Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results

Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications

(2021) So Yeon Won et al.   EUROPEAN JOURNAL OF RADIOLOGY

Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation

(2020) Sohi Bae et al.   Scientific Reports

External validation of prognostic models: what, why, how, when and where?

(2020) Chava L Ramspek et al.   Clinical Kidney Journal

Accuracy of Deep Learning to Differentiate the Histopathological Grading of Meningiomas on MR Images: A Preliminary Study

(2019) Tommaso Banzato et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

(2019) Martina Sollini et al.   EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING

Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement

(2019) Ji Eun Park et al.   EUROPEAN RADIOLOGY

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

(2019) Chaoyue Chen et al.   Frontiers in Oncology

Machine learning algorithm validation with a limited sample size

(2019) Andrius Vabalas et al.   PLoS One

The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study

(2019) Chaoyue Chen et al.   Frontiers in Oncology

Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging

(2018) Yae Won Park et al.   EUROPEAN RADIOLOGY

Diagnostic challenges in meningioma

(2017) Martha Nowosielski et al.   NEURO-ONCOLOGY

Cross-validation failure: Small sample sizes lead to large error bars

(2017) Gaël Varoquaux   NEUROIMAGE

External validation is only needed when prediction models are worth it (Letter commenting on: J Clin Epidemiol. 2015;68:25-34)

(2016) Forike K. Martens et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Response to letter by Forike et al.: more rigorous, not less, external validation is needed

(2016) George C.M. Siontis et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Prediction models need appropriate internal, internal–external, and external validation

(2016) Ewout W. Steyerberg et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models

(2014) Peter C Austin et al.   STATISTICAL METHODS IN MEDICAL RESEARCH

The Unreasonable Effectiveness of Data

(2009) Alon Halevy et al.   IEEE INTELLIGENT SYSTEMS

Data splitting for artificial neural networks using SOM-based stratified sampling

(2009) R.J. May et al.   NEURAL NETWORKS