Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report.

Détails

Ressource 1Télécharger: 29312547_BIB_1E465AAD4A48.pdf (1959.15 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_1E465AAD4A48
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report.
Périodique
Oncotarget
Auteur(s)
De Bari B., Vallati M., Gatta R., Lestrade L., Manfrida S., Carrie C., Valentini V.
ISSN
1949-2553 (Electronic)
ISSN-L
1949-2553
Statut éditorial
Publié
Date de publication
12/12/2017
Peer-reviewed
Oui
Volume
8
Numéro
65
Pages
108509-108521
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
The role of prophylactic inguinal irradiation (PII) in the treatment of anal cancer patients is controversial. We developped an innovative algorithm based on the Machine Learning (ML) allowing the tailoring of the prescription of PII.
Once verified on the independent testing set, J48 showed the better performances, with specificity, sensitivity, and accuracy rates in predicting relapsing patients of 86.4%, 50.0% and 83.1% respectively (vs 36.5%, 90.4% and 80.25%, respectively, for LR).
We classified 194 anal cancer patients with Logistic Regression (LR) and other 3 ML techniques based on decision trees (J48, Random Tree and Random Forest), using a large set of clinical and therapeutic variables. We tested obtained ML algorithms on an independent testing set of 65 anal cancer patients. TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) methodology was used for the development, the Quality Assurance and the description of the experimental procedures.
In an internationally approved quality assurance framework, ML seems promising in predicting the outcome of patients that would benefit or not of the PII. Once confirmed in larger and/or multi-centric databases, ML could support the physician in tailoring the treatment and in deciding if deliver or not the PII.

Mots-clé
anal canal cancer, machine learning, predicitive models, prophylactic inguinal irradiation, radiochemotherapy
Pubmed
Web of science
Open Access
Oui
Création de la notice
28/07/2016 15:34
Dernière modification de la notice
20/08/2019 12:54
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