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Can Density Changes of Surrounding Soft Tissues Cause Post-RT Cardiopulmonary Perfusion Defects?
Proceedings of the 52nd Annual ASTRO Meeting lung. Furthermore, for our patient cohort, incorporating GTV centroid information did not lead to a statistically significant gain in pneumonitis model accuracy, indicating that adding GTV centroid information does not increase the predictive power of our pneumonitis model. Author Disclosure: Y. Vinogradskiy, None; S. Tucker, None; Z. Liao, None; M. Martel, None.
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Patient Specific TCP - A Strong Outcome Predictor for Radiation Therapy of Cervical Cancer
J. Z. Wang, Z. Huang, S. S. Lo, J. C. Grecula, W. T. C. Yuh, N. A. Mayr The Ohio State University, Columbus, OH Purpose/Objective(s): The number of initial tumor clonogens and their radiosensitivity significantly influence the treatment outcome of radiation therapy. The purpose of this study is to evaluate whether tumor control probability (TCP) calculated based on the initial tumor volume and tumor radiosensitivity predicts the ultimate clinical outcome for cervical cancer. Materials/Methods: Sequential Magnetic Resonance Imaging (MRI) scans were used to measure tumor initial volume (V0) and monitor tumor response to ongoing radiation therapy for 110 patients with advanced cervical cancer. The time points of the three MRI scans were before, at 2-2.5 weeks and 4-5 weeks during radiation therapy. Patients had stages IB2-IVB and median follow-up time was 6.2 (range: 0.2 to 9.4) years. A kinetic model incorporating effects of radiation cell killing, tumor repopulation, and resolution of dead/inactivated cells, was developed to analyze the tumor regression data and estimate the in vivo radiosensitivity (S2 - cell surviving fraction after 2 Gy) for each individual patient. Combined with the initial tumor volume V0, the S2 was used to calculate the TCP based on the specific radiation schedule of each patient. Correlation of the TCP data with local tumor control and disease-specific survival was conducted using the Mann-Whitney rank sum test. Survival curves were obtained using Kaplan-Meier survival analysis. Results: Both V0 and S2 correlated to the treatment outcome for local tumor control (p = 0.019; p = 0.057, respectively) and disease-specific survival (p = 0.001; p = 0.19, respectively). However, the patient-specific TCP combining information from both V0 and S2 provided a better outcome prediction with p = 0.001 and 0.005 for local tumor control and disease-specific survival, respectively. The 6-year local tumor control rate was 91% and 64% (p \ 0.001) with TCP \ 0.63 and TCP.0.63; the 6-year disease-specific survival rate was 76% and 51% (p = 0.008), respectively. Conclusions: These results suggest that not only initial tumor clonogen number/volume, but also tumor-cell radiosensitivity influence the treatment outcome. The patient-specific TCP data, based on radiobiological modeling, adds significant information on tumor responsiveness to radiation therapy, thereby providing strong prediction of ultimate clinical outcome. Adaptive therapy may be necessary for patients with initially large tumor volume and low radiosensitivity to improve outcome. Supported in part by NIH R01 CA 71906. Author Disclosure: J.Z. Wang, None; Z. Huang, None; S.S. Lo, None; J.C. Grecula, None; W.T.C. Yuh, None; N.A. Mayr, None.
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Can Density Changes of Surrounding Soft Tissues Cause Post-RT Cardiopulmonary Perfusion Defects?
