[HTML][HTML] Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI)
field. Modern radiation oncology is based on the exploitation of advanced computational …
field. Modern radiation oncology is based on the exploitation of advanced computational …
[HTML][HTML] Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review
Radiomics research is rapidly growing in recent years, but more concerns on radiomics
reliability are also raised. This review attempts to update and overview the current status of …
reliability are also raised. This review attempts to update and overview the current status of …
A guide to artificial intelligence for cancer researchers
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …
a readily accessible tool for cancer researchers. AI-based tools can boost research …
A review of radiomics and genomics applications in cancers: the way towards precision medicine
S Li, B Zhou - Radiation Oncology, 2022 - Springer
The application of radiogenomics in oncology has great prospects in precision medicine.
Radiogenomics combines large volumes of radiomic features from medical digital images …
Radiogenomics combines large volumes of radiomic features from medical digital images …
Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
M Cobo, P Menéndez Fernández-Miranda… - Scientific data, 2023 - nature.com
Recent advances in computer-aided diagnosis, treatment response and prognosis in
radiomics and deep learning challenge radiology with requirements for world-wide …
radiomics and deep learning challenge radiology with requirements for world-wide …
Generalizability of machine learning models: quantitative evaluation of three methodological pitfalls
Purpose To investigate the impact of the following three methodological pitfalls on model
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …
The application of radiomics in predicting gene mutations in cancer
Y Qi, T Zhao, M Han - European radiology, 2022 - Springer
With the development of genome sequencing, the role of molecular targeted therapy in
cancer is becoming increasingly important. However, genetic testing remains expensive …
cancer is becoming increasingly important. However, genetic testing remains expensive …
[HTML][HTML] Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics
B Koçak - Diagnostic and Interventional Radiology, 2022 - ncbi.nlm.nih.gov
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology
research to deal with large and complex imaging data sets. Nowadays, ML tools have …
research to deal with large and complex imaging data sets. Nowadays, ML tools have …
The effects of in-plane spatial resolution on CT-based radiomic features' stability with and without ComBat harmonization
Simple Summary Handcrafted radiomic features (HRFs) are quantitative features extracted
from medical images, and they are mined for associations with different clinical endpoints …
from medical images, and they are mined for associations with different clinical endpoints …
Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation
Objectives In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative
prognostic biomarkers are available. Radiomics has demonstrated potential but lacks …
prognostic biomarkers are available. Radiomics has demonstrated potential but lacks …