Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study
Melek YAKAR1,Durmuş ETİZ1,Şenay YILMAZ2,Özer ÇELİK3,Güntülü AK2,Muzaffer METİNTAŞ2
1Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir-Turkey
2Department of Chest Diseases, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir-Turkey
3Department of Mathematics and Computer Science, Eskisehir Osmangazi University Faculty of Arts and Sciences, Eskisehir-Turkey
DOI : 10.5505/tjo.2021.2788 OBJECTIVE
This study aimed to predict the overall survival (OS), survival time, and time to progression in cases diagnosed with Stage III lung cancer.

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.

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).

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. Keywords : Lung cancer; machine learning; overall survival; progression-free survival; radiotherapy