Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

Détails

ID Serval
serval:BIB_D2C8C19D0F93
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.
Périodique
Cancer Investigation
Auteur(s)
De Bari B., Vallati M., Gatta R., Simeone C., Girelli G., Ricardi U., Meattini I., Gabriele P., Bellavita R., Krengli M., Cafaro I., Cagna E., Bunkheila F., Borghesi S., Signor M., Di Marco A., Bertoni F., Stefanacci M., Pasinetti N., Buglione M., Magrini S.M.
ISSN
1532-4192 (Electronic)
ISSN-L
0735-7907
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
33
Numéro
6
Pages
232-240
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.
Mots-clé
Aged, Aged, 80 and over, Algorithms, Artificial Intelligence, Decision Trees, Humans, Lymphatic Metastasis/diagnosis, Male, Middle Aged, Pelvis/pathology, Pilot Projects, Prostatic Neoplasms/pathology, Sensitivity and Specificity
Pubmed
Web of science
Création de la notice
13/10/2015 18:17
Dernière modification de la notice
20/08/2019 15:52
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