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Title: WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data

Abstract

Purpose: To evaluate the ability of a multiscale radiobiological model of tumor response to predict mid-treatment hypoxia images, based on pretreatment imaging of perfusion and hypoxia with [18-F]FMISO dynamic PET and glucose metabolism with [18-F]FDG PET. Methods: A mechanistic tumor control probability (TCP) radiobiological model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013) was extended to account for intra-tumor nutrient heterogeneity, dynamic cell migration due to nutrient gradients, and stromal cells. This extended model was tested on 10 head and neck cancer patients treated with chemoradiotherapy, randomly drawn from a larger MSKCC protocol involving baseline and mid-therapy dynamic PET scans. For each voxel, initial fractions of proliferative and hypoxic tumor cells were obtained by finding an approximate solution to a system of linear equations relating cell fractions to voxel-level FDG uptake, perfusion (FMISO K{sub 1}) and hypoxia (FMISO k{sub 3}). The TCP model then predicted their evolution over time up until the mid treatment scan. Finally, the linear model was reapplied to predict each lesion’s median hypoxia level (k{sub 3}[med,sim]) which in turn was compared to the FMISO k{sub 3}[med] measured at mid-therapy. Results: The average k3[med] of the tumors in pre-treatment scans wasmore » 0.0035 min{sup −1}, with an inter-tumor standard deviation of σ[pre]=0.0034 min{sup −1}. The initial simulated k{sub 3}[med,sim] of each tumor agreed with the corresponding measurements within 0.1σ[pre]. In 7 out of 10 lesions, the mid-treatment k{sub 3}[med,sim] prediction agreed with the data within 0.3σ[pre]. The remaining cases corresponded to the most extreme relative changes in k{sub 3}[med]. Conclusion: This work presents a method to personalize the prediction of a TCP model using pre-treatment kinetic imaging data, and validates the modeling of radiotherapy response by predicting changes in median hypoxia values during treatment. Variations from predicted response may be a useful biomarker, which should be further explored. Partially supported by NIH grant #1 R01 CA157770-01A1 and a grant from Varian Corporation.« less

Authors:
; ; ; ; ; ; ; ;  [1]
  1. Memorial Sloan Kettering Cancer Center, New York, NY (United States)
Publication Date:
OSTI Identifier:
22654113
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANOXIA; BIOLOGICAL MARKERS; BIOMEDICAL RADIOGRAPHY; CELL PROLIFERATION; FLUORINE 18; FORECASTING; IMAGES; ION MICROPROBE ANALYSIS; MASS SPECTROSCOPY; NEOPLASMS; POSITRON COMPUTED TOMOGRAPHY; RADIOTHERAPY; SIMULATION; TCP; TUMOR CELLS

Citation Formats

Crispin-Ortuzar, M, Grkovski, M, Beattie, B, Lee, N, Riaz, N, Humm, J, Jeong, J, Fontanella, A, and Deasy, J. WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data. United States: N. p., 2016. Web. doi:10.1118/1.4957752.
Crispin-Ortuzar, M, Grkovski, M, Beattie, B, Lee, N, Riaz, N, Humm, J, Jeong, J, Fontanella, A, & Deasy, J. WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data. United States. doi:10.1118/1.4957752.
Crispin-Ortuzar, M, Grkovski, M, Beattie, B, Lee, N, Riaz, N, Humm, J, Jeong, J, Fontanella, A, and Deasy, J. 2016. "WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data". United States. doi:10.1118/1.4957752.
