Decision support systems for the prediction of lymph node involvement in early breast cancer.

Research output: Contribution to journalArticlepeer-review


The prediction of lymph node involvement represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies. An artificial intelligence model able to predict it in pre-operative phase requires the interface among multiple data structures. The trade-off among time consuming, expensive and invasive methodologies is emerging in experimental setups exploited for the analysis of sentinel lymph nodes, where machine learning algorithms represent a key ingredient in recorded data elaboration. The accuracy required for clinical applications is obtainable matching different kind of data. Statistical associations of prognostic factors with symptoms and predictive models implemented also through on-line softwares represent useful diagnostic support tools which translate into patients quality of life improvement and costs reduction.

Original languageEnglish
Pages (from-to)275-277
Number of pages3
JournalJournal of B.U.ON.
Issue number1
Publication statusPublished - Jan 2021


  • Bioinformatics
  • Complex diseases
  • Data science
  • Machine learning
  • Personalized medicine

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research


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