bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐241: Wavelet‐Based Temporal Feature Extraction From DCE‐MRI to Identify Sub‐Volumes of Low Blood Volume in Head‐And‐Neck Cancer
Authors: You D , Aryal M , Samuels S , Eisbruch A , Cao Y 
Published: June 2015

Purpose:A previous study showed that large sub‐volumes of tumor with low blood volume (BV) (poorly perfused) in head‐and‐neck (HN) cancers are significantly associated with local‐regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel‐wise contrast‐enhanced temporal features of dynamic contrastenhanced (DCE) MRI in HN cancers for predicting LRF.Methods:Our model development consists of training and testing stages. The training stage includes preprocessing of individual‐voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, feature selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast‐enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel‐wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub‐volume classification. Voxel‐wise classification accuracy was computed to evaluate performance of the model.Results:Average classification accuracies were 87.2% for training (10‐fold crossvalidation) and 82.5% for testing. The lowest and highest accuracies (patient‐wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub‐volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub‐volume boundaries.Conclusion:This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrastenhanced features of DCE‐MRI in other tumors.We would like to acknowledge NIH for funding support: UO1 CA183848
SU‐E‐J‐252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi‐Automated Segmentation Methods
Authors: Lee M , Woo B , Kim J , Jamshidi N , Kuo M 
Published: June 2015

Purpose:Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi‐automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM).Methods:MRI examinations with T1 post‐gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi‐automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automatically from the segmented tumor. The inter‐observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility.Results:Inter‐observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE‐T1W1.Conclusion:Comparison of two semi‐automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.
SU‐E‐J‐256: Predicting Metastasis‐Free Survival of Rectal Cancer Patients Treated with Neoadjuvant Chemo‐Radiotherapy by Data‐Mining of CT Texture Features of Primary Lesions
Authors: Zhong H , Wang J , Shen L , Hu W , Wan J , Zhou Z , Zhang Z 
Published: June 2015

Purpose:The purpose of this study is to investigate the relationship between computed tomographic (CT) texture features of primary lesions and metastasis‐free survival for rectal cancer patients; and to develop a datamining prediction model using texture features.Methods:A total of 220 rectal cancer patients treated with neoadjuvant chemo‐radiotherapy (CRT) were enrolled in this study. All patients underwent CT scans before CRT. The primary lesions on the CT images were delineated by two experienced oncologists. The CT images were filtered by Laplacian of Gaussian (LoG) filters with different filter values (1.0–2.5: from fine to coarse). Both filtered and unfiltered images were analyzed using Gray‐level Co‐occurrence Matrix (GLCM) texture analysis with different directions (transversal, sagittal, and coronal). Totally, 270 texture features with different species, directions and filter values were extracted. Texture features were examined with Student's t‐test for selecting predictive features. Principal Component Analysis (PCA) was performed upon the selected features to reduce the feature collinearity. Artificial neural network (ANN) and logistic regression were applied to establish metastasis prediction models.Results:Forty‐six of 220 patients developed metastasis with a follow‐up time of more than 2 years. Sixtyseven texture features were significantly different in t‐test (p<0.05) between patients with and without metastasis, and 12 of them were extremely significant (p<0.001). The Area‐under‐the‐curve (AUC) of ANN was 0.72, and the concordance index (CI) of logistic regression was 0.71. The predictability of ANN was slightly better than logistic regression.Conclusion:CT texture features of primary lesions are related to metastasisfree survival of rectal cancer patients. Both ANN and logistic regression based models can be developed for prediction.
SU‐E‐J‐257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets
Authors: Chetvertkov M , Siddiqui F , Chetty I , Kim J , Kumarasiri A , Liu C , Gordon J 
Published: June 2015

