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Interchangeability of a Radiomic Signature Between Conventional and Weekly Cone Beam Computed Tomography Allowing Response Prediction in Non-Small Cell Lung Cancer
ePoster Sessions S193
Volume 96 Number 2S Supplement 2016
1068 Quantitative Computed Tomography for Tumor Response Assessment During Radiation Therapy for Lung Cancer J. Paul,1 E.M. Gore,2 and A. Li1; 1Medical College of Wisconsin, Milwaukee, WI, 2Medical College of Wisconsin and Clement J. Zablocki VA Medical Center Department of Radiation Oncology, Milwaukee, WI Purpose/Objective(s): Ability to assess treatment response in early phase of radiation therapy (RT) delivery is highly desirable for adaptive RT. In this work, by investigating radiation-induced changes in CT Hounsfield Unit (HU) histogram features and their correlations with treatment outcome, we determine the feasibility of using quantitative CT for early tumor response assessment during RT for lung cancer. Materials/Methods: Daily diagnostic quality CTs acquired during CT guided RT delivery using a CT-on-Rails for 20 lung cancer patients were retrospectively analyzed. Patients had an age range of 38-87 years, clinical tumor stages range from T2-T4, and were treated with either chemotherapy and RT (11) or RT alone with radiation dose of 45-61 Gy in 15-34 fractions. Contour of gross tumor volume (GTV) on each daily CT was generated by populating GTV contour from the simulation CT to daily CT with manual editing. A series of HU histogram features for the GTV, including HU histogram, mean HU, entropy, skewness, kurtosis, and correlation, were obtained for these daily CT sets using an in house software tool. Patient follow up data, e.g., local tumor control and survival data were analyzed to establish their correlations with the histogram parameters. Changes of radiomic features from the first to the last RT fractions were categorized into two groups in the analyses of outcome correlations. Results: Radiation induced changes in CT HU histogram features in GTV was noticed during delivery of RT for lung cancer. For the patients studied, average reduction of mean HU in GTV from the first to the last RT fractions was 28 12 (11-47) HU. Reductions of more than 30 HU in GTV were observed for 55% (11/ 20) cases, while in 8/20 cases had reductions less than 30 HU. Patient survival and local control were higher in group with higher mean HU reduction. The survival rates at 31 months were 50% and 33.3% respectively for change in HU >30 and <30 groups. Increase of kurtosis during RT delivery was observed in the 70% cases, and the correlation reduced in 60% cases. The entropy reduced in 60% cases, and the 70% of the cases experienced reduced skewness. The mean reductions (calculated from the first to last daily CTs) in entropy and skewness were 1.6 and 1.5, respectively. Reductions in entropy and skewness were associated with higher patient overall survival rates. The survival rates at 31 months were 50% and 33.3% respectively for decrease and increase in entropy. Conclusion: Radiation induces changes in CT HU histogram features in tumor during RT delivery for lung cancer. Higher reduction in mean HU of GTV observed during RT delivery correlates with better patient overall survival rate and local tumor control. If this correlation is confirmed with data from more patients, quantitative CT would be an effective biomarker for early tumor response assessment of RT for lung cancer. Author Disclosure: J. Paul: None. E.M. Gore: None. A. Li: None.
1069 Interchangeability of a Radiomic Signature Between Conventional and Weekly Cone Beam Computed Tomography Allowing Response Prediction in Non-Small Cell Lung Cancer J.E. van Timmeren, R.T.H. Leijenaar, W. van Elmpt, and P. Lambin; Department of Radiation Oncology (MAASTRO), GROW e School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, the Netherlands Purpose/Objective(s): A previously validated radiomic signature based on treatment planning CT (pCT) scans showed to have prognostic information for lung cancer patients. Kilovolt cone-beam CT (CBCT) imaging is acquired prior to each fraction and it therefore has great potential to monitor response to treatment. In this study we aimed to assess the interchangeability of the radiomic signature between pCT and pre-
radiotherapy CBCT imaging of lung cancer patients and show the potential of CBCT Delta Radiomics (i.e. treatment response monitoring). Materials/Methods: A total of 122 stage II-III NSCLC patients for which daily CBCTs were available were included in this study. All patients received radiation therapy between January 2012 and January 2014. Overall survival was retrieved for all patients. The radiomic signature is a Cox regression hazards model based on four features1, (I) “Energy,” (II) “Grey Level Nonuniformity,” (III) “HLH Grey Level Nonuniformity,” and (IV) “Compactness.” These features were derived from the pCT and the CBCT prior to the first radiotherapy fraction (CBCT-FX1). To assess interchangeability of the signature features between conventional planning CT and CBCT imaging we compared feature values for 20 patients by means of linear regression and excluded these patients from further analysis. As an indication of the model’s discriminate power, Harrell’s concordance index (c-index) was calculated, ranging from 0.5 (no discrimination) to 1 (perfect discrimination). Moreover, the c-index of the radiomic signature with or without “Delta volume” was assessed, which was calculated by extracting tumor volume from CBCT-FX1 and CBCT obtained three weeks after the first treatment fraction. Results: A linear relationship between pCT and CBCT-FX1 was found for all four features