A data mining based clinical decision support system for survival in lung cancer - Sorbonne Université
Article Dans Une Revue Reports of Practical Oncology & Radiotherapy Année : 2021

A data mining based clinical decision support system for survival in lung cancer

Beatriz Pontes
  • Fonction : Auteur
Francisco Núñez
  • Fonction : Auteur
Cristina Rubio
  • Fonction : Auteur
Alberto Moreno
  • Fonction : Auteur
Isabel Nepomuceno
  • Fonction : Auteur
Jesús Moreno
  • Fonction : Auteur
Jon Cacicedo
  • Fonction : Auteur
Juan Manuel Praena-Fernandez
  • Fonction : Auteur
Carlos Parra
  • Fonction : Auteur
Blas David Delgado León
  • Fonction : Auteur
Felipe Couñago
  • Fonction : Auteur
Jose Riquelme
  • Fonction : Auteur
Jose Luis Lopez Guerra
  • Fonction : Auteur
Juan Manuel Praena-Fernandez
  • Fonction : Auteur
Blas David Delgado León
  • Fonction : Auteur

Résumé

Background: A clinical decision support system (CDSS) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients. Materials and methods: Prospective multicenter data from 543 consecutive (2013–2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared. Results: Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001). Conclusions: The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.

Domaines

Cancer
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Dates et versions

hal-03518930 , version 1 (10-01-2022)

Identifiants

Citer

Beatriz Pontes, Francisco Núñez, Cristina Rubio, Alberto Moreno, Isabel Nepomuceno, et al.. A data mining based clinical decision support system for survival in lung cancer. Reports of Practical Oncology & Radiotherapy, 2021, 26 (6), pp.839 - 848. ⟨10.5603/rpor.a2021.0088⟩. ⟨hal-03518930⟩
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