Radiomics: the bridge between medical imaging and personalized medicine
Radiomics, the high-throughput mining of quantitative image features from standard-of-care
medical imaging that enables data to be extracted and applied within clinical-decision …
medical imaging that enables data to be extracted and applied within clinical-decision …
[HTML][HTML] The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review
This paper reviews objective methods for prognostic modelling of cancer tumours located
within radiology images, a process known as radiomics. Radiomics is a novel feature …
within radiology images, a process known as radiomics. Radiomics is a novel feature …
Multiparametric MRI and radiomics in prostate cancer: a review
Y Sun, HM Reynolds, B Parameswaran… - Australasian physical & …, 2019 - Springer
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging
with one or more functional MRI sequences. It has become a versatile tool for detecting and …
with one or more functional MRI sequences. It has become a versatile tool for detecting and …
[HTML][HTML] Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT
Abstract Machine learning applications for personalized medicine are highly dependent on
access to sufficient data. For personalized radiation oncology, datasets representing the …
access to sufficient data. For personalized radiation oncology, datasets representing the …
[HTML][HTML] Machine learning applications in radiation oncology
Abstract Machine learning technology has a growing impact on radiation oncology with an
increasing presence in research and industry. The prevalence of diverse data including 3D …
increasing presence in research and industry. The prevalence of diverse data including 3D …
Decision support systems for personalized and participative radiation oncology
A paradigm shift from current population based medicine to personalized and participative
medicine is underway. This transition is being supported by the development of clinical …
medicine is underway. This transition is being supported by the development of clinical …
Machine learning and modeling: data, validation, communication challenges
With the era of big data, the utilization of machine learning algorithms in radiation oncology
is rapidly growing with applications including: treatment response modeling, treatment …
is rapidly growing with applications including: treatment response modeling, treatment …
[HTML][HTML] Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer
Introduction Emerging evidence suggests that data-driven support tools have found their
way into clinical decision-making in a number of areas, including cancer care. Improving …
way into clinical decision-making in a number of areas, including cancer care. Improving …
A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and …
S Hindocha, TG Charlton, K Linton-Reid, B Hunter… - …, 2022 - thelancet.com
Background Surveillance is universally recommended for non-small cell lung cancer
(NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform …
(NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform …
What is needed to make cardiovascular models suitable for clinical decision support? A viewpoint paper
W Huberts, SGH Heinen, N Zonnebeld… - Journal of computational …, 2018 - Elsevier
The potential impact of hemodynamic and vascular wall models on the diagnosis, treatment,
and well-being of thousands of patients suffering from cardiovascular diseases, is …
and well-being of thousands of patients suffering from cardiovascular diseases, is …