Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis

[1]  K. Shi,et al.  A nomogram based on preoperative inflammatory markers predicting the overall survival of pancreatic ductal adenocarcinoma , 2017, Journal of gastroenterology and hepatology.

[2]  K. Lillemoe,et al.  Does Size Matter in Pancreatic Cancer?: Reappraisal of Tumour Dimension as a Predictor of Outcome Beyond the TNM , 2017, Annals of surgery.

[3]  Richard Tuli,et al.  Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients. , 2017, Journal of gastrointestinal oncology.

[4]  Ronald M. Summers,et al.  A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling , 2015, IEEE Transactions on Image Processing.

[5]  Le Lu,et al.  Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks , 2016, MICCAI.

[6]  J. Cameron,et al.  Circulating Tumor Cells Expressing Markers of Tumor-Initiating Cells Predict Poor Survival and Cancer Recurrence in Patients with Pancreatic Ductal Adenocarcinoma , 2016, Clinical Cancer Research.

[7]  C. Haglund,et al.  Systemic Inflammatory Response and Elevated Tumour Markers Predict Worse Survival in Resectable Pancreatic Ductal Adenocarcinoma , 2016, PloS one.

[8]  Dong-ping Wang,et al.  Prognostic value of combined preoperative lactate dehydrogenase and alkaline phosphatase levels in patients with resectable pancreatic ductal adenocarcinoma , 2016, Medicine.

[9]  J. Tomlinson,et al.  Long-term survival in patients with pancreatic ductal adenocarcinoma. , 2016, Surgery.

[10]  S. Hyun,et al.  Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[11]  B. Davidson,et al.  Radiological tumor density and lymph node size correlate with survival in resectable adenocarcinoma of the pancreatic head: A retrospective cohort study. , 2016, Journal of cancer research and therapeutics.

[12]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[13]  Marius George Linguraru,et al.  Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors , 2015, Medical Image Anal..

[14]  Yuman Fong,et al.  Texture analysis of preoperative CT images for prediction of postoperative hepatic insufficiency: a preliminary study. , 2015, Journal of the American College of Surgeons.

[15]  M. Endo,et al.  Preoperative FDG-PET Predicts Early Recurrence and a Poor Prognosis After Resection of Pancreatic Adenocarcinoma , 2015, Annals of Surgical Oncology.

[16]  Claude Chelala,et al.  A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma , 2014, Genome Medicine.

[17]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[18]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[19]  Elliot K Fishman,et al.  Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association. , 2014, Radiology.

[20]  Michael E Griswold,et al.  Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. , 2013, Radiology.

[21]  Robert Grützmann,et al.  Preoperative CEA and CA 19-9 are prognostic markers for survival after curative resection for ductal adenocarcinoma of the pancreas - a retrospective tumor marker prognostic study. , 2013, International journal of surgery.

[22]  Simon Wan,et al.  Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer , 2013, Clinical Cancer Research.

[23]  H. Pitt,et al.  Risk of Morbidity and Mortality Following Hepato-Pancreato-Biliary Surgery , 2012, Journal of Gastrointestinal Surgery.

[24]  Rangaraj M. Rangayyan,et al.  Statistical measures of orientation of texture for the detection of architectural distortion in prior mammograms of interval-cancer , 2012, J. Electronic Imaging.

[25]  Jae Young Lee,et al.  Small (≤ 20 mm) pancreatic adenocarcinomas: analysis of enhancement patterns and secondary signs with multiphasic multidetector CT. , 2011, Radiology.

[26]  S. Park,et al.  Visually isoattenuating pancreatic adenocarcinoma at dynamic-enhanced CT: frequency, clinical and pathologic characteristics, and diagnosis at imaging examinations. , 2010, Radiology.

[27]  Laura H. Tang,et al.  Pancreatic Adenocarcinoma: The Actual 5-Year Survivors , 2008, Journal of Gastrointestinal Surgery.

[28]  J. Neoptolemos,et al.  Current standards of surgery for pancreatic cancer , 2004, The British journal of surgery.

[29]  Murray F. Brennan,et al.  Prognostic Nomogram for Patients Undergoing Resection for Adenocarcinoma of the Pancreas , 2004, Annals of surgery.

[30]  J. Birkmeyer,et al.  Hospital volume and surgical mortality in the United States. , 2002, The New England journal of medicine.

[31]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..

[32]  Stéphane Buczkowski,et al.  Measurements of fractal dimension by box-counting: a critical analysis of data scatter , 1998 .

[33]  M. Talamini,et al.  Six hundred fifty consecutive pancreaticoduodenectomies in the 1990s: pathology, complications, and outcomes. , 1997, Annals of surgery.

[34]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[35]  T. Ichikawa.  [A comparative study of histopathological findings and CT images related to pancreatic carcinomas. An attempt at diagnosis in tissue characterization by CT]. , 1992, Nihon Ika Daigaku zasshi.

[36]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[37]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..