M. V. Lawrence1, J. Roper2, T. Bateman1, J. Bailey1, D. Fried1, T. Wong2, R. Jaszczak2, S. Das2, S. Zhou2, L. Marks1 1
University of North Carolina, Chapel Hill, NC, 2Duke University, Durham, NC
Purpose/Objective(s): Abnormalities in SPECT perfusion within the lung and heart are often detected following radiation for tumors in/around the thorax (e.g., lung cancer or left-sided breast cancer). The presence of SPECT perfusion defects are determined by comparing pre- and post-RT SPECT images. However, RT may increase the density of the soft tissue surrounding the lung/heart (e.g., chest wall/breast), possibly leading to an ‘‘apparent’’ SPECT perfusion defect due to increased attenuation of emitted photons. We herein quantitatively assess the degree of density changes in soft tissues following radiation in a series of patients on a prospective clinical study. Materials/Methods: Patients receiving thoracic RT were enrolled on a prospective clinical study including pre- and post-RT thoracic CT scans. Using image registration, changes in tissue density within the soft tissues were quantified (as percent change in average CT Hounsfield units, HU). The data were considered in two ways. 1) Dichotomous Analysis: The presence of an increase in HU was scored as yes/no. A prior study of 26 patients with pre- and 6 months post-RT CT scans showed that density values are within 8.1 HU for tissues receiving no dose. Therefore, regions with increases of $8.1 HU were scored as having an increase in tissue density. 2) Continuous Analysis: Changes in HU were considered as a continuous variable. The potential impact of these density changes on SPECT images was estimated using simulation data from a female SPECT thorax phantom with varying tissue densities. Results: Pre- and serial post-RT CT images were quantitatively studied in 23 patients (4 breast cancer, 19 lung cancer). Data was generated from 2 soft tissue regions receiving doses of 20-50 Gy. Dichotomous Analysis: Based upon the threshold of 8.1 HU at 6 months post-RT, 56% (13/23) of patients showed an increase in soft tissue density, 22% (5/23) showed a decrease, and 22% (5/23) showed no change. Continuous Analysis: The average increase in density of the chest-wall was 8.7 HU (range, 47.0 to -34.5, 15.4 std dev). The average change in breast density was a decrease of 1.1 HU (range, 12.8 to -12.7, 11.6 std dev). There was no apparent dose response in the dichotomous or the continuous analysis. The changes in HU represent a \2% average change in tissue density. Simulations using an increase in tissue density from 10 - 50% demonstrate that this small degree of density change is unlikely to yield meaningful changes in either SPECT lung or heart perfusion. Conclusions: RT doses of 20 - 50 Gy can cause up to a 2% increase in soft tissue density 6 months post-RT. These modest increases in soft tissue density are unlikely to be responsible for the perfusion changes seen on post-RT SPECT lung or heart scans. Supported in part by grants from the Lance Armstrong Foundation, and NIH (R01-CA69579). Author Disclosure: M.V. Lawrence, None; J. Roper, None; T. Bateman, None; J. Bailey, None; D. Fried, None; T. Wong, None; R. Jaszczak, None; S. Das, None; S. Zhou, None; L. Marks, None.
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Patient Specific TCP - A Strong Outcome Predictor for Radiation Therapy of Cervical Cancer
J. Z. Wang, Z. Huang, S. S. Lo, J. C. Grecula, W. T. C. Yuh, N. A. Mayr The Ohio State University, Columbus, OH Purpose/Objective(s): The number of initial tumor clonogens and their radiosensitivity significantly influence the treatment outcome of radiation therapy. The purpose of this study is to evaluate whether tumor control probability (TCP) calculated based on the initial tumor volume and tumor radiosensitivity predicts the ultimate clinical outcome for cervical cancer. Materials/Methods: Sequential Magnetic Resonance Imaging (MRI) scans were used to measure tumor initial volume (V0) and monitor tumor response to ongoing radiation therapy for 110 patients with advanced cervical cancer. The time points of the three MRI scans were before, at 2-2.5 weeks and 4-5 weeks during radiation therapy. Patients had stages IB2-IVB and median follow-up time was 6.2 (range: 0.2 to 9.4) years. A kinetic model incorporating effects of radiation cell killing, tumor repopulation, and resolution of dead/inactivated cells, was developed to analyze the tumor regression data and estimate the in vivo radiosensitivity (S2 - cell surviving fraction after 2 Gy) for each individual patient. Combined with the initial tumor volume V0, the S2 was used to calculate the TCP based on the specific radiation schedule of each patient. Correlation of the TCP data with local tumor control and disease-specific survival was conducted using the Mann-Whitney rank sum test. Survival curves were obtained using Kaplan-Meier survival analysis. Results: Both V0 and S2 correlated to the treatment outcome for local tumor control (p = 0.019; p = 0.057, respectively) and disease-specific survival (p = 0.001; p = 0.19, respectively). However, the patient-specific TCP combining information from both V0 and S2 provided a better outcome prediction with p = 0.001 and 0.005 for local tumor control and disease-specific survival, respectively. The 6-year local tumor control rate was 91% and 64% (p \ 0.001) with TCP \ 0.63 and TCP.0.63; the 6-year disease-specific survival rate was 76% and 51% (p = 0.008), respectively. Conclusions: These results suggest that not only initial tumor clonogen number/volume, but also tumor-cell radiosensitivity influence the treatment outcome. The patient-specific TCP data, based on radiobiological modeling, adds significant information on tumor responsiveness to radiation therapy, thereby providing strong prediction of ultimate clinical outcome. Adaptive therapy may be necessary for patients with initially large tumor volume and low radiosensitivity to improve outcome. Supported in part by NIH R01 CA 71906. Author Disclosure: J.Z. Wang, None; Z. Huang, None; S.S. Lo, None; J.C. Grecula, None; W.T.C. Yuh, None; N.A. Mayr, None.
