Normal Tissue Complication Probability Modeling for Chest Wall Pain in Stereotactic Body Radiation Therapy: An Analysis of Compiled Clinical Data

Volume 90  Number 1S  Supplement 2014

3635 CT Number Changes Observed During CT-Guided Radiation Therapy for Head and Neck Cancer: A New Indicator for Treatment Response F. Mei,1 C. Yang,2 S. Xu,3 I.C. Moraru,4 C. Schultz,5 and A. Li6; 1Si Chuan Cancer Hospital, Chengdu, China, 2Radiation Oncology of Froedert Hospital, Wauwatosa, WI, 3PLA General Hospital, 100853, China, 4 Medical College of Wisconsin, Wauwatosa, WI, 5Radiation Oncology of Froedert Hospital, Wauwatosa, WI, 6Medical College of Wisconsin, Wauwatosa, WI Purpose/Objective(s): We investigate CT number (CTN) changes in gross tumor volume (GTV) and other structures based on daily CTs acquired during CT-guide radiation therapy (RT) for head and neck cancer. The relationship between the CTN change, organ volume regression, and radiation dose is studied. Materials/Methods: Diagnostic-quality CTs acquired using a CT-on-Rails during daily CT-guided IMRT for selected 11 patients with stage III and IVa squamous cell carcinoma of the oropharynx were analyzed. All patients were treated with 70 Gy in 35 fractions concurrently with chemotherapy. The GTV, parotid glands, spinal cord and non-specified tissue (NST, lower dose region, a part of trapezius below sternum) were manually delineated by a radiation oncologist on the daily CTs. The changes of the CTN (HU, Hounsfield Units) histogram and its mean and mode in each of the structures were analyzed in relationship to the volume changes of the structures and the delivered radiation dose (the delivered fraction number) during the course of RT. Pearson analysis was used to assess the correlation between CTN and volume changes in GTV and parotid glands. Results: All selected patients exhibited volume reductions in GTVs, ipsiand contra-lateral parotid gland, with mean reduction ratios of 0.84  0.13, 0.42  0.16 and 0.43  0.14 respectively, between the first and last fractions. Changes in CTN histograms and their mean and mode values were observed in GTVs and parotid glands for all patients over the course of RT. The mean CTN in GTV, and ipsi- and contra-lateral parotid glands were reduced by 910, 108 and 1211 HU, respectively, between the first and last fractions. These reductions were substantial in 3 out of 11 patients (patients 7, 10 and 11) in GTVs, with mean CTN reduced by 1040 HU between the first and last fractions. The substantial mean CTN reductions (10-40 HU) were found in the ipsilateral parotids on patients 2, 5, 7, 8 and 10 and in the contralateral parotids on patients 2, 4, 7, 8 and 10. The CTN changes in both spinal cord and NST were almost invisible (< 3 HU) during the course of RT. For the three patients with substantial mean CTN reductions in GTV, the mean CTN had negative correlations with the increased treatment fractions (r Z -0.979, p < 0.001; r Z -0.843, p Z 0.020; r Z 0.949, p < 0.001 for the three patients respectively). For all patients, there was a positive correlation between the relative volume changes and mean CTN reductions for GTV (r Z 0.59, p Z 0.05). Conclusions: The mean CTN was reduced as the volume decreases in GTV and both parotid glands during the course of RT for head and neck cancer. This CTN reduction was radiation induced and patient specific, indicating that it may be used as an early indicator to evaluate radiation response. Author Disclosure: F. Mei: None. C. Yang: None. S. Xu: None. I.C. Moraru: None. C. Schultz: None. A. Li: None.

