A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy

Arthur Jochems*, Issam El-Naqa, Marc Kessler, Charles S. Mayo, Shruti Jolly, Martha Matuszak, Corinne Faivre-Finn, Gareth Price, Lois Holloway, Shalini Vinod, Matthew Field, Mohamed Samir Barakat, David Thwaites, Dirk de Ruysscher, Andre Dekker, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Web of Science)

Abstract

Background: Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. Material and methods: Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N=1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N=698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4months of receiving the first radiotherapy fraction was used as the outcome. Results: Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. Conclusions: Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.
Original languageEnglish
Pages (from-to)226-230
Number of pages5
JournalActa Oncologica
Volume57
Issue number2
DOIs
Publication statusPublished - Feb 2018

Keywords

  • EXTERNAL VALIDATION
  • PROGNOSTIC-FACTOR
  • SURVIVAL
  • STAGE
  • RADIOTHERAPY
  • MORTALITY
  • CARCINOMA
  • ONCOLOGY
  • OUTCOMES
  • SURGERY
  • Prognosis
  • Area Under Curve
  • Humans
  • Treatment Outcome
  • Models, Statistical
  • Machine Learning
  • Chemoradiotherapy/methods
  • Lung Neoplasms/mortality
  • ROC Curve
  • Carcinoma, Non-Small-Cell Lung/mortality

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