A review in radiomics: making personalized medicine a reality via routine imaging
J Guiot, A Vaidyanathan, L Deprez… - Medicinal research …, 2022 - Wiley Online Library
Radiomics is the quantitative analysis of standard‐of‑care medical imaging; the information
obtained can be applied within clinical decision support systems to create diagnostic …
obtained can be applied within clinical decision support systems to create diagnostic …
Radiomics: a primer on high-throughput image phenotyping
Radiomics is a high-throughput approach to image phenotyping. It uses computer
algorithms to extract and analyze a large number of quantitative features from radiological …
algorithms to extract and analyze a large number of quantitative features from radiological …
[HTML][HTML] Initiatives, concepts, and implementation practices of the findable, accessible, interoperable, and reusable data principles in health data stewardship: scoping …
ET Inau, J Sack, D Waltemath, AA Zeleke - Journal of Medical Internet …, 2023 - jmir.org
Background Thorough data stewardship is a key enabler of comprehensive health research.
Processes such as data collection, storage, access, sharing, and analytics require …
Processes such as data collection, storage, access, sharing, and analytics require …
Radiomics for predicting lung cancer outcomes following radiotherapy: a systematic review
GM Walls, SOS Osman, KH Brown, KT Butterworth… - Clinical oncology, 2022 - Elsevier
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with
respect to radical radiotherapy. At present there are no validated biomarkers available for …
respect to radical radiotherapy. At present there are no validated biomarkers available for …
RRc-UNet 3D for lung tumor segmentation from CT scans of Non-Small Cell Lung Cancer patients
Lung cancer is a grave disease that accounts for more than one million deaths, and Non-
Small Cell Lung Cancer (NSCLC) accounts for 85% of all lung cancers. Rapid detection of …
Small Cell Lung Cancer (NSCLC) accounts for 85% of all lung cancers. Rapid detection of …
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation
Accurate and robust segmentation of lung cancers from CT, even those located close to
mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure …
mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure …
[HTML][HTML] Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach
Y Yimit, P Yasin, A Tuersun, A Abulizi, W Jia… - European Journal of …, 2023 - Springer
Background Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share
similar in locations and imaging appearance. However, they require distinct treatment …
similar in locations and imaging appearance. However, they require distinct treatment …
Implementation of big imaging data pipeline adhering to FAIR principles for federated machine learning in oncology
Cancer is a fatal disease and one of the leading causes of death worldwide. The cure rate in
cancer treatment remains low; hence, cancer treatment is gradually shifting toward …
cancer treatment remains low; hence, cancer treatment is gradually shifting toward …
[PDF][PDF] Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)
48 49 Public datasets play a key role in enabling the medical research community to
validate and build 50 upon each other works using data acquired outside of their home …
validate and build 50 upon each other works using data acquired outside of their home …
[HTML][HTML] Making radiotherapy more efficient with FAIR data
Given the rapid growth of artificial intelligence (AI) applications in radiotherapy and the
related transformations toward the data-driven healthcare domain, this article summarizes …
related transformations toward the data-driven healthcare domain, this article summarizes …