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

Vincenzo Valentini, Roberto Gatta, Stefania Manfrida, Berardino De Bari, Mauro Vallati, Laëtitia Lestrade, Christian Carrie

Risultato della ricerca: Contributo in rivistaArticolo in rivista

5 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)108509-108521
Numero di pagine13
RivistaOncotarget
Volume8
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Anal canal cancer
  • Machine learning
  • Oncology
  • Predicitive models
  • Prophylactic inguinal irradiation
  • Radiochemotherapy

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