@article{osti_22654113,
title = {WE-AB-202-11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data},
author = {Crispin-Ortuzar, M and Grkovski, M and Beattie, B and Lee, N and Riaz, N and Humm, J and Jeong, J and Fontanella, A and Deasy, J},
abstractNote = {Purpose: To evaluate the ability of a multiscale radiobiological model of tumor response to predict mid-treatment hypoxia images, based on pretreatment imaging of perfusion and hypoxia with [18-F]FMISO dynamic PET and glucose metabolism with [18-F]FDG PET. Methods: A mechanistic tumor control probability (TCP) radiobiological model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013) was extended to account for intra-tumor nutrient heterogeneity, dynamic cell migration due to nutrient gradients, and stromal cells. This extended model was tested on 10 head and neck cancer patients treated with chemoradiotherapy, randomly drawn from a larger MSKCC protocol involving baseline and mid-therapy dynamic PET scans. For each voxel, initial fractions of proliferative and hypoxic tumor cells were obtained by finding an approximate solution to a system of linear equations relating cell fractions to voxel-level FDG uptake, perfusion (FMISO K{sub 1}) and hypoxia (FMISO k{sub 3}). The TCP model then predicted their evolution over time up until the mid treatment scan. Finally, the linear model was reapplied to predict each lesion’s median hypoxia level (k{sub 3}[med,sim]) which in turn was compared to the FMISO k{sub 3}[med] measured at mid-therapy. Results: The average k3[med] of the tumors in pre-treatment scans was 0.0035 min{sup −1}, with an inter-tumor standard deviation of σ[pre]=0.0034 min{sup −1}. The initial simulated k{sub 3}[med,sim] of each tumor agreed with the corresponding measurements within 0.1σ[pre]. In 7 out of 10 lesions, the mid-treatment k{sub 3}[med,sim] prediction agreed with the data within 0.3σ[pre]. The remaining cases corresponded to the most extreme relative changes in k{sub 3}[med]. Conclusion: This work presents a method to personalize the prediction of a TCP model using pre-treatment kinetic imaging data, and validates the modeling of radiotherapy response by predicting changes in median hypoxia values during treatment. Variations from predicted response may be a useful biomarker, which should be further explored. Partially supported by NIH grant #1 R01 CA157770-01A1 and a grant from Varian Corporation.},
doi = {10.1118/1.4957752},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: Combination of serial tumor imaging with radiobiological modeling can provide more accurate information on the nature of treatment response and what underlies resistance. The purpose of this article is to improve the algorithms related to imaging-based radiobilogical modeling of tumor response. Methods: Serial imaging of tumor response to radiation therapy represents a sum of tumor cell sensitivity, tumor growth rates, and the rate of cell loss which are not separated explicitly. Accurate treatment response assessment would require separation of these radiobiological determinants of treatment response because they define tumor control probability. We show that the problem of reconstruction ofmore » radiobiological parameters from serial imaging data can be considered as inverse ill-posed problem described by the Fredholm integral equation of the first kind because it is governed by a sum of several exponential processes. Therefore, the parameter reconstruction can be solved using regularization methods. Results: To study the reconstruction problem, we used a set of serial CT imaging data for the head and neck cancer and a two-level cell population model of tumor response which separates the entire tumor cell population in two subpopulations of viable and lethally damage cells. The reconstruction was done using a least squared objective function and a simulated annealing algorithm. Using in vitro data for radiobiological parameters as reference data, we shown that the reconstructed values of cell surviving fractions and potential doubling time exhibit non-physical fluctuations if no stabilization algorithms are applied. The variational regularization allowed us to obtain statistical distribution for cell surviving fractions and cell number doubling times comparable to in vitro data. Conclusion: Our results indicate that using variational regularization can increase the number of free parameters in the model and open the way to development of more advanced algorithms which take into account tumor heterogeneity, for example, related to hypoxia.« less
