TY - JOUR
T1 - Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer
AU - Vaidya, Manushka
AU - Creach, Kimberly M.
AU - Frye, Jennifer
AU - Dehdashti, Farrokh
AU - Bradley, Jeffrey D.
AU - El Naqa, Issam
PY - 2012/2
Y1 - 2012/2
N2 - Background and Purpose: Prediction of local failure in radiotherapy patients with non-small cell lung cancer (NSCLC) remains a challenging task. Recent evidence suggests that FDG-PET images can be used to predict outcomes. We investigate an alternative multimodality image-feature approach for predicting post-radiotherapy tumor progression in NSCLC. Material and methods: We analyzed pre-treatment FDG-PET/CT studies of twenty-seven NSCLC patients for local and loco-regional failures. Thirty-two tumor region features based on SUV or HU, intensity-volume-histogram (IVH) and texture characteristics were extracted. Statistical analysis was performed using Spearman's correlation (rs) and multivariable logistic regression. Results: For loco-regional recurrence, IVH variables had the highest univariate association. In PET, IVH-slope reached rs = 0.3426 (p = 0.0403). Motion correction slightly improved correlation of texture features. In CT, coefficient of variation had the highest association rs = -0.2665 (p = 0.0871). Similarly for local failure, a CT-IVH parameter reached rs = 0.4530 (p = 0.0105). For loco-regional and local failures, a 2-parameter model of PET-V 80 and CT-V 70 yielded rs = 0.4854 (p = 0.0067) and rs = 0.5908 (p = 0.0013), respectively. Addition of dosimetric variables provided improvement in cases of loco-regional but not local failures. Conclusions: We proposed a feature-based approach to evaluate radiation tumor response. Our study demonstrates that multimodality image-feature modeling provides better performance compared to existing metrics and holds promise for individualizing radiotherapy planning.
AB - Background and Purpose: Prediction of local failure in radiotherapy patients with non-small cell lung cancer (NSCLC) remains a challenging task. Recent evidence suggests that FDG-PET images can be used to predict outcomes. We investigate an alternative multimodality image-feature approach for predicting post-radiotherapy tumor progression in NSCLC. Material and methods: We analyzed pre-treatment FDG-PET/CT studies of twenty-seven NSCLC patients for local and loco-regional failures. Thirty-two tumor region features based on SUV or HU, intensity-volume-histogram (IVH) and texture characteristics were extracted. Statistical analysis was performed using Spearman's correlation (rs) and multivariable logistic regression. Results: For loco-regional recurrence, IVH variables had the highest univariate association. In PET, IVH-slope reached rs = 0.3426 (p = 0.0403). Motion correction slightly improved correlation of texture features. In CT, coefficient of variation had the highest association rs = -0.2665 (p = 0.0871). Similarly for local failure, a CT-IVH parameter reached rs = 0.4530 (p = 0.0105). For loco-regional and local failures, a 2-parameter model of PET-V 80 and CT-V 70 yielded rs = 0.4854 (p = 0.0067) and rs = 0.5908 (p = 0.0013), respectively. Addition of dosimetric variables provided improvement in cases of loco-regional but not local failures. Conclusions: We proposed a feature-based approach to evaluate radiation tumor response. Our study demonstrates that multimodality image-feature modeling provides better performance compared to existing metrics and holds promise for individualizing radiotherapy planning.
KW - Lung cancer outcomes
KW - Multimodality analysis
KW - PET/CT imaging
KW - Pattern recognition
KW - Radiotherapy
UR - http://www.scopus.com/inward/record.url?scp=84857636637&partnerID=8YFLogxK
U2 - 10.1016/j.radonc.2011.10.014
DO - 10.1016/j.radonc.2011.10.014
M3 - Article
C2 - 22098794
AN - SCOPUS:84857636637
SN - 0167-8140
VL - 102
SP - 239
EP - 245
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 2
ER -