Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

[1]  H. Chung,et al.  Prognostic value of metabolic tumor volume measured by FDG-PET/CT in patients with cervical cancer. , 2011, Gynecologic oncology.

[2]  R. Wahl,et al.  From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors , 2009, Journal of Nuclear Medicine.

[3]  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.

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

[5]  M. Hatt,et al.  Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET , 2012, The Journal of Nuclear Medicine.

[6]  W. Oyen,et al.  SUV: From Silly Useless Value to Smart Uptake Value , 2010, Journal of Nuclear Medicine.

[7]  Jesús Angulo,et al.  Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification , 2014, IEEE Transactions on Biomedical Engineering.

[8]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[9]  Kari Känsälä,et al.  Inter-item correlations among function points , 1993, [1993] Proceedings First International Software Metrics Symposium.

[10]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[11]  A. Jemal,et al.  Colorectal cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[12]  C. Claussen,et al.  Early prediction of treatment response to high-dose salvage chemotherapy in patients with relapsed germ cell cancer using [18F]FDG PET , 2002, British Journal of Cancer.

[13]  Osama Mawlawi,et al.  PET/CT imaging artifacts. , 2005, Journal of nuclear medicine technology.

[14]  Vicky Goh,et al.  Correlation of Intra-Tumor 18F-FDG Uptake Heterogeneity Indices with Perfusion CT Derived Parameters in Colorectal Cancer , 2014, PloS one.

[15]  F O'Sullivan,et al.  Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. , 2005, Biostatistics.

[16]  I. Naqa.  The role of quantitative PET in predicting cancer treatment outcomes , 2014, Clinical and Translational Imaging.

[17]  Vishwa Parekh,et al.  Radiomics: a new application from established techniques , 2016, Expert review of precision medicine and drug development.

[18]  Ying Liang,et al.  A Segmentation Algorithm for Quantitative Analysis of Heterogeneous Tumors of the Cervix With 18 F-FDG PET/CT , 2015, IEEE Transactions on Biomedical Engineering.

[19]  Issam El-Naqa,et al.  Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..

[20]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[21]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[22]  Geoffrey G. Zhang,et al.  Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms , 2017, Journal of applied clinical medical physics.

[23]  H. Gulliksen.  The relation of item difficulty and inter-item correlation to test variance and reliability , 1945 .

[24]  A. Rositch,et al.  Hysterectomy‐corrected cervical cancer mortality rates reveal a larger racial disparity in the United States , 2017, Cancer.

[25]  E. De Ponti,et al.  18F-FDG PET/CT can predict nodal metastases but not recurrence in early stage uterine cervical cancer. , 2012, Gynecologic oncology.

[26]  O. Couturier,et al.  For avid glucose tumors, the SUV peak is the most reliable parameter for [18F]FDG-PET/CT quantification, regardless of acquisition time , 2016, EJNMMI Research.

[27]  P. Grigsby,et al.  FDG-PET-based prognostic nomograms for locally advanced cervical cancer. , 2012, Gynecologic oncology.

[28]  P. Grigsby,et al.  Pelvic lymph node F‐18 fluorodeoxyglucose uptake as a prognostic biomarker in newly diagnosed patients with locally advanced cervical cancer , 2010, Cancer.

[29]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[30]  U. Zingg,et al.  Accuracy of PET-CT in Predicting Survival in Patients with Esophageal Cancer , 2012, World Journal of Surgery.

[31]  Geoffrey G. Zhang,et al.  Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.

[32]  Paul Kinahan,et al.  Instrumentation factors affecting variance and bias of quantifying tracer uptake with PET/CT. , 2010, Medical physics.

[33]  Thierry Denoeux,et al.  Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction , 2016, Medical Image Anal..

[34]  I. El Naqa,et al.  Beyond imaging: The promise of radiomics. , 2017, 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.