Results 41 to 50 of about 82,903 (262)

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

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

Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection [PDF]

, 2017
While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial.
Shafiee, Mohammad Javad, Wong, Alexander
core   +3 more sources

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

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

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

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

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

Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Scientific Data, 2023
Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols.
Miriam Cobo, P. Menéndez Fernández-Miranda, G. Bastarrika, L. Lloret Iglesias   +3 more
semanticscholar   +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