[HTML][HTML] The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges
Medical imaging can assess the tumor and its environment in their entirety, which makes it
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
Machine and deep learning methods for radiomics
Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …
extracted imaging information to clinical and biological endpoints. The development of …
Deep learning predicts lung cancer treatment response from serial medical imaging
Purpose: Tumors are continuously evolving biological systems, and medical imaging is
uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking …
uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking …
[HTML][HTML] Deep learning classification of lung cancer histology using CT images
TL Chaunzwa, A Hosny, Y Xu, A Shafer, N Diao… - Scientific reports, 2021 - nature.com
Tumor histology is an important predictor of therapeutic response and outcomes in lung
cancer. Tissue sampling for pathologist review is the most reliable method for histology …
cancer. Tissue sampling for pathologist review is the most reliable method for histology …
[HTML][HTML] Repeatability and reproducibility of radiomic features: a systematic review
Purpose An ever-growing number of predictive models used to inform clinical decision
making have included quantitative, computer-extracted imaging biomarkers, or “radiomic …
making have included quantitative, computer-extracted imaging biomarkers, or “radiomic …
Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study
Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …
courses and outcomes, even within the same tumor stage. This study explores deep …
[HTML][HTML] Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
S Trebeschi, SG Drago, NJ Birkbak, I Kurilova… - Annals of …, 2019 - Elsevier
Introduction Immunotherapy is regarded as one of the major breakthroughs in cancer
treatment. Despite its success, only a subset of patients responds—urging the quest for …
treatment. Despite its success, only a subset of patients responds—urging the quest for …
Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
MR Chetan, FV Gleeson - European radiology, 2021 - Springer
Objectives Radiomics is the extraction of quantitative data from medical imaging, which has
the potential to characterise tumour phenotype. The radiomics approach has the capacity to …
the potential to characterise tumour phenotype. The radiomics approach has the capacity to …
Radiomics and radiogenomics in lung cancer: a review for the clinician
Lung cancer is responsible for a large proportion of cancer-related deaths across the globe,
with delayed detection being perhaps the most significant factor for its high mortality rate …
with delayed detection being perhaps the most significant factor for its high mortality rate …
Radiomics and deep learning in lung cancer
Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …
learning. By providing a three-dimensional characterization of the lesion, models based on …