Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR

Texture Analysis as Imaging Biomarker of Tumoral Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients Studied with 3-T Magnetic Resonance

(2015) Carlo N. De Cecco et al.   INVESTIGATIVE RADIOLOGY

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images

(2014) Yoganand Balagurunathan et al.   Translational Oncology

Texture-based classification of different gastric tumors at contrast-enhanced CT

(2013) Ahmed Ba-Ssalamah et al.   EUROPEAN JOURNAL OF RADIOLOGY

Surgical Pathology of Lung Cancer

(2013) M. Rezaei et al.   SEMINARS IN RESPIRATORY AND CRITICAL CARE MEDICINE

Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET

(2012) F. Tixier et al.   JOURNAL OF NUCLEAR MEDICINE

Pathology of Lung Cancer

(2011) William D. Travis   CLINICS IN CHEST MEDICINE

Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer

(2011) F. Tixier et al.   JOURNAL OF NUCLEAR MEDICINE

Feasibility of Texture Analysis for the Assessment of Biochemical Changes in Meniscal Tissue on T1 Maps Calculated From Delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Cartilage Data

(2010) Marius E. Mayerhoefer et al.   INVESTIGATIVE RADIOLOGY

Characterization of Breast Cancer Types by Texture Analysis of Magnetic Resonance Images

(2009) Kirsi Holli et al.   ACADEMIC RADIOLOGY

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

(2009) Evangelia I. Zacharaki et al.   MAGNETIC RESONANCE IN MEDICINE

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

(2008) Pantelis Georgiadis et al.   MAGNETIC RESONANCE IMAGING

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

(2008) I. El Naqa et al.   PATTERN RECOGNITION