Results 31 to 40 of about 89,335 (330)

Radiomics in the diagnosis of glioblastoma

Polish Journal of Radiology, 2023
Radiomics is a process of extracting many quantitative data obtained from medical images and analysing them. In neuroradiology it may be used to discover magnetic resonance imaging (MRI) features of glioblastomas that are impossible to identify by human vision alone.
Kwiatkowska-Miernik, Agnieszka, Mruk, Bartosz, Sklinda, Katarzyna, Zaczyński, Artur, Walecki, Jerzy   +4 more
openaire   +2 more sources

Radiomics of lung cancer [PDF]

Journal of Thoracic Oncology, 2016
Submillisecond pulses of radiation have been shown to generate less exchange chromosomal aberrations [1, 2] and a smaller extent of delayed cell death [3, 4] than continuous irradiation delivered at conventional dose-rate. This prompted us to determine whether and how pulsed irradiation affects the response of normal lung tissue in vivo.
Robert J. Gillies, Goldgof Dmitry, Olya Stringfield, Matthew B. Schabath, Hall Lawrence, Yoganand Balagurunathan, Hawkins Samuel, Ying Liu, Qian Li   +8 more
openaire   +2 more sources

Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma

Scientific Reports, 2021
Glioblastoma is the most common primary brain tumor. Standard therapy consists of maximum safe resection combined with adjuvant radiochemotherapy followed by chemotherapy with temozolomide, however prognosis is extremely poor.
Alonso Garcia-Ruiz, Pablo Naval-Baudin, Marta Ligero, Albert Pons-Escoda, Jordi Bruna, Gerard Plans, Nahum Calvo, Monica Cos, Carles Majós, Raquel Perez-Lopez   +9 more
doaj   +1 more source

Exploring the Value of Radiomics Features Based on B-Mode and Contrast-Enhanced Ultrasound in Discriminating the Nature of Thyroid Nodules

Frontiers in Oncology, 2021
BackgroundWith the improvement of ultrasound imaging resolution and the application of various new technologies, the detection rate of thyroid nodules has increased greatly in recent years. However, there are still challenges in accurately diagnosing the
Shi Yan Guo, Ping Zhou, Yan Zhang, Li Qing Jiang, Yong Feng Zhao   +4 more
doaj   +1 more source

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Journal of Clinical Medicine, 2023
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases,
F. Pesapane, P. De Marco, Anna Rapino, Eleonora Lombardo, L. Nicosia, Priyan Tantrige, A. Rotili, A. Bozzini, S. Penco, V. Dominelli, C. Trentin, F. Ferrari, Mariagiorgia Farina, L. Meneghetti, A. Latronico, F. Abbate, D. Origgi, G. Carrafiello, E. Cassano   +18 more
semanticscholar   +1 more source

Radiomics: Images Are More than Pictures, They Are Data

Radiology, 2015
This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
R. Gillies, Paul Kinahan, H. Hricak
semanticscholar   +1 more source

Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI

BMC Medical Imaging, 2023
Background The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer.
Hasan Khanfari, Saeed Mehranfar, Mohsen Cheki, Mahmoud Mohammadi Sadr, Samir Moniri, Sahel Heydarheydari, S. M. Rezaeijo   +6 more
semanticscholar   +1 more source

Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features

SSRN Electronic Journal, 2022
Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment response assessment. Different image discretization parameters (e.g. bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels can be ...
Rahmim, Arman, Toosi, Amirhosein, Salmanpour, Mohammad R., Dubljevic, Natalia, Janzen, Ian, Shiri Lord, Isaac, Yuan, Ren, Ho, Cheryl, Zaidi, Habib, MacAulay, Calum, Uribe, Carlos, Yousefirizi, Fereshteh   +11 more
openaire   +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

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