of the signature (slope of I: 1.21, II: 0.70, III: 0.65, IV: 1.01, all R2 >0.98, P < 0.0001). This slope was used to correct the feature values extracted from CBCT-FX1 images to be compared with the pCT scenario. Harrell’s c-index was 0.66 (95% CI Z 0.58e0.74, P Z 4.7*10-5) for CBCT-FX1 images. A c-index of 0.68 (95% CI Z 0.61e0.75, P Z 1.9*10-7) was found after adding the Delta volume (week 4 e baseline) in the model. A c-index of 0.63 (95% CI Z 0.55e0.71, P Z 2.2*10-3) was found when using Delta volume as the only variable in the model. Conclusion: The performance of the model on CBCT images shows that similar prognostic information on overall survival can be derived from images of the first fraction of treatment, providing a calibration was performed. Furthermore, these preliminary results show the prognostic value could be improved by including information during treatment (e.g. “Delta volume”): a proof of concept for CBCT Delta radiomics. Author Disclosure: J.E. van Timmeren: None. R. Leijenaar: None. W. van Elmpt: None. P. Lambin: None.
1070 Predicting Distant Failure in Lung Stereotactic Body Radiation Therapy Using Multiobjective Radiomics Model Z. Zhou,1 M.R. Folkert,1 P. Iyengar,1 Y. Zhang,2 K.D. Westover,1 and J. Wang1; 1University of Texas Southwestern Medical Center, Dallas, TX, 2UT Southwestern Medical Center, Dallas, TX Purpose/Objective(s): To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Materials/Methods: Currently, most of radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single objective may not reflect the performance of a predictive model. In this work, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. This new multi-objective radiomics model is used to predict distant failure in lung SBRT using 52 patients. These patients underwent SBRT from 2006 to 2012 and the median follow-up time was about 15 months. 12 patients (23%) failed at distant sites. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12), and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4), and pretreatment medicines (6). The total number of features is 80. The modelling procedure consists of two steps: (1) extracting features from segmented tumors in PET and CT; and (2) selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model. The optimization problem is solved by a nondominated sorting-based multiobjective evolutionary computation algorithm II (NSGA-II).
Volume 96 Number 2S Supplement 2016
1068 Quantitative Computed Tomography for Tumor Response Assessment During Radiation Therapy for Lung Cancer J. Paul,1 E.M. Gore,2 and A. Li1; 1Medical College of Wisconsin, Milwaukee, WI, 2Medical College of Wisconsin and Clement J. Zablocki VA Medical Center Department of Radiation Oncology, Milwaukee, WI Purpose/Objective(s): Ability to assess treatment response in early phase of radiation therapy (RT) delivery is highly desirable for adaptive RT. In this work, by investigating radiation-induced changes in CT Hounsfield Unit (HU) histogram features and their correlations with treatment outcome, we determine the feasibility of using quantitative CT for early tumor response assessment during RT for lung cancer. Materials/Methods: Daily diagnostic quality CTs acquired during CT guided RT delivery using a CT-on-Rails for 20 lung cancer patients were retrospectively analyzed. Patients had an age range of 38-87 years, clinical tumor stages range from T2-T4, and were treated with either chemotherapy and RT (11) or RT alone with radiation dose of 45-61 Gy in 15-34 fractions. Contour of gross tumor volume (GTV) on each daily CT was generated by populating GTV contour from the simulation CT to daily CT with manual editing. A series of HU histogram features for the GTV, including HU histogram, mean HU, entropy, skewness, kurtosis, and correlation, were obtained for these daily CT sets using an in house software tool. Patient follow up data, e.g., local tumor control and survival data were analyzed to establish their correlations with the histogram parameters. Changes of radiomic features from the first to the last RT fractions were categorized into two groups in the analyses of outcome correlations. Results: Radiation induced changes in CT HU histogram features in GTV was noticed during delivery of RT for lung cancer. For the patients studied, average reduction of mean HU in GTV from the first to the last RT fractions was 28 12 (11-47) HU. Reductions of more than 30 HU in GTV were observed for 55% (11/ 20) cases, while in 8/20 cases had reductions less than 30 HU. Patient survival and local control were higher in group with higher mean HU reduction. The survival rates at 31 months were 50% and 33.3% respectively for change in HU >30 and <30 groups. Increase of kurtosis during RT delivery was observed in the 70% cases, and the correlation reduced in 60% cases. The entropy reduced in 60% cases, and the 70% of the cases experienced reduced skewness. The mean reductions (calculated from the first to last daily CTs) in entropy and skewness were 1.6 and 1.5, respectively. Reductions in entropy and skewness were associated with higher patient overall survival rates. The survival rates at 31 months were 50% and 33.3% respectively for decrease and increase in entropy. Conclusion: Radiation induces changes in CT HU histogram features in tumor during RT delivery for lung cancer. Higher reduction in mean HU of GTV observed during RT delivery correlates with better patient overall survival rate and local tumor control. If this correlation is confirmed with data from more patients, quantitative CT would be an effective biomarker for early tumor response assessment of RT for lung cancer. Author Disclosure: J. Paul: None. E.M. Gore: None. A. Li: None.