  • Purpose: The ability to obtain soft-tissue imaging in the treatment room, such as with megavoltage CT imaging, enables the observation of tumor regression during a course of external beam radiation therapy. In this current study, we report on the most extensive study looking at the rate of regression of non-small-cell lung cancers during a course of external beam radiotherapy by analyzing serial megavoltage CT images obtained on 10 patients. Methods and Materials: The analysis is performed on 10 patients treated with the Helical Tomotherapy Hi*Art device. All 10 patients had non-small-cell lung cancer. A total of 274 megavoltage CT setsmore » were obtained on the 10 patients (average, 27 scans per patient; range, 9-35). All patients had at least a scan at beginning and at the end of treatment. The frequency of scanning was determined by the treating physician. The treatment was subsequently delivered with the Tomotherapy Hi*Art system. The gross tumor volumes (GTVs) were later contoured on each megavoltage CT scan, and tumor volumes were calculated. Although some patients were treated to draining nodal areas in addition to the primary tumor, only the primary GTVs were tracked. Response to treatment was quantified by the relative decrease in tumor volume over time, i.e., elapsed days from the first day of therapy. The individual GTVs ranged from 5.9 to 737.2 cc in volume at the start of treatment. In 6 of the 10 patients, dose recalculations were also performed to document potential variations in delivered doses within the tumors. The megavoltage CT scans were used, and the planned treatment was recalculated on the daily images. The hypothesis was that dose deposited in the target would increase throughout the course of radiotherapy because of tumor shrinkage and subsequent decreasing attenuation. Specifically, the dose received by 95% of the GTV (D{sub 95}) was monitored over time for each of the 6 patients treated at M.D. Anderson Cancer Center Orlando. Results: Regression of all 10 lung tumors could be observed on the serial megavoltage CT scans. The decrease in volume was observed at a relatively constant rate throughout the treatments, with no obvious initial or final plateaus. For all 10 tumors, the average decrease in volume was 1.2% per day. However, individual tumor regression rates were observed with a range of 0.6% to 2.3% per day. The lowest rate of shrinkage was observed for the smallest lesion, and the highest rate was observed in the largest lesion. Of the 6 cases in which dose recalculations were performed, 5 demonstrated a small but noticeable gradual increase in deposited doses within the tumor, with the D{sub 95} increases ranging from 0.02% to 0.1% per day. Conclusion: With the advent of in-room soft-tissue imaging techniques such as megavoltage CT imaging with a helical tomotherapy unit, daily documentation of the status of a grossly visible targeted tumor becomes possible. The current study demonstrated that tumor regression can be documented for patients with non-small-cell lung cancer treated with helical tomotherapy. Clinical correlations between the observations made during the course of treatment and ultimate outcomes, e.g. local control, should be investigated.« less
  • Objective: To determine whether changes in tumor volume occur during the course of conformal 3D radiotherapy of high-grade gliomas by use of magnetic resonance imaging (MRI) during treatment and whether these changes had an impact on tumor coverage. Methods and Materials: Between December 2000 and January 2004, 21 patients with WHO Grades 3 to 4 supratentorial malignant gliomas treated with 3D conformal radiotherapy (median dose, 70 Gy) were enrolled in a prospective clinical study. All patients underwent T1-weighted contrast-enhancing and T2-weighted and fluid-attenuated inversion recovery (FLAIR) imaging at approximately 1 to 2 weeks before radiotherapy, during radiotherapy (Weeks 1 andmore » 3), and at routine intervals thereafter. All MRI scans were coregistered to the treatment-planning CT. Gross tumor volume (GTV Pre-Rx) was defined from a postoperative T1-weighted contrast-enhancing MRI performed 1 to 2 weeks before start of radiotherapy. A second GTV (GTV Week 3) was defined by use of an MRI performed during Week 3 of radiotherapy. A uniform 0.5 cm expansion of the respective GTV, PTV (Pre-Rx), and PTV (Week 3) was applied to the final boost plan. Dose-volume histograms (DVH) were used to analyze any potential adverse changes in tumor coverage based on Week 3 MRI. Results: All MRI scans were reviewed independently by a neuroradiologist (DGH). Two patients were noted to have multifocal disease at presentation and were excluded from analysis. In 19 cases, changes in the GTV based on MRI at Week 3 during radiotherapy were as follows: 2 cases had an objective decrease in GTV ({>=}50%); 12 cases revealed a slight decrease in the rim enhancement or changes in cystic appearance of the GTV; 2 cases showed no change in GTV; and 3 cases demonstrated an increase in tumor volume. Both cases with objective decreases in GTV