Exploiting Machine Learning for predicting nodal status in prostate cancer patients

Mauro Vallati, Berardino De Bari, Roberto Gatta, Michela Buglione, Stefano M. Magrini, Barbara A. Jereczek-Fossa, Filippo Bertoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prostate cancer is the second cause of cancer in males. The prophylactic pelvic irradiation is usually needed for treating prostate cancer patients with Subclinical Nodal Metestases. Currently, the physicians decide when to deliver pelvic irradiation in nodal negative patients mainly by using the Roach formula, which gives an approximate estimation of the risk of Subclinical Nodal Metestases. In this paper we study the exploitation of Machine Learning techniques for training models, based on several pre-treatment parameters, that can be used for predicting the nodal status of prostate cancer patients. An experimental retrospective analysis, conducted on the largest Italian database of prostate cancer patients treated with radical External Beam Radiation Therapy, shows that the proposed approaches can effectively predict the nodal status of patients.

Original languageEnglish
Title of host publicationIFIP Advances in Information and Communication Technology
Pages61-70
Number of pages10
Volume412
DOIs
Publication statusPublished - 2013
Event9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013 - Paphos, Cyprus
Duration: Sep 30 2013Oct 2 2013

Publication series

NameIFIP Advances in Information and Communication Technology
Volume412
ISSN (Print)18684238

Other

Other9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013
CountryCyprus
CityPaphos
Period9/30/1310/2/13

Fingerprint

Machine learning
Prostate cancer
Irradiation
Exploitation
Data base
Cancer
Physicians
Radiation
Therapy
Pretreatment

Keywords

  • Classification
  • Machine Learning
  • Medicine applications

ASJC Scopus subject areas

  • Information Systems and Management

Cite this

Vallati, M., De Bari, B., Gatta, R., Buglione, M., Magrini, S. M., Jereczek-Fossa, B. A., & Bertoni, F. (2013). Exploiting Machine Learning for predicting nodal status in prostate cancer patients. In IFIP Advances in Information and Communication Technology (Vol. 412, pp. 61-70). (IFIP Advances in Information and Communication Technology; Vol. 412). https://doi.org/10.1007/978-3-642-41142-7_7

Exploiting Machine Learning for predicting nodal status in prostate cancer patients. / Vallati, Mauro; De Bari, Berardino; Gatta, Roberto; Buglione, Michela; Magrini, Stefano M.; Jereczek-Fossa, Barbara A.; Bertoni, Filippo.

IFIP Advances in Information and Communication Technology. Vol. 412 2013. p. 61-70 (IFIP Advances in Information and Communication Technology; Vol. 412).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Vallati, M, De Bari, B, Gatta, R, Buglione, M, Magrini, SM, Jereczek-Fossa, BA & Bertoni, F 2013, Exploiting Machine Learning for predicting nodal status in prostate cancer patients. in IFIP Advances in Information and Communication Technology. vol. 412, IFIP Advances in Information and Communication Technology, vol. 412, pp. 61-70, 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013, Paphos, Cyprus, 9/30/13. https://doi.org/10.1007/978-3-642-41142-7_7
Vallati M, De Bari B, Gatta R, Buglione M, Magrini SM, Jereczek-Fossa BA et al. Exploiting Machine Learning for predicting nodal status in prostate cancer patients. In IFIP Advances in Information and Communication Technology. Vol. 412. 2013. p. 61-70. (IFIP Advances in Information and Communication Technology). https://doi.org/10.1007/978-3-642-41142-7_7
Vallati, Mauro ; De Bari, Berardino ; Gatta, Roberto ; Buglione, Michela ; Magrini, Stefano M. ; Jereczek-Fossa, Barbara A. ; Bertoni, Filippo. / Exploiting Machine Learning for predicting nodal status in prostate cancer patients. IFIP Advances in Information and Communication Technology. Vol. 412 2013. pp. 61-70 (IFIP Advances in Information and Communication Technology).
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