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Can Density Changes of Surrounding Soft Tissues Cause Post-RT Cardiopulmonary Perfusion Defects?
M. V. Lawrence1, J. Roper2, T. Bateman1, J. Bailey1, D. Fried1, T. Wong2, R. Jaszczak2, S. Das2, S. Zhou2, L. Marks1 1
University of North Carolina, Chapel Hill, NC, 2Duke University, Durham, NC
Purpose/Objective(s): Abnormalities in SPECT perfusion within the lung and heart are often detected following radiation for tumors in/around the thorax (e.g., lung cancer or left-sided breast cancer). The presence of SPECT perfusion defects are determined by comparing pre- and post-RT SPECT images. However, RT may increase the density of the soft tissue surrounding the lung/heart (e.g., chest wall/breast), possibly leading to an ‘‘apparent’’ SPECT perfusion defect due to increased attenuation of emitted photons. We herein quantitatively assess the degree of density changes in soft tissues following radiation in a series of patients on a prospective clinical study. Materials/Methods: Patients receiving thoracic RT were enrolled on a prospective clinical study including pre- and post-RT thoracic CT scans. Using image registration, changes in tissue density within the soft tissues were quantified (as percent change in average CT Hounsfield units, HU). The data were considered in two ways. 1) Dichotomous Analysis: The presence of an increase in HU was scored as yes/no. A prior study of 26 patients with pre- and 6 months post-RT CT scans showed that density values are within 8.1 HU for tissues receiving no dose. Therefore, regions with increases of $8.1 HU were scored as having an increase in tissue density. 2) Continuous Analysis: Changes in HU were considered as a continuous variable. The potential impact of these density changes on SPECT images was estimated using simulation data from a female SPECT thorax phantom with varying tissue densities. Results: Pre- and serial post-RT CT images were quantitatively studied in 23 patients (4 breast cancer, 19 lung cancer). Data was generated from 2 soft tissue regions receiving doses of 20-50 Gy. Dichotomous Analysis: Based upon the threshold of 8.1 HU at 6 months post-RT, 56% (13/23) of patients showed an increase in soft tissue density, 22% (5/23) showed a decrease, and 22% (5/23) showed no change. Continuous Analysis: The average increase in density of the chest-wall was 8.7 HU (range, 47.0 to -34.5, 15.4 std dev). The average change in breast density was a decrease of 1.1 HU (range, 12.8 to -12.7, 11.6 std dev). There was no apparent dose response in the dichotomous or the continuous analysis. The changes in HU represent a \2% average change in tissue density. Simulations using an increase in tissue density from 10 - 50% demonstrate that this small degree of density change is unlikely to yield meaningful changes in either SPECT lung or heart perfusion. Conclusions: RT doses of 20 - 50 Gy can cause up to a 2% increase in soft tissue density 6 months post-RT. These modest increases in soft tissue density are unlikely to be responsible for the perfusion changes seen on post-RT SPECT lung or heart scans. Supported in part by grants from the Lance Armstrong Foundation, and NIH (R01-CA69579). Author Disclosure: M.V. Lawrence, None; J. Roper, None; T. Bateman, None; J. Bailey, None; D. Fried, None; T. Wong, None; R. Jaszczak, None; S. Das, None; S. Zhou, None; L. Marks, None.
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