renal cell carcinoma: which features predict advanced stage? , 2015, Clinical radiology.
[39] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[40] A. Oberai,et al. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses , 2020, European Radiology.
[41] Ghassan Hamarneh,et al. Renal Cell Carcinoma Staging with Learnable Image Histogram-Based Deep Neural Network , 2019, MLMI@MICCAI.
[42] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] H. Yin,et al. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. , 2018, European journal of radiology.
[44] J. Cheville,et al. The International Society of Urological Pathology (ISUP) Grading System for Renal Cell Carcinoma and Other Prognostic Parameters , 2013, The American journal of surgical pathology.
[45] J. Earls,et al. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study , 2019, Abdominal Radiology.
[46] Xiaopeng He,et al. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images. , 2020, Academic radiology.
[47] C. Meyer,et al. Critical analysis of a simplified Fuhrman grading scheme for prediction of cancer specific mortality in patients with clear cell renal cell carcinoma--Impact on prognosis. , 2016, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.
[48] O. Kilickesmez,et al. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade , 2018, European Radiology.
[49] Ghassan Hamarneh,et al. Noninvasive Determination of Gene Mutations in Clear Cell Renal Cell Carcinoma Using Multiple Instance Decisions Aggregated CNN , 2018, MICCAI.
[50] Ghassan Hamarneh,et al. ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans , 2019, MICCAI.
[51] Xiaoli Meng,et al. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade. , 2019, European journal of radiology.
[52] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[53] Michael J Schwartz,et al. Partial Nephrectomy is Associated with Higher Risk of Relapse Compared with Radical Nephrectomy for Clinical Stage T1 Renal Cell Carcinoma Pathologically Up Staged to T3a , 2017, The Journal of urology.
[54] Mitko Veta,et al. Automated clear cell renal carcinoma grade classification with prognostic significance , 2019, PloS one.
[55] Lin Liu,et al. Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images , 2019, Medicine.
[56] E. Bass,et al. Diagnostic Accuracy and Risks of Biopsy in the Diagnosis of a Renal Mass Suspicious for Localized Renal Cell Carcinoma: Systematic Review of the Literature. , 2016, The Journal of urology.