Your browser doesn't support javascript.
loading
Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma.
Wang, Du; Lee, Sang Ho; Geng, Huaizhi; Zhong, Haoyu; Plastaras, John; Wojcieszynski, Andrzej; Caruana, Richard; Xiao, Ying.
Affiliation
  • Wang D; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
  • Lee SH; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
  • Geng H; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
  • Zhong H; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
  • Plastaras J; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
  • Wojcieszynski A; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
  • Caruana R; Microsoft Research, Redmond, WA, United States.
  • Xiao Y; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States.
Front Artif Intell ; 5: 1059033, 2022.
Article in En | MEDLINE | ID: mdl-36568580
Key words
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Prognostic_studies / Risk_factors_studies Language: En Journal: Front Artif Intell Year: 2022 Type: Article Affiliation country: United States
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Prognostic_studies / Risk_factors_studies Language: En Journal: Front Artif Intell Year: 2022 Type: Article Affiliation country: United States