Results 141 to 150 of about 71,655 (241)

Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas

, 2016
Ying Liu, Jongphil Kim, Yoganand Balagurunathan, Qian Li, Alberto López García, Olya Stringfield, Zhaoxiang Ye, Robert J. Gillies   +7 more
openalex   +1 more source

Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status [PDF]

, 2017
Dan Ou, Pierre Blanchard, Silvia Rosellini, Antonin Lévy, F. Nguyen, Ralph T. H. Leijenaar, Ingrid Garberis, Philippe Gorphe, François Bidault, Charles Ferté, Charlotte Robert, Odile Casiraghi, Jean‐Yves Scoazec, Philippe Lambin, Stéphane Temam, Éric Deutsch, Yungan Tao   +16 more
openalex   +1 more source

Radiomics: extracting more information from medical images using advanced feature analysis.

European Journal of Cancer, 2012
P. Lambin, Emmanuel Rios-Velazquez, R. Leijenaar, S. Carvalho, R. V. van Stiphout, P. Granton, C. Zegers, R. Gillies, R. Boellard, A. Dekker, H. Aerts   +10 more
semanticscholar   +1 more source

Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics [PDF]

arXiv
Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on the acquisition and pre-processing techniques used.
arxiv  

Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges [PDF]

, 2017
Hesham Elhalawani, Abdallah Mohamed, Aubrey L. White, James Zafereo, Andrew Wong, Joel Berends, Shady Abohashem, Bowman Williams, Jeremy M. Aymard, Aasheesh Kanwar, Subha Perni, Crosby D. Rock, Luke C. Cooksey, Shauna Campbell, Yao Ding, Stephen Y. Lai, Elisabeta Marai, David M. Vock, Guadalupe Canahuate, John Freymann, Keyvan Farahani, Jayashree Kalpathy–Cramer, Clifton D. Fuller   +22 more
openalex   +1 more source

Machine learning‐based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

Thoracic Cancer
Background Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity
Yuan Li, Baihan Lyu, Rong Wang, Yue Peng, Haoyu Ran, Bolun Zhou, Yang Liu, Guangyu Bai, Qilin Huai, Xiaowei Chen, Chun Zeng, Qingchen Wu, Cheng Zhang, Shugeng Gao   +13 more
doaj   +1 more source

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis [PDF]

arXiv
Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve breast cancer (BC) diagnosis and subtype differentiation.
arxiv  

Reproducibility with repeat CT in radiomics study for rectal cancer

, 2016
Panpan Hu, Jiazhou Wang, Haoyu Zhong, Zhen Zhou, Lijun Shen, Weigang Hu, Zhen Zhang   +6 more
openalex   +2 more sources

NIMG-32. DIFFERENTIATION OF PSEUDOPROGRESSION FROM TUMOR PROGRESSION IN GLIOBLASTOMA PATIENTS BASED ON FET PET RADIOMICS [PDF]

, 2017
Philipp Lohmann, Christoph Lerche, Elena Bauer, Jan Steger, Gabriele Stoffels, Tobias Blau, Veronika Dunkl, Christian Filß, Carina Stegmayr, Bernd Neumaier, N. Jon Shah, Gereon R. Fink, Karl‐Josef Langen, Norbert Galldiks   +13 more
openalex   +1 more source

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

Nature Reviews Clinical Oncology, 2021
K. Bera, Nathaniel Braman, Amit Gupta, V. Velcheti, A. Madabhushi   +4 more
semanticscholar   +1 more source