Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis
A Zwanenburg - European journal of nuclear medicine and molecular …, 2019 - Springer
Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in
multicentre settings is an important criterion for clinical translation. We therefore performed a …
multicentre settings is an important criterion for clinical translation. We therefore performed a …
Emerging applications of artificial intelligence in neuro-oncology
Due to the exponential growth of computational algorithms, artificial intelligence (AI)
methods are poised to improve the precision of diagnostic and therapeutic methods in …
methods are poised to improve the precision of diagnostic and therapeutic methods in …
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have
reported results from either private institutional data or publicly available datasets. However …
reported results from either private institutional data or publicly available datasets. However …
Within-modality synthesis and novel radiomic evaluation of brain MRI scans
Simple Summary Brain MRI scans often require different imaging sequences based on
tissue types, posing a common challenge. In our research, we propose a method that utilizes …
tissue types, posing a common challenge. In our research, we propose a method that utilizes …
Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects
Radiomic features have a wide range of clinical applications, but variability due to image
acquisition factors can affect their performance. The harmonization tool ComBat is a …
acquisition factors can affect their performance. The harmonization tool ComBat is a …
Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma
H Moradmand, SMR Aghamiri… - Journal of applied …, 2020 - Wiley Online Library
To investigate the effect of image preprocessing, in respect to intensity inhomogeneity
correction and noise filtering, on the robustness and reproducibility of the radiomics features …
correction and noise filtering, on the robustness and reproducibility of the radiomics features …
Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI
Background In the field of glioma, transcriptome subtypes have been considered as an
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
Blueprint for cancer research: critical gaps and opportunities
LW Elmore, SF Greer, EC Daniels… - CA: A Cancer …, 2021 - Wiley Online Library
We are experiencing a revolution in cancer. Advances in screening, targeted and immune
therapies, big data, computational methodologies, and significant new knowledge of cancer …
therapies, big data, computational methodologies, and significant new knowledge of cancer …