Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib

(2017) Masoom A. Haider et al.   CANCER IMAGING

MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection

(2016) Andrew Cameron et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Global surveillance of cancer survival 1995–2009: analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2)

(2015) Claudia Allemani et al.   LANCET

Application of high-dimensional feature selection: evaluation for genomic prediction in man

(2015) M. L. Bermingham et al.   Scientific Reports

Building Predictive Models inRUsing thecaretPackage

(2015) Max Kuhn   Journal of Statistical Software

Machine Learning methods for Quantitative Radiomic Biomarkers

(2015) Chintan Parmar et al.   Scientific Reports

SMOTE for high-dimensional class-imbalanced data

(2013) Rok Blagus et al.   BMC BIOINFORMATICS

Predicting sample size required for classification performance

(2012) Rosa L Figueroa et al.   BMC Medical Informatics and Decision Making

Radiomics: Extracting more information from medical images using advanced feature analysis

(2012) Philippe Lambin et al.   EUROPEAN JOURNAL OF CANCER

Radiomics: the process and the challenges

(2012) Virendra Kumar et al.   MAGNETIC RESONANCE IMAGING

Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival

(2011) Balaji Ganeshan et al.   EUROPEAN RADIOLOGY

Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage

(2011) Balaji Ganeshan et al.   CANCER IMAGING

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

(2009) Lung-Cheng Huang et al.   Journal of Translational Medicine