Results 41 to 50 of about 89,335 (330)

Radiomics for Precision Medicine in Glioblastoma [PDF]

Journal of Neuro-Oncology, 2021
Abstract Introduction: Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities ...
Kiran Aftab, Faiqa Binte Aamir, Saad Mallick, Fatima Mubarak, Whitney B. Pope, Tom Mikkelsen, Jack P. Rock, Syed Ather Enam   +7 more
openaire   +4 more sources

Radiomics: the process and the challenges [PDF]

Magnetic Resonance Imaging, 2012
"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large ...
Virendra, Kumar, Kumar, Virendra, Gu, Yuhua, Basu, Satrajit, Berglund, Anders, Eschrich, Steven A., Schabath, Matthew B., Forster, Kenneth, Aerts, Hugo J.W.L., Dekker, Andre, Fenstermacher, David, Goldgof, Dmitry B., Hall, Lawrence O., Lambin, Philippe, Balagurunathan, Yoganand, Gatenby, Robert A., Gillies, Robert J.   +16 more
openaire   +3 more sources

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. [PDF]

, 2016
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in ...
Balagurunathan, Yoganand, Cha, Kenny, Cherezov, Dmitry, Dilger, Samantha KN, Driscoll, Brandan, Echegaray, Sebastian, Gillies, Robert, Goldgof, Dmitry, Hadjiiski, Lubomir, Kalpathy-Cramer, Jayashree, Lo, Pechin, Lu, Lin, Mamomov, Artem, McNitt-Gray, Michael, Napel, Sandy, Rubin, Daniel, Sieren, Jessica C, Uthoff, Johanna, Yeung, Ivan, Zhao, Binsheng   +19 more
core   +2 more sources

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, D. Cester, S. Tanadini-Lang, H. Alkadhi, B. Baessler   +4 more
semanticscholar   +1 more source

A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors

European Radiology, 2023
Objectives To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner.
Jing-Yuan Ye, Peng Fang, Zhenpeng Peng, Xi-Tai Huang, Jin-Zhao Xie, Xiao-Yu Yin   +5 more
semanticscholar   +1 more source

Radiomics and Deep Radiomics for precision medicine

, 2022
{"references": ["E Bertelli, L Mercatelli, C Marzi, E Pachetti, M Baccini, et al. Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI., Fron in Oncology, 5515, 2021, https://doi.org/10.3389/fonc.2021.802964", "F Gioia, MA Pascali, A Greco, S Colantonio, EP Scilingo.
openaire   +1 more source

Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram

Frontiers in Oncology, 2021
ObjectiveTo build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC).MethodsTotally, 92 individuals with pathologically confirmed RMAC ...
Zhihui Li, Shuai Li, Shuqin Zang, Xiaolu Ma, Fangying Chen, Yuwei Xia, Liuping Chen, Fu Shen, Yong Lu, Jianping Lu   +9 more
doaj   +1 more source

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software. [PDF]

, 2017
ObjectiveThe purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software.Materials and methodsMR images of 45 ...
Jamshidi, Neema, Kim, Jong Hyo, Kuo, Michael D, Lee, Myungeun, Woo, Boyeong   +4 more
core   +1 more source

Radiomics in colorectal cancer patients

World Journal of Gastroenterology, 2023
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging.
Riccardo Inchingolo, C. Maino, R. Cannella, F. Vernuccio, F. Cortese, M. Dezio, A. Pisani, T. Giandola, M. Gatti, V. Giannini, D. Ippolito, R. Faletti   +11 more
semanticscholar   +1 more source

Deep segmentation networks predict survival of non-small cell lung cancer [PDF]

, 2019
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed
Allen, Bryan, Baek, Stephen, Baek, Stephen, Buatti, John, Cabel, Katherine, Diehn, Maximilian, Gannon, Maggie, He, Yusen, Kim, Yusung, Loo, Billy, Plichta, Kristin, Seyedin, Steven, Smith, Brian, Sun, Zhiyu, Tong, Ling, Wu, Jia, Wu, Xiaodong   +16 more
core   +2 more sources