Purpose:Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART).Methods:Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction‐to‐fraction changes were superimposed on the DVFs. A patient‐specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment.Results:For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient's body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction‐to‐fraction changes in patient's anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change.Conclusion:The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction‐to‐fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population‐based PCA models that can be used to prospectively identify significant anatomical changes early in treatment.This work is supported in part by a grant from Varian Medical Systems, Palo Alto, CA.
SU‐E‐J‐258: Prediction of Cervical Cancer Treatment Response Using Radiomics Features Based On F18‐FDG Uptake in PET Images
Authors: Altazi B , Fernandez D , Zhang G , Biagioli M , Moros E , Moffitt H. Lee 
Published: June 2015

Purpose:Radiomics have shown potential for predicting treatment outcomes in several body sites. This study investigated the correlation between PET Radiomics features and treatment response of cervical cancer outcomes.Methods:our dataset consisted of a cohort of 79 patients diagnosed with cervical cancer, FIGO stage IB‐IVA, age range 25–86 years, (median age at diagnosis: 50 years) all treated between: 2009–14 with external beam radiation therapy to a dose range between: 45–50.4 Gy (median= 45 Gy), concurrent cisplatin chemotherapy and MRI‐based brachytherapy to a dose of 20–30 Gy (median= 28 Gy). Metabolic Tumor Volume (MTV) in patient's primary site was delineated on pretreatment PET/CT by two board certified Radiation Oncologists. The features extracted from each patient's volume were: 26 Co‐occurrence matrix (COM) Feature, 11 Run‐Length Matrix (RLM), 11 Gray Level Size Zone Matrix (GLSZM) and 33 Intensity‐based features (IBF). The treatment outcome was divided based on the last follow up status into three classes: No Evidence of Disease (NED), Alive with Disease (AWD) and Dead of Disease (DOD). The ability for the radiomics features to differentiate between the 3 treatments outcome categories were assessed by One‐Way ANOVA test with p‐value < 0.05 was to be statistically significant. The results from the analysis were compared with the ones obtained previously for standard Uptake Value (SUV).Results:Based on patients last clinical follow‐up; 52 showed NED, 17 AWD and 10 DOD. Radiomics Features were able to classify the patients based on their treatment response. A parallel analysis was done for SUV measurements for comparison.Conclusion:Radiomics features were able to differentiate between the three different classes of treatment outcomes. However, most of the features were only able to differentiate between NED and DOD class. Also, The ability or radiomics features to differentiate types of response were more significant than SUV.
SU‐E‐J‐273: Skin Temperature Recovery Rate as a Potential Predictor for Radiation‐Induced Skin Reactions
Authors: Biswal N C , Wu Z , Sun J , Chu J 
Published: June 2015

Purpose:To assess the potential of dynamic infrared imaging to evaluate early skin reactions during radiation therapy in cancer patients.Methods:Thermal images were captured by our home‐built system consisting of two flash lamps and an infrared (IR) camera. The surface temperature of the skin was first raised by ∼ 6 °C from ∼1 ms short flashes; the camera then captured a series of IR images for 10 seconds. For each image series, a basal temperature was recorded for 0.5 seconds before flash was triggered. The temperature gradients (ε) were calculated between a reference point (immediately after the flash) and at a time point of 2sec, 4sec and 9sec after that. A 1.0 cm region of interest (ROI) on the skin was drawn; the mean and standard deviations of the ROIs were calculated. The standard ε values for normal human skins were evaluated by imaging 3 healthy subjects with different skin colors. All of them were imaged on 3 separate days for consistency checks.Results:The temperature gradient, which is the temperature recovery rate, depends on the thermal properties of underlying tissue, i.e. thermal conductivity. The average ε for three volunteers averaged over 3 measurements were 0.64±0.1, 0.72±0.2 and 0.80±0.3 at 2sec, 4sec and 9sec respectively. The standard deviations were within 1.5%–3.2%. One of the volunteers had a prior small skin burn on the left wrist and the ε values for the burned site were around 9% (at 4sec) and 13% (at 9sec) lower than that from the nearby normal skin.Conclusion:The temperature gradients from the healthy subjects were reproducible within 1.5%–3.2 % and that from a burned skin showed a significant difference (9%–13%) from the normal skin. We have an IRB approved protocol to image head and neck patients scheduled for radiation therapy.
SU‐E