Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study


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YAKAR M. Ç., ETİZ D., YILMAZ Ş., ÇELİK Ö., AK G., METİNTAŞ M.

Turkish Journal of Oncology, vol.36, no.4, pp.446-458, 2021 (ESCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.5505/tjo.2021.2788
  • Journal Name: Turkish Journal of Oncology
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, CINAHL, EMBASE, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.446-458
  • Keywords: Lung cancer, machine learning, overall survival, progression-free survival, radiotherapy, PROGNOSTIC-FACTORS, LYMPHOCYTE RATIO, TUMOR VOLUME, CLASSIFICATION, NEUTROPHIL, EXPRESSION, COUNT
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

OBJECTIVE This study aimed to predict the overall survival (OS), survival time, and time to progression in cases diagnosed with Stage III lung cancer. METHODS The sample consisted of 585 patients that underwent radiotherapy and chemotherapy with the diagnosis of Stage III lung cancer. OS prediction was undertaken in 324 cases, survival time prediction in 241 that died due to lung cancer, and prediction of time to progression in 223 that showed progression during follow-up. Twenty-seven variables were evaluated, and logistic regression, multilayer perceptron classifier (MLP), extreme gradient boosting, support vector clustering, random forest classifier (RFC), Gaussian Naive Bayes, and light gradient boosting machine algorithms were used to construct prediction models. RESULTS In OS prediction, over a median 21-month follow-up, 255 of 324 cases died and the median OS was 20 (2-101) months. The best predictive algorithms belonged to logistic regression for OS (accuracy rate: 70%, confidence interval [CI]: 0.60-0.82, area under curve [AUC]: 0.76), MLP classifier for 12- and 20-month survival times (67%, CI: 0.54-0.81, AUC: 0.64 and 71%, CI: 0.59-0.84, AUC: 0.61, respectively), and RFC for time to progression (76%, CI: 0.66-0.86, AUC: 0.78). CONCLUSION Considering high treatment costs, potential serious toxicity, the harm of early progression, and low survival in cases of ineffective treatment, machine learning-based predictive systems are promising. Personalizing prognosis and treatment using these algorithms can improve oncological results.