3636 Detecting Prostate Focal Lesions in CT Images K. Sheng,1 S. Gou,2 P. Kupelian,3 M. Kamrava,4 D. Low,1 and M.L. Steinberg4; 1University of California Los Angeles, Los Angeles, CA, 2UCLA, Los Angeles, CA, 3UCLA, Orlando, FL, 4UCLA Radiation Oncology, Los Angeles, CA Purpose/Objective(s): The ability to image prostate focal lesions has important implications for radiation therapy aiming at focal lesion boost and potentially quantitative outcome assessment. Unlike MRI, CT has been traditionally considered incapable of providing the functionality for the prostate focal lesion identification. We hypothesize that due to the higher cellularity in the focal lesion that contributes to the MRI apparent

Poster Viewing Abstracts S853 diffusion coefficient (ADC) contrast, there is a slight increase in the CT number in the focal lesion due to decreased water content and increased cell density and subsequently increased inorganic ions that leads to slightly increased photoelectric absorption and increased CT number. However, the contrast is typically lower than the noise floor in CT images and essentially inconspicuous. We have employed an advanced denoising method based on 3D sparse transform and non-local means to eliminate the noise while maintaining the subtle contrast of the focal lesions. Materials/Methods: Five prostate cancer patients with MRI ADC and dynamic contrast enhanced (DCE) images confirmed focal lesions were included. A block matching 3D (BM3D) algorithm was adapted to reduce the noise of kVCT images used for treatment planning. BM3D is an imaging denoising algorithm developed from non-local means methods. Different from local imaging denoising methods, non-local means methods globally search for imaging patches in similar intensity space and use them as a group for noise reduction. BM3D additionally creates 3D groups by stacking 2D patches by the order of similarity. 3D denoising operation is then performed. The resultant 3D group is inverse transformed back to 2D images. Hard noise thresholds were determined by a sensitivity study within the standard deviation range between 1 and 10. Results: Denoised kVCT images using a hard threshold of 4 showed focal regions at 5, 8,11-1, 2, and 8-10 o’clock for the 5 patients, this is highly consistent to the radiologist confirmed focal lesions based on MRI at 5, 7, 11-1, 2 and 8-10 o’clock in the axial plane. These CT focal regions matched well with the MRI focal lesions in the cranio-caudal position. The average increase in density compared to background prostate glands was 0.86%, which corresponds to w50% increase in cellularity and is lower than the average CT noise level of 2.4%. One patient showed an additional focal region in the CT that was not observed in the MRI. Overall the detecting sensitivity and specificity were 100% and 83%, respectively, using MRI as the ground truth. Conclusions: It has been preliminarily demonstrated that the higher tumor cellularity can be detected using kVCT. The low contrast-to-noise information requires advanced denoising to reveal. The finding is significant to radiation therapy by providing an additional tool to locate prostate focal lesions while avoiding the challenge of registration between CT and MRI. Author Disclosure: K. Sheng: None. S. Gou: None. P. Kupelian: None. M. Kamrava: None. D. Low: None. M.L. Steinberg: None.

3637 Normal Tissue Complication Probability Modeling for Chest Wall Pain in Stereotactic Body Radiation Therapy: An Analysis of Compiled Clinical Data P. Prior, J. Wilson, and X. Li; Medical College of Wisconsin, Milwaukee, WI Purpose/Objective(s): Chest wall (CW) pain is a concern in stereotactic body radiation therapy (SBRT). Our objective was to model normal tissue complication probability (NTCP) of CW pain during SBRT based on published clinical data from SBRT for lung cancer. Materials/Methods: Reports citing crude estimates of CW pain were considered in our NTCP modeling. The NCTP model considered has the form: NTCP Z 1/(1+exp(-(BED - BEDdistance $x - TD50)/k)), where BED is the biological effective dose calculated from the report using the linear quadratic model, BEDdistance is a fitting term accounting the effects of increased toxicity due to increased dose near the surface of the skin, x is the distance below the skin surface in centimeters that the SBRT dose was found to be associated with the toxicity, TD50 is the dose required to achieve 50% complication, & k is a fitting constant related to the slope of the dose response curve at TD50. One model calculates BED using a/b Z 8.5 and the other with 14 Gy. The parameters for each model were estimated using the Chi-squared fitting method. Akaike’s information criteria (AIC) was calculated for the NTCP model calculated using a/b Z 8.5 & 14 Gy. Results: Literature search yielded three clinical reports useful in developing a NTCP model for CW pain. In these studies, a mean total dose of 39.2 Gy (range: 30-50 Gy) with a mean dose per fraction of 10.6 Gy/fx