1069 Interchangeability of a Radiomic Signature Between Conventional and Weekly Cone Beam Computed Tomography Allowing Response Prediction in Non-Small Cell Lung Cancer J.E. van Timmeren, R.T.H. Leijenaar, W. van Elmpt, and P. Lambin; Department of Radiation Oncology (MAASTRO), GROW e School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, the Netherlands Purpose/Objective(s): A previously validated radiomic signature based on treatment planning CT (pCT) scans showed to have prognostic information for lung cancer patients. Kilovolt cone-beam CT (CBCT) imaging is acquired prior to each fraction and it therefore has great potential to monitor response to treatment. In this study we aimed to assess the interchangeability of the radiomic signature between pCT and pre-
radiotherapy CBCT imaging of lung cancer patients and show the potential of CBCT Delta Radiomics (i.e. treatment response monitoring). Materials/Methods: A total of 122 stage II-III NSCLC patients for which daily CBCTs were available were included in this study. All patients received radiation therapy between January 2012 and January 2014. Overall survival was retrieved for all patients. The radiomic signature is a Cox regression hazards model based on four features1, (I) “Energy,” (II) “Grey Level Nonuniformity,” (III) “HLH Grey Level Nonuniformity,” and (IV) “Compactness.” These features were derived from the pCT and the CBCT prior to the first radiotherapy fraction (CBCT-FX1). To assess interchangeability of the signature features between conventional planning CT and CBCT imaging we compared feature values for 20 patients by means of linear regression and excluded these patients from further analysis. As an indication of the model’s discriminate power, Harrell’s concordance index (c-index) was calculated, ranging from 0.5 (no discrimination) to 1 (perfect discrimination). Moreover, the c-index of the radiomic signature with or without “Delta volume” was assessed, which was calculated by extracting tumor volume from CBCT-FX1 and CBCT obtained three weeks after the first treatment fraction. Results: A linear relationship between pCT and CBCT-FX1 was found for all four features of the signature (slope of I: 1.21, II: 0.70, III: 0.65, IV: 1.01, all R2 >0.98, P < 0.0001). This slope was used to correct the feature values extracted from CBCT-FX1 images to be compared with the pCT scenario. Harrell’s c-index was 0.66 (95% CI Z 0.58e0.74, P Z 4.7*10-5) for CBCT-FX1 images. A c-index of 0.68 (95% CI Z 0.61e0.75, P Z 1.9*10-7) was found after adding the Delta volume (week 4 e baseline) in the model. A c-index of 0.63 (95% CI Z 0.55e0.71, P Z 2.2*10-3) was found when using Delta volume as the only variable in the model. Conclusion: The performance of the model on CBCT images shows that similar prognostic information on overall survival can be derived from images of the first fraction of treatment, providing a calibration was performed. Furthermore, these preliminary results show the prognostic value could be improved by including information during treatment (e.g. “Delta volume”): a proof of concept for CBCT Delta radiomics. Author Disclosure: J.E. van Timmeren: None. R. Leijenaar: None. W. van Elmpt: None. P. Lambin: None.
1070 Predicting Distant Failure in Lung Stereotactic Body Radiation Therapy Using Multiobjective Radiomics Model Z. Zhou,1 M.R. Folkert,1 P. Iyengar,1 Y. Zhang,2 K.D. Westover,1 and J. Wang1; 1University of Texas Southwestern Medical Center, Dallas, TX, 2UT Southwestern Medical Center, Dallas, TX Purpose/Objective(s): To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Materials/Methods: Currently, most of radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single objective may not reflect the performance of a predictive model. In this work, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. This new multi-objective radiomics model is used to predict distant failure in lung SBRT using 52 patients. These patients underwent SBRT from 2006 to 2012 and the median follow-up time was about 15 months. 12 patients (23%) failed at distant sites. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12), and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4), and pretreatment medicines (6). The total number of features is 80. The modelling procedure consists of two steps: (1) extracting features from segmented tumors in PET and CT; and (2) selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model. The optimization problem is solved by a nondominated sorting-based multiobjective evolutionary computation algorithm II (NSGA-II).