Automated Author Profile

Henschke, Claudia I.

Current S-Index

239.3

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

239.3

Average Dataset Index per dataset

Total Datasets

1

Total datasets for this author

Average FAIR Score

84.6%

Average FAIR Score per dataset

Total Citations

345

Total citations to the author's datasets

Total Mentions

110

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Data From LIDC-IDRI (Version: 4)

The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

Authors

  • Armato III, Samuel G. ;
  • McLennan, Geoffrey ;
  • Bidaut, Luc ;
  • McNitt-Gray, Michael F. ;
  • Meyer, Charles R. ;
  • Reeves, Anthony P. ;
  • Zhao, Binsheng ;
  • Aberle, Denise R. ;
  • Henschke, Claudia I. ;
  • Hoffman, Eric A. ;
  • Kazerooni, Ella A. ;
  • MacMahon, Heber ;
  • Van Beek, Edwin J.R. ;
  • Yankelevitz, David ;
  • Biancardi, Alberto M. ;
  • Bland, Peyton H. ;
  • Brown, Matthew S. ;
  • Engelmann, Roger M. ;
  • Laderach, Gary E. ;
  • Max, Daniel ;
  • Pais, Richard C. ;
  • Qing, David P.Y. ;
  • Roberts, Rachael Y. ;
  • Smith, Amanda R. ;
  • Starkey, Adam ;
  • Batra, Poonam ;
  • Caligiuri, Philip ;
  • Farooqi, Ali ;
  • Gladish, Gregory W. ;
  • Jude, C. Matilda ;
  • Munden, Reginald F. ;
  • Petkovska, Iva ;
  • Quint, Leslie E. ;
  • Schwartz, Lawrence H. ;
  • Sundaram, Baskaran ;
  • Dodd, Lori E. ;
  • Fenimore, Charles ;
  • Gur, David ;
  • Petrick, Nicholas ;
  • Freymann, John ;
  • Kirby, Justin ;
  • Hughes, Brian ;
  • Casteele, Alessi Vande ;
  • Gupte, Sangeeta ;
  • Sallam, Maha ;
  • Heath, Michael D. ;
  • Kuhn, Michael H. ;
  • Dharaiya, Ekta ;
  • Burns, Richard ;
  • Fryd, David S. ;
  • Salganicoff, Marcos ;
  • Anand, Vikram ;
  • Shreter, Uri ;
  • Vastagh, Stephen ;
  • Croft, Barbara Y. ;
  • Clarke, Laurence P.
345 Citations110 Mentions85% FAIR240.0 Dataset Index
10.7937/k9/tcia.2015.lo9ql9sx2015