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

1 Citation (Scopus)

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.

LanguageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations
Subtitle of host publication9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings
EditorsHarris Papadopoulos, Andreas S. Andreou, Lazaros Iliadis, Ilias Maglogiannis
PublisherSpringer Verlag
Pages61-70
Number of pages10
ISBN (Electronic)9783642411427
ISBN (Print)9783642411410
DOIs
Publication statusPublished - 1 Dec 2013
Event9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations - Paphos, Cyprus
Duration: 30 Sep 20132 Oct 2013
Conference number: 9

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume412
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations
Abbreviated titleAIAI 2013
CountryCyprus
CityPaphos
Period30/09/132/10/13

Fingerprint

Learning systems
Irradiation
Radiotherapy
Machine learning
Prostate cancer

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 H. Papadopoulos, A. S. Andreou, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Intelligence Applications and Innovations: 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings (pp. 61-70). (IFIP Advances in Information and Communication Technology; Vol. 412). Springer Verlag. 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. Artificial Intelligence Applications and Innovations: 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings. editor / Harris Papadopoulos ; Andreas S. Andreou ; Lazaros Iliadis ; Ilias Maglogiannis. Springer Verlag, 2013. pp. 61-70 (IFIP Advances in Information and Communication Technology).
@inproceedings{26e0b558b6e640e3b85a6a499ef31363,
title = "Exploiting Machine Learning for Predicting Nodal Status in Prostate Cancer Patients",
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.",
keywords = "Classification, Machine Learning, Medicine applications",
author = "Mauro Vallati and {De Bari}, Berardino and Roberto Gatta and Michela Buglione and Magrini, {Stefano M.} and Jereczek-Fossa, {Barbara A.} and Filippo Bertoni",
year = "2013",
month = "12",
day = "1",
doi = "10.1007/978-3-642-41142-7_7",
language = "English",
isbn = "9783642411410",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer Verlag",
pages = "61--70",
editor = "Harris Papadopoulos and Andreou, {Andreas S.} and Lazaros Iliadis and Ilias Maglogiannis",
booktitle = "Artificial Intelligence Applications and Innovations",

}

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 H Papadopoulos, AS Andreou, L Iliadis & I Maglogiannis (eds), Artificial Intelligence Applications and Innovations: 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings. IFIP Advances in Information and Communication Technology, vol. 412, Springer Verlag, pp. 61-70, 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, Paphos, Cyprus, 30/09/13. 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.

Artificial Intelligence Applications and Innovations: 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings. ed. / Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. Springer Verlag, 2013. p. 61-70 (IFIP Advances in Information and Communication Technology; Vol. 412).

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

TY - GEN

T1 - Exploiting Machine Learning for Predicting Nodal Status in Prostate Cancer Patients

AU - Vallati, Mauro

AU - De Bari, Berardino

AU - Gatta, Roberto

AU - Buglione, Michela

AU - Magrini, Stefano M.

AU - Jereczek-Fossa, Barbara A.

AU - Bertoni, Filippo

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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.

AB - 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.

KW - Classification

KW - Machine Learning

KW - Medicine applications

UR - http://www.scopus.com/inward/record.url?scp=84894076780&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-41142-7_7

DO - 10.1007/978-3-642-41142-7_7

M3 - Conference contribution

SN - 9783642411410

T3 - IFIP Advances in Information and Communication Technology

SP - 61

EP - 70

BT - Artificial Intelligence Applications and Innovations

A2 - Papadopoulos, Harris

A2 - Andreou, Andreas S.

A2 - Iliadis, Lazaros

A2 - Maglogiannis, Ilias

PB - Springer Verlag

ER -

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 Papadopoulos H, Andreou AS, Iliadis L, Maglogiannis I, editors, Artificial Intelligence Applications and Innovations: 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings. Springer Verlag. 2013. p. 61-70. (IFIP Advances in Information and Communication Technology). https://doi.org/10.1007/978-3-642-41142-7_7