Limited impact of discretization/interpolation parameters on the predictive power of CT radiomic features in a surgical cohort of pancreatic cancer patients

[1]  L. Martí-Bonmatí,et al.  Pancreatic cancer, Radiomics and Artificial Intelligence: A Review , 2022, The British Journal of Radiology.

[2]  H. Schlemmer,et al.  Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force , 2022, European Radiology.

[3]  E. Neri,et al.  Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging , 2022, BioMed research international.

[4]  M. Ay,et al.  The Impact of Preprocessing on the PET-CT Radiomics Features in Non-small Cell Lung Cancer , 2021, Frontiers in Biomedical Technologies.

[5]  C. Fiorino,et al.  Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach , 2021, Cancers.

[6]  A. Revishvili,et al.  Computed tomography-based radiomics approach in pancreatic tumors characterization , 2021, La radiologia medica.

[7]  Andrew D. A. Maidment,et al.  The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization , 2021, Cancers.

[8]  Binsheng Zhao.  Understanding Sources of Variation to Improve the Reproducibility of Radiomics , 2021, Frontiers in Oncology.

[9]  M. Manzoni,et al.  CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors , 2021, La radiologia medica.

[10]  K. Lillemoe,et al.  Implications of Perineural Invasion on Disease Recurrence and Survival After Pancreatectomy for Pancreatic Head Ductal Adenocarcinoma , 2020, Annals of surgery.

[11]  V. Bettinardi,et al.  Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  C. Fiorino,et al.  Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms. , 2020, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[13]  R. Steenbakkers,et al.  The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.

[14]  Jin-Hyeok Hwang,et al.  Neoadjuvant therapy versus upfront surgery in resectable pancreatic cancer according to intention-to-treat and per-protocol analysis: A systematic review and meta-analysis , 2019, Scientific Reports.

[15]  Emiliano Spezi,et al.  Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging , 2019, Scientific Reports.

[16]  Jie Lu,et al.  Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability. , 2019, Quantitative imaging in medicine and surgery.

[17]  T. Shichinohe,et al.  A Preoperative Prognostic Scoring System to Predict Prognosis for Resectable Pancreatic Cancer: Who Will Benefit from Upfront Surgery? , 2018, Journal of Gastrointestinal Surgery.

[18]  R. Gillies,et al.  Repeatability and Reproducibility of Radiomic Features: A Systematic Review , 2018, International journal of radiation oncology, biology, physics.

[19]  Qianjin Feng,et al.  Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT , 2018, European Radiology.

[20]  Philippe Lambin,et al.  4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[21]  B. Erickson,et al.  Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study , 2017, PloS one.

[22]  J. Cameron,et al.  Patterns, Timing, and Predictors of Recurrence Following Pancreatectomy for Pancreatic Ductal Adenocarcinoma , 2017, Annals of surgery.

[23]  S. Barni,et al.  Borderline resectable pancreatic cancer: More than an anatomical concept. , 2017, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[24]  Lawrence H. Schwartz,et al.  Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings , 2016, PloS one.

[25]  Stefan Leger,et al.  Image biomarker standardisation initiative version 1 . 4 , 2016, 1612.07003.

[26]  Zaiyi Liu,et al.  Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule , 2016, Scientific Reports.

[27]  Wolfgang Weber,et al.  Reliability of PET/CT Shape and Heterogeneity Features in Functional and Morphologic Components of Non–Small Cell Lung Cancer Tumors: A Repeatability Analysis in a Prospective Multicenter Cohort , 2016, The Journal of Nuclear Medicine.

[28]  Jiazhou Wang,et al.  Reproducibility with repeat CT in radiomics study for rectal cancer , 2016, Oncotarget.

[29]  M. Farnell,et al.  Carbohydrate Antigen 19-9 Elevation in Anatomically Resectable, Early Stage Pancreatic Cancer Is Independently Associated with Decreased Overall Survival and an Indication for Neoadjuvant Therapy: A National Cancer Database Study. , 2016, Journal of the American College of Surgeons.

[30]  Ronald Boellaard,et al.  Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [18F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation , 2016, Molecular Imaging and Biology.

[31]  Geoffrey G. Zhang,et al.  Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer , 2015, Translational oncology.

[32]  G. Honda,et al.  Proposed preoperative risk factors for early recurrence in patients with resectable pancreatic ductal adenocarcinoma after surgical resection: A multi-center retrospective study. , 2015, Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.].

[33]  U. Ballehaninna,et al.  The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal. , 2012, Journal of gastrointestinal oncology.

[34]  M. Hatt,et al.  Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.

[35]  C. Bassi,et al.  Resectable Pancreatic Cancer: Who Really Benefits From Resection? , 2009, Annals of Surgical Oncology.

[36]  M. Falconi,et al.  Is CA 19-9 a screening marker? , 2009, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[37]  Joseph O Deasy,et al.  CERR: a computational environment for radiotherapy research. , 2003, Medical physics.

[38]  Sara Broggi,et al.  Ct radiomic features of pancreatic neuroendocrine neoplasms (panNEN) are robust against delineation uncertainty. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[39]  Matthew P. Goetz,et al.  NCCN CLINICAL PRACTICE GUIDELINES IN ONCOLOGY , 2019 .

[40]  C. Aubé,et al.  [Pancreatic adenocarcinoma]. , 2006, Presse medicale.