Artificial intelligence and machine learning in cancer imaging
An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …
learning (ML) for cancer imaging. The development of an optimal tool requires …
Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies
Rapid advances in automated methods for extracting large numbers of quantitative features
from medical images have led to tremendous growth of publications reporting on radiomic …
from medical images have led to tremendous growth of publications reporting on radiomic …
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 …
Prediction of Cognitive decline in Parkinson's Disease using clinical and DAT SPECT Imaging features, and Hybrid Machine Learning systems
M Hosseinzadeh, A Gorji, A Fathi Jouzdani… - Diagnostics, 2023 - mdpi.com
Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in
Parkinson's disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and …
Parkinson's disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and …
[HTML][HTML] Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images
Objective To investigate the impact of harmonization on the performance of CT, PET, and
fused PET/CT radiomic features toward the prediction of mutations status, for epidermal …
fused PET/CT radiomic features toward the prediction of mutations status, for epidermal …
Understanding sources of variation to improve the reproducibility of radiomics
B Zhao - Frontiers in oncology, 2021 - frontiersin.org
Radiomics is the method of choice for investigating the association between cancer imaging
phenotype, cancer genotype and clinical outcome prediction in the era of precision …
phenotype, cancer genotype and clinical outcome prediction in the era of precision …
FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging
The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …
of data generated by today's clinical systems, has led to the development of imaging AI …
Deep versus handcrafted tensor radiomics features: prediction of survival in head and neck cancer using machine learning and fusion techniques
MR Salmanpour, SM Rezaeijo, M Hosseinzadeh… - Diagnostics, 2023 - mdpi.com
Background: Although handcrafted radiomics features (RF) are commonly extracted via
radiomics software, employing deep features (DF) extracted from deep learning (DL) …
radiomics software, employing deep features (DF) extracted from deep learning (DL) …
[HTML][HTML] Overall survival prognostic modelling of non-small cell lung cancer patients using positron emission tomography/computed tomography harmonised radiomics …
Aims Despite the promising results achieved by radiomics prognostic models for various
clinical applications, multiple challenges still need to be addressed. The two main limitations …
clinical applications, multiple challenges still need to be addressed. The two main limitations …