Radiomics in medical imaging—“how-to” guide and critical reflection

JE Van Timmeren, D Cester, S Tanadini-Lang… - Insights into …, 2020 - Springer
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the
existing data available to clinicians by means of advanced mathematical analysis. Through …

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 …

Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies

RB Ger, S Zhou, PCM Chi, HJ Lee, RR Layman… - Scientific reports, 2018 - nature.com
Radiomics has shown promise in improving models for predicting patient outcomes.
However, to maximize the information gain of the radiomics features, especially in larger …

[HTML][HTML] The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review

R Reiazi, E Abbas, P Famiyeh, A Rezaie… - Computers in Biology …, 2021 - Elsevier
The field of radiomics is at the forefront of personalized medicine. However, there is concern
that high variation in imaging parameters will impact robustness of radiomic features and …

CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: Technical reproducibility of acquisition and scanners

H Hu, Q Shan, S Chen, B Li, S Feng, E Xu, X Li… - La radiologia …, 2020 - Springer
Purpose To test the technical reproducibility of acquisition and scanners of CT image-based
radiomics model for early recurrent hepatocellular carcinoma (HCC). Methods We included …

[HTML][HTML] Learning from scanners: Bias reduction and feature correction in radiomics

I Zhovannik, J Bussink, A Traverso, Z Shi… - Clinical and translational …, 2019 - Elsevier
Purpose Radiomics are quantitative features extracted from medical images. Many radiomic
features depend not only on tumor properties, but also on non-tumor related factors such as …

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives

L Dercle, T Henry, A Carré, N Paragios, E Deutsch… - Methods, 2021 - Elsevier
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last
decade due to numerous technological breakthroughs. Imaging is now playing a critical role …

Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations

A Parakh, J Cao, TT Pierce, MA Blake, CA Savage… - European …, 2021 - Springer
Objectives To investigate the image quality and perception of a sinogram-based deep
learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared …

Review of the influence of noise in X-ray computed tomography measurement uncertainty

Á Rodríguez-Sánchez, A Thompson, L Körner… - Precision …, 2020 - Elsevier
Different aspects of noise in X-ray computed tomography (XCT) for industrial purposes are
examined. An overview of the most common noise metrics is given, together with a …

Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study

M Khorrami, K Bera, P Leo, P Vaidya, P Patil… - Lung Cancer, 2020 - Elsevier
Objectives To evaluate whether combining stability and discriminability criteria in building
radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small …