NCTN assessment on current applications of radiomics in oncology
K Nie, H Al-Hallaq, XA Li, SH Benedict, JW Sohn… - International Journal of …, 2019 - Elsevier
Radiomics is a fast-growing research area based on converting standard-of-care imaging
into quantitative minable data and building subsequent predictive models to personalize …
into quantitative minable data and building subsequent predictive models to personalize …
Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands
Purpose To analyze baseline CT/MR-based image features of salivary glands to predict
radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy …
radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy …
Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases
A Wu, Y Li, M Qi, X Lu, Q Jia, F Guo, Z Dai, Y Liu… - Oral oncology, 2020 - Elsevier
Objectives To investigate whether dosiomics can benefit to IMRT treated patient's
locoregional recurrences (LR) prediction through a comparative study on prediction …
locoregional recurrences (LR) prediction through a comparative study on prediction …
[HTML][HTML] Machine learning methods uncover radiomorphologic dose patterns in salivary glands that predict xerostomia in patients with head and neck cancer
Purpose Patients with head-and-neck cancer (HNC) may experience xerostomia after
radiation therapy (RT), which leads to compromised quality of life. The purpose of this study …
radiation therapy (RT), which leads to compromised quality of life. The purpose of this study …
Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a …
Background This study aimed to investigate the value of clinical, radiomic features extracted
from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics) …
from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics) …
The needs and benefits of continuous model updates on the accuracy of RT-induced toxicity prediction models within a learning health system
Purpose Clinical data collection and development of outcome prediction models by machine
learning can form the foundation for a learning health system offering precision radiation …
learning can form the foundation for a learning health system offering precision radiation …
Needs and challenges for radiation oncology in the era of precision medicine
Modern medicine, including the care of the cancer patient, has significantly advanced, with
the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now …
the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now …
Utility of a clinical decision support system in weight loss prediction after head and neck cancer radiotherapy
PURPOSE To evaluate the utility of a clinical decision support system (CDSS) using a
weight loss prediction model. METHODS A prediction model for significant weight loss (loss …
weight loss prediction model. METHODS A prediction model for significant weight loss (loss …
Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer
A Talebi, A Bitarafan‐Rajabi… - Journal of Applied …, 2024 - Wiley Online Library
Purpose Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly
affecting patient outcomes. To improve the likelihood of favorable outcomes for breast …
affecting patient outcomes. To improve the likelihood of favorable outcomes for breast …
The role of the quality control system for diagnostics of oncological diseases in radiomics
AN Khoruzhaya, ES Ahkmad… - Digital …, 2021 - jdigitaldiagnostics.com
Modern medical imaging methods allow for both qualitative and quantitative evaluations of
tumors and issues surrounding them. Advances in computer science and big data …
tumors and issues surrounding them. Advances in computer science and big data …