during treatment were Grade 3 tumors. No cases of tumor progression were noted in Grade 3 tumors during treatment. In comparison, three of 12 Grade 4 tumors had tumor progression, based on MRI obtained during Week 3 of radiotherapy. Median increase in GTV (Week 3) was 11.7 cc (range, 9.8-21.3). Retrospective DVH analysis of PTV (Pre-Rx) and PTV (Week 3) demonstrated a decrease in V{sub 95%}(PTV volume receiving 95% of the prescribed dose) in those 3 cases. Conclusions: Routine MR imaging during radiotherapy may be essential in ensuring tumor coverage if highly conformal radiotherapy techniques such as stereotactic boost and intensity-modulated radiotherapy are used in dose-escalation trials that utilize smaller treatment margins.« less
  • Purpose: To measure regression of cancer of the uterine cervix during external beam radiotherapy using magnetic resonance imaging, derive radiobiologic parameters from a mathematical model of tumor regression, and compare these parameters with the pretreatment measurements of tumor hypoxia. Methods and Materials: A total of 27 eligible patients undergoing external beam radiotherapy for cervical cancer underwent weekly magnetic resonance imaging scans. The tumor volume was assessed on each of these scans and the rate of regression plotted. A radiobiologic model was formulated to simulate the effect on tumor regression of the surviving proportion of cells after 2 Gy (SP{sub 2}),more » the cell clearance constant (clearance of irreparably damaged cells from the tumor [T{sub c}]), and accelerated repopulation. Nonlinear regression analysis was used to fit the radiobiologic model to the magnetic resonance imaging-derived tumor volumes and to derive the estimates of SP{sub 2} and T{sub c} for each patient. These were compared to the pretreatment hypoxia measurements. Results: The initial tumor volume was 8-209 cm{sup 3}. The relative reduction in volume during treatment was 0.02-0.79. The simulations using representative values of the independent biologic variables derived from published data showed SP{sub 2} and T{sub c} to strongly influence the shape of the volume-response curves. Nonlinear regression analysis yielded a median SP{sub 2} of 0.71 and median T{sub c} of 10 days. Tumors with a high SP{sub 2} >0.71 were significantly more hypoxic at diagnosis (p = 0.02). Conclusion: The results of our study have shown that cervical cancer regresses during external beam radiotherapy, although marked variability is present among patients and is influenced by underlying biologic processes, including cellular sensitivity to radiotherapy and proliferation. Better understanding of the biologic mechanisms might facilitate novel adaptive treatment strategies in future studies.« less
  • Purpose: Tumor treatment response may potentially be assessed during radiation therapy (RT) by analyzing changes in CT-textures. We investigated the different early RT-responses between small cell (SCLC) and non-small cell lung cancer (NSCLC) as assessed by CT-texture. Methods: Daily diagnostic-quality CT acquired during routine CT-guided RT using a CT-on-Rails for 13-NSCLC and 5-SCLC patients were analyzed. These patient had ages ranging from 45–78 and 38–63 years, respectively, for NSCLC and SCLC groups, and tumor-stages ranging from T2-T4, and were treated with either RT or chemotherapy and RT with 45–66Gy/ 20–34 fractions. Gross-tumor volume (GTV) contour was generated on each dailymore » CT by populating GTV contour from simulation to daily CTs with manual editing if necessary. CT-texture parameters, such as Hounsfield Unit (HU) histogram, mean HU, skewness, kurtosis, entropy, and short-run high-gray level emphasis (SRHGLE), were calculated in GTV from each daily CT-set using an in house software tool. Difference in changes of these texture parameters during RT between NSCLC and SCLC was analyzed and compared with GTV volume changes. Results: Radiation-induced changes in CT-texture were different between SCLC and NSCLC. Average changes from first to the last fractions for NSCLC and SCLC in GTV were 28±10(12–44) and 30±15(11–47) HU (mean HU reduction), 12.7% and 18.3% (entropy), 50% and 55% (SRHGLE), 19% and 22% (kurtosis), and 5.2% and 22% (skewness), respectively. Good correlation in kurtosis changes and GTV was seen (R{sup 2}=0.8923) for SCLC, but not for NSCLC (R{sup 2}=0.4748). SCLC had better correlations between GTV volume reduction and entropy (SCLC R{sup 2}=0.847; NSCLC R{sup 2}=0.6485), skewness (SCLC R{sup 2}=0.935; NSCLC R{sup 2}=0.7666), or SRHGLE (SCLC R{sup 2}=0.9619; NSCLC R{sup 2}=0.787). Conclusion: NSCLC and SCLC exhibited different early RT-responses as assessed by CT-texture changes during RT-delivery. The observed larger changes in various CT-texture parameters for SCLC indicate that SCLC may respond to RT more rapid than NSCLC.« less