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

De Bari, Berardino, Vallati, Mauro, Gatta, Roberto, Lestrade, Laëtitia, Manfrida, Stefania, Carrie, Christian and Valentini, Vincenzo (2017) Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report. Oncotarget, 8 (65). pp. 108509-108521. ISSN 1949-2553

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Abstract

Introduction: 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.
Results: 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).
Methods: 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.
Conclusion: 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.

Item Type: Article
Uncontrolled Keywords: anal canal cancer, radiochemotherapy, prophylactic inguinal irradiation, machine learning, predicitive models
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Sally Hughes
Date Deposited: 25 Jan 2018 16:32
Last Modified: 25 Jan 2018 16:37
URI: http://eprints.hud.ac.uk/id/eprint/34374

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