[HTML][HTML] Radiomics with artificial intelligence: a practical guide for beginners
Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high
number of quantitative features from medical images. Artificial intelligence (AI) is broadly a …
number of quantitative features from medical images. Artificial intelligence (AI) is broadly a …
Artificial intelligence-driven assessment of radiological images for COVID-19
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of
COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients …
COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients …
CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma
Objectives To evaluate the utility of CT-based radiomics signatures in discriminating low-
grade (grades 1–2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3–4) …
grade (grades 1–2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3–4) …
Quality control and whole-gland, zonal and lesion annotations for the PROSTATEx challenge public dataset
Purpose Radiomic features are promising quantitative parameters that can be extracted from
medical images and employed to build machine learning predictive models. However …
medical images and employed to build machine learning predictive models. However …
Radiomics in stratification of pancreatic cystic lesions: Machine learning in action
Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their
diagnosis can be challenging as their behavior varies from benign to malignant disease …
diagnosis can be challenging as their behavior varies from benign to malignant disease …
MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal …
Objective To interrogate the mesorectal fat using MRI radiomics feature analysis in order to
predict clinical outcomes in patients with locally advanced rectal cancer. Methods This …
predict clinical outcomes in patients with locally advanced rectal cancer. Methods This …
Texture analysis in cerebral gliomas: a review of the literature
Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify
macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity …
macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity …
DCE-MRI radiomics analysis in differentiating luminal A and luminal B breast cancer molecular subtypes
O Lafcı, P Celepli, PS Öztekin, PN Koşar - Academic Radiology, 2023 - Elsevier
Rationale and Objectives The aim of the present study was to investigate the association
between Luminal A and Luminal B molecular subtypes and radiomic features of dynamic …
between Luminal A and Luminal B molecular subtypes and radiomic features of dynamic …
Texture analysis imaging “what a clinical radiologist needs to know”
Texture analysis has arisen as a tool to explore the amount of data contained in images that
cannot be explored by humans visually. Radiomics is a method that extracts a large number …
cannot be explored by humans visually. Radiomics is a method that extracts a large number …
Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma
AIM To investigate machine learning based models combining clinical, radiomic, and
molecular information to distinguish between early true progression (tPD) and …
molecular information to distinguish between early true progression (tPD) and …