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

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_1E465AAD4A48
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report.
Journal
Oncotarget
Author(s)
De Bari B., Vallati M., Gatta R., Lestrade L., Manfrida S., Carrie C., Valentini V.
ISSN
1949-2553 (Electronic)
ISSN-L
1949-2553
Publication state
Published
Issued date
12/12/2017
Peer-reviewed
Oui
Volume
8
Number
65
Pages
108509-108521
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
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.

Keywords
anal canal cancer, machine learning, predicitive models, prophylactic inguinal irradiation, radiochemotherapy
Pubmed
Web of science
Open Access
Yes
Create date
28/07/2016 16:34
Last modification date
20/08/2019 13:54
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