TY - JOUR
T1 - Predicting radiation pneumonitis in locally advanced stage II–III non-small cell lung cancer using machine learning
AU - Luna, José Marcio
AU - Chao, Hann Hsiang
AU - Diffenderfer, Eric S.
AU - Valdes, Gilmer
AU - Chinniah, Chidambaram
AU - Ma, Grace
AU - Cengel, Keith A.
AU - Solberg, Timothy D.
AU - Berman, Abigail T.
AU - Simone, Charles B.
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/4
Y1 - 2019/4
N2 - Background and purpose: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors. Materials and methods: We evaluated 32 clinical features per patient in a cohort of 203 stage II–III LA-NSCLC patients treated with definitive chemoradiation to a median dose of 66.6 Gy in 1.8 Gy daily fractions at our institution from 2008 to 2016. Of this cohort, 17.7% of patients developed grade ≥2 RP. Univariate analysis was performed using trained decision stumps to individually analyze statistically significant predictors of RP and perform feature selection. Applying Random Forest, we performed multivariate analysis to assess the combined performance of important predictors of RP. Results: On univariate analysis, lung V20, lung mean, lung V10 and lung V5 were found to be significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, Random Forest (AUC = 0.66, p = 0.0005) identified esophagus max (20.5%), lung V20 (16.4%), lung mean (15.7%) and pack-year (14.9%) as the most common primary differentiators of RP. Conclusions: We highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.
AB - Background and purpose: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors. Materials and methods: We evaluated 32 clinical features per patient in a cohort of 203 stage II–III LA-NSCLC patients treated with definitive chemoradiation to a median dose of 66.6 Gy in 1.8 Gy daily fractions at our institution from 2008 to 2016. Of this cohort, 17.7% of patients developed grade ≥2 RP. Univariate analysis was performed using trained decision stumps to individually analyze statistically significant predictors of RP and perform feature selection. Applying Random Forest, we performed multivariate analysis to assess the combined performance of important predictors of RP. Results: On univariate analysis, lung V20, lung mean, lung V10 and lung V5 were found to be significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, Random Forest (AUC = 0.66, p = 0.0005) identified esophagus max (20.5%), lung V20 (16.4%), lung mean (15.7%) and pack-year (14.9%) as the most common primary differentiators of RP. Conclusions: We highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.
KW - CART
KW - Logistic regression
KW - Machine learning
KW - Non-small cell lung cancer
KW - RUSBoost
KW - Radiation pneumonitis
KW - Random forest
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85060298953&partnerID=8YFLogxK
U2 - 10.1016/j.radonc.2019.01.003
DO - 10.1016/j.radonc.2019.01.003
M3 - Article
C2 - 30935565
AN - SCOPUS:85060298953
SN - 0167-8140
VL - 133
SP - 106
EP - 112
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
ER -