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International Journal of Radiation Oncology  Biology  Physics

(range: 9.4 -12.5 Gy/fx) was specified at a distance of 1.3 cm (range: 0.52.5 cm) below the skin. The NTCP model parameters for CW pain using a/ b Z 8.5 Gy were: TD50 Z 115.9 Gy, k Z 18.4 Gy-1, & BEDdistance Z 2.74 Gy/cm. The NTCP model parameters for a/b Z 14 Gy were: TD50 Z 92.0 Gy, k Z 14.3 Gy-1, & BEDdistance Z 0.81 Gy/cm. The AIC values for NTCPs models where BED was calculated using a/b Z 8.5 & 14 Gy were -53.67 and -57.74, respectively. Conclusions: Three published reports were found useful in constructing an NTCP model of CW pain. The NTCP with an a /b Z 8.5 Gy was found to have the lowest AIC value and thus most likely to describe the published data. Future studies will focus on understanding the relationship between entrance dose and area on CW pain, as well as the subsequent development of skin injury, since other investigators have shown a correlation between CW pain and skin toxicity for tumors located near the skin surface. Author Disclosure: P. Prior: None. J. Wilson: None. X. Li: None.

underlying radiation-induced injury across multiple prostate cancer cohorts. Materials/Methods: We applied factor analyses in four cohorts of patients previously treated with RT for localized prostate cancer (N Z 1025). Cohort A included three sub-cohorts of patients treated with external-beam RT (postoperatively (P): N Z 191, primary (E): N Z 277 to 70 [email protected]/fx, or primary combined with brachytherapy (EB): N Z 344 to 50 [email protected]/fx + 20 Gy@10Gy/fx). Cohort B included 213 patients treated with primary external-beam RT to 78 [email protected] Gy/fx. We used the study-specific questionnaires, which included 34 and 27 questions related to GI symptoms in Cohort A and B, respectively. Exploratory factor analysis followed by confirmatory factor analysis was performed in each cohort. The comparative fit index (CFI, range: 0.0-1.0) was calculated with the highest values determining the number of latent factors (range: 2-8 factors) and a threshold for the relative contribution of each question/ symptom (range: 0.0-1.0) to each factor. Results: For all four cohorts, the highest CFI values suggested a threefactor-solution with a threshold for question/symptom contribution at 0.45 (Cohort A: 0.78, 0.90 and 0.93 for P, E and EB, respectively; Cohort B: 0.93). The identified latent factors can be categorized as “defecation urgency” (Cohort A: P; Cohort B), “fecal leakage” (Cohort A+B), “mucous” (Cohort A) and “pain” (Cohort A: E, EB; Cohort B). For Cohort A, a four-factor-solution resulted in comparable CFI values (0.75, 0.90 and 0.93) and for this solution we found a factor relating to defecation urgency for E and EB, and a factor relating to pain for P (Table). Conclusions: We have identified underlying latent factors related to GI symptoms with similar factor formations across the four investigated cohorts. This opens up for the possibility of pooling of data between cohorts with different scoring systems. On-going predictive modelling work aims to investigate the relevance of the identified factors in terms of radiationinduced injuries. Author Disclosure: M. Thor: None. C. Olsson: None. J. Oh: None. S. Petersen: None. D. Alsadius: None. M. Høyer: None. N. Pettersson: None. L. Bentzen: None. J. Deasy: None. L. Muren: None. G. Steineck: None.

3638 Identifying Groups of Patient-Reported Gastro-Intestinal Symptoms Using Factor Analysis M. Thor,1 C. Olsson,2 J. Oh,3 S. Petersen,4 D. Alsadius,2 M. Høyer,4 N. Pettersson,2 L. Bentzen,4 J. Deasy,3 L. Muren,4 and G. Steineck2; 1 Memorial Sloan-Kettering Cancer Centre, New York City, NY, 2Institute of Clinical Sciences, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden, 3Memorial Sloan-Kettering Cancer Center, New York City, NY, 4Aarhus University Hospital, Aarhus, Denmark Purpose/Objective(s): Beyond rectal bleeding, the dose-response relationship of gastro-intestinal (GI) symptoms, following pelvic radiation therapy (RT), is relatively uncertain. Patient-reported outcomes, presenting extensive descriptions of symptoms, may be useful for this purpose but straightforward translations between scoring systems can be challenging. The purpose of this study was to investigate if factor analysis can be useful in identifying latent factors to group GI symptoms relating to the same

Scientific Abstract 3638; Table factor solutions

Latent factor “Defecation urgency”

Symptom

Prompt to toilet Time stool hold Diarrhea Leakage asleep Entire release Re-defecation “Fecal leakage” @phys. activity Leakage awake Leakage loose Pads usage Leakage ’normal’ Leakage blood Leakage asleep Fecal odor @cough/sneeze Re-defecation Entire release Partial emptying “Mucous” Noticed in stools Leakage awake Instead of stools Leakage asleep Instead of wind “Pain“ Abdominal pain Dull pain Bowel distension Pain@defecation Pain rectal region Strain@defecation Diff@defecation ‘False alarm’ Pain@urgency

The 3 (Cohort B) and 4 (Cohort A) latent

3639

Relative symptom contribution Cohort A (P, E, EB)

Relative symptom contribution Cohort B

0.46, 0.78, 0.85 -, 0.72, 0.83 -, 0.47, 0.47 0.64, -, 0.55, -, -, -, 0.89, 0.62, 0.76 0.63, 0.87, 0.88 -, 0.59, 0.67 0.53, 0.67, -, -, 0.48 0.72, -, -, -, -, 0.57, -, 0.54, -, -, -, 0.48, -, -, 0.94, 0.62, 0.95 0.73, 0.87, 0.54 0.61, 0.49, 0.64 0.50, 0.59, 0.47, -, 0.85, 0.71, 0.89 0.64, 0.74, 0.75 0.52, 0.58, -, -, -, -, -, -, -, -, -, -, 0.48, -, -

0.85 0.64 0.46 0.54 0.57 0.51 0.46 0.59 0.66 0.58 0.56 0.56 0.49 -

A Free Multi-Model Program for Comparing Linear-Quadratic and Non-Linear Quadratic Models in TCP Prediction of SABR-Treated NSCLC J. Kang,1 Y. Zhang,2 D.A. Clump,2 J.C. Flickinger,2 X. Li,2 and M.S. Huq2; 1University of Pittsburgh School of Medicine, Pittsburgh, PA, 2University of Pittsburgh Medical Center, Pittsburgh, PA Purpose/Objective(s): Stereotactic ablative body radiation therapy (SABR) is effective in treating early-stage non-small cell lung cancer (NSCLC). Dose selection models for predicting tumor control are not fully applicable in large part due to the accuracy of the linear-quadratic (LQ) formula in modeling biologically effective dose (BED). Here, we present a novel, easily-modified pipeline built in MATLAB that predicts tumor control probability (TCP) for various LQ and non-LQ models and test our system in relation to the known outcomes of a central primary NSCLC SABR dataset. In our system, we can (1) input dose volume histograms (DVH) from multiple patients as an input; (2) retrieve initial parameters from published data; (3) calculate biologically effective doses (BED) and equivalent uniform dose (EUD) for LQ/non-LQ models; and (4) calculate TCPs based on each model. Materials/Methods: We design a MATLAB system that interfaces with DVH files from our treatment planning system (TPS). Using initial parameters from the literature, we calculate BEDs for the linear-quadratic (LQ), linear-quadratic-linear (LQ-L), and universal survival curve (USC) formulations. For the LQ model, we use BED_LQ Z n*d(1+d/(a⁄b)) where a⁄b Z 10, number of fractions n Z 4, and dose-per-fraction d Z 12 Gy. For LQ-L, we used BED_LQL Z n*d_T (1+d_T/(a⁄b))+n(d-d_T )(1+ 2d_T/(a⁄b)) where the LQ-L threshold dose d_T Z 11 Gy. For USC, we used BED_USC Z 1/(a*D_0) (D-n*D_q) for the high dose fractions