Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

(2020) Alex Zwanenburg et al.   RADIOLOGY

Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base

(2020) Yang Zhang et al.   Frontiers in Oncology

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

(2020) Yoshitaka Toyama et al.   Scientific Reports

Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis

(2019) Moran Artzi et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types

(2019) Francesco Bianconi et al.   MOLECULAR IMAGING AND BIOLOGY

Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas

(2019) Shuang Wu et al.   JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers

(2019) Zenghui Qian et al.   CANCER LETTERS

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

(2019) Junjiong Zheng et al.   CANCER

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

(2019) Ruimeng Yang et al.   EUROPEAN RADIOLOGY

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

(2019) Chaoyue Chen et al.   Frontiers in Oncology

Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT

(2019) Sho Koyasu et al.   ANNALS OF NUCLEAR MEDICINE

The clinical value of texture analysis of dual-time-point 18F-FDG-PET/CT imaging to differentiate between 18F-FDG-avid benign and malignant pulmonary lesions

(2019) Masatoyo Nakajo et al.   EUROPEAN RADIOLOGY

Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma

(2019) Yimeng Fan et al.   Frontiers in Oncology

A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer

(2019) Seung Hyup Hyun et al.   CLINICAL NUCLEAR MEDICINE

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study

(2019) Gu-Wei Ji et al.   EBioMedicine

Diabetes classification model based on boosting algorithms

(2018) Peihua Chen et al.   BMC BIOINFORMATICS

LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity

(2018) Christophe Nioche et al.   CANCER RESEARCH

Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas

(2018) Chia-Feng Lu et al.   CLINICAL CANCER RESEARCH

Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions

(2018) Margarita Kirienko et al.   EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING

Current Applications and Future Impact of Machine Learning in Radiology

(2018) Garry Choy et al.   RADIOLOGY

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values

(2018) David Bonekamp et al.   RADIOLOGY

Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine

(2018) Chuanze Kang et al.   JOURNAL OF THEORETICAL BIOLOGY

Applications of random forest feature selection for fine-scale genetic population assignment

(2017) Emma V. A. Sylvester et al.   Evolutionary Applications

Urinary bladder cancer staging in CT urography using machine learning

(2017) Sankeerth S. Garapati et al.   MEDICAL PHYSICS

Diagnostic value of 18F-FDG-PET/CT for the evaluation of solitary pulmonary nodules

(2017) Zong Ruilong et al.   NUCLEAR MEDICINE COMMUNICATIONS

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

(2017) Chang Zhou et al.   PLoS One

PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

(2017) M. Sollini et al.   Scientific Reports

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

(2016) Kun-Hsing Yu et al.   Nature Communications

FDG PET-CT for solitary pulmonary nodule and lung cancer: Literature review

(2016) D. Groheux et al.   Diagnostic and Interventional Imaging

Prognostic Significance of Intratumoral Metabolic Heterogeneity on 18F-FDG PET/CT in Pathological N0 Non–Small Cell Lung Cancer

(2015) Do-Hoon Kim et al.   CLINICAL NUCLEAR MEDICINE

False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review

(2015) Anastasia Chalkidou et al.   PLoS One

Machine Learning methods for Quantitative Radiomic Biomarkers

(2015) Chintan Parmar et al.   Scientific Reports

Computerized Image Analysis for Identifying Triple-Negative Breast Cancers and Differentiating Them from Other Molecular Subtypes of Breast Cancer on Dynamic Contrast-enhanced MR Images: A Feasibility Study

(2014) Shannon C. Agner et al.   RADIOLOGY

What Is the Clinical Value of PET/CT in the Diagnosis of Pulmonary Nodules?

(2014) C. Lohrmann et al.   ZENTRALBLATT FUR CHIRURGIE

The solitary pulmonary nodule in patients with previous cancer history: Results of surgical treatment

(2013) O. Rena et al.   EJSO

Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

(2011)   NEW ENGLAND JOURNAL OF MEDICINE

Deaths and complications associated with respiratory endoscopy: A survey by the Japan Society for Respiratory Endoscopy in 2010

(2011) FUMIHIRO ASANO et al.   RESPIROLOGY