Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening

Autor: Saeed S. Alahmari, Dmitry Cherezov, Dmitry B. Goldgof, Lawrence O. Hall, Robert J. Gillies, Matthew B. Schabath

Publikováno v: IEEE Access, Vol 6, Pp 77796-77806 (2018)

Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite the life-saving benefit of early detection by LDCT, there are many limitations of this imaging modality including high rates of detection of inde

Externí odkaz: https://doaj.org/article/4a3a7d93d516421294393dc0901c86b2

Deep radiomics: deep learning on radiomics texture images

Autor: Matthew B. Schabath, Dmitry Cherezov, Sherzod Kariev, Rahul Paul, Lawrence O. Hall, Dmitry B. Goldgof, Robert J. Gillies

Publikováno v: Medical Imaging 2021: Computer-Aided Diagnosis.

Lung cancer has high mortality and occurrence worldwide. Radiomics is a method for extracting quantitative features from medical images that can be used for predictive analysis. Radiomics has been applied quite successfully for lung nodule malignancy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::83936a8771683a4377e8504c63757c73
https://doi.org/10.1117/12.2582102

Towards deep radiomics: nodule malignancy prediction using CNNs on feature images

Autor: Rahul Paul, Dmitry Cherezov, Lawrence O. Hall, Robert J. Gillies, Dmitry B. Goldgof, Matthew B. Schabath

Publikováno v: Medical Imaging: Computer-Aided Diagnosis

Lung cancer is a leading cause of cancer-related death worldwide and in the USA. Low Dose Computed tomography (LDCT) is the primary method of detection and diagnosis of lung cancers. Radiomics provides further analysis using LDCT scans which provide

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::39647a731617db123dba3f07d0c2048a
https://doi.org/10.1117/12.2512513

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

Autor: Jayashree Kalpathy-Cramer, Lubomir M. Hadjiiski, Jessica C. Sieren, Sandy Napel, Johanna Uthoff, Brandon Driscoll, Dmitry B. Goldgof, Sebastian Echegaray, Yoganand Balagurunathan, Pechin Lo, Artem Mamomov, Robert J. Gillies, Samantha K. N. Dilger, Binsheng Zhao, Dmitry Cherezov, Michael F. McNitt-Gray, Kenny H. Cha, Lin Lu, Ivan Yeung, Daniel L. Rubin

Publikováno v: Tomography : a journal for imaging research
Tomography
Volume 2
Issue 4
Pages 430-437
Tomography (Ann Arbor, Mich.), vol 2, iss 4
Tomography; Volume 2; Issue 4; Pages: 430-437

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 tumo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c09fe2ac136ba0c643a4c8cd914e6eff
http://europepmc.org/articles/PMC5279995

Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial

Autor: Dmitry B. Goldgof, Ying Liu, Robert J. Gillies, Dmitry Cherezov, Qian Li, Lawrence O. Hall, Matthew B. Schabath, Yoganand Balagurunathan, Samuel H. Hawkins

Publikováno v: Cancer Medicine

Background Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aec01c304534b773dd77406e6106869a
https://pubmed.ncbi.nlm.nih.gov/30507033