Results 31 to 40 of about 71,655 (241)

Introduction to Radiomics

Journal of Nuclear Medicine, 2020
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics—the so-called radiomic features—within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and
M. Mayerhoefer, A. Materka, G. Langs, I. Häggström, Piotr M. Szczypiński, P. Gibbs, G. Cook   +6 more
semanticscholar   +1 more source

Development and validation of a radiomics-based nomogram for predicting a major pathological response to neoadjuvant immunochemotherapy for patients with potentially resectable non-small cell lung cancer

Frontiers in Immunology, 2023
Introduction The treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects.
Chaoyuan Liu, Wei Zhao, Junpeng Xie, Huashan Lin, Xingsheng Hu, Chang Li, Youlan Shang, Yapeng Wang, Yingjia Jiang, Mengge Ding, Muyun Peng, T. Xu, Ao’ran Hu, Yuda Huang, Yuan Gao, Xianling Liu, J. Liu, Fang Ma   +17 more
semanticscholar   +1 more source

Uncontrolled Confounders May Lead to False or Overvalued Radiomics Signature: A Proof of Concept Using Survival Analysis in a Multicenter Cohort of Kidney Cancer

Frontiers in Oncology, 2021
PurposeWe aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them.Materials and MethodsPreoperative contrast ...
Lin Lu, Firas S. Ahmed, Oguz Akin, Lyndon Luk, Xiaotao Guo, Hao Yang, Jin Yoon, A. Aari Hakimi, Lawrence H. Schwartz, Binsheng Zhao   +9 more
doaj   +1 more source

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Nature Communications, 2014
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features.
H. Aerts, E. R. Velazquez, R. Leijenaar, C. Parmar, P. Grossmann, Sara Cavalho, J. Bussink, R. Monshouwer, Benjamin Haibe-Kains, D. Rietveld, F. Hoebers, M. Rietbergen, C. R. Leemans, A. Dekker, John Quackenbush, R. Gillies, P. Lambin   +16 more
semanticscholar   +1 more source

Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients

Journal of Hematology & Oncology, 2022
Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features ...
Renjie Wang, W. Dai, J. Gong, Mingzhu Huang, T. Hu, Hang Li, Kailin Lin, C. Tan, Hongbo Hu, T. Tong, G. Cai   +10 more
semanticscholar   +1 more source

The transformational potential of molecular radiomics

Journal of Medical Radiation Sciences, 2023
Conventional radiomics in nuclear medicine involve hand‐crafted and computer‐assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI‐augmented segmentation and extraction of lower order traditional ...
Geoffrey Currie, K Elizabeth Hawk, Eric Rohren   +2 more
doaj   +1 more source

The predictive value of radiomics-based machine learning for peritoneal metastasis in gastric cancer patients: a systematic review and meta-analysis

Frontiers in Oncology, 2023
BackgroundFor patients with gastric cancer (GC), effective preoperative identification of peritoneal metastasis (PM) remains a severe challenge in clinical practice. Regrettably, effective early identification tools are still lacking up to now.
Fan Zhang, Guoxue Wu, Nan Chen, Ruyue Li
doaj   +1 more source

Future Artificial Intelligence tools and perspectives in medicine [PDF]

Curr Opin Urol. 2021 Jul 1;31(4):371-377, 2022
Purpose of review: Artificial intelligence (AI) has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical variables, etc.) and used the statistical and machine learning methods to measure the model ...
arxiv   +1 more source

Radiomics in medical imaging—“how-to” guide and critical reflection

Insights into Imaging, 2020
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis.
J. V. van Timmeren, Davide Cester, S. Tanadini-Lang, H. Alkadhi, B. Baessler   +4 more
semanticscholar   +1 more source

Towards reproducible radiomics research: introduction of a database for radiomics studies

European Radiology, 2023
Objectives To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database. Methods A total of 1254 articles published between January 1, 2021, and December 31, 2022,
Tugba Akinci D'Antonoli, R. Cuocolo, B. Baessler, D. Pinto dos Santos   +3 more
semanticscholar   +1 more source