TY  - JOUR
TI  - Prediction of Survival and Progression-free Survival Using 
Machine Learning in Stage III Lung Cancer: A Pilot Study
AB  - 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.
AU  - Çelik, Özer
AU  - AK, Güntülü
AU  - Metintas, Muzaffer
AU  - Yılmaz, Şenay
AU  - Etiz, Durmuş
AU  - Yakar, Melek
DO  - 10.5505/tjo.2021.2788
PY  - 2021
JO  - Türk Onkoloji Dergisi
VL  - 36
IS  - 4
SN  - 1300-7467
SP  - 446
EP  - 458
DB  - TRDizin
UR  - http://search/yayin/detay/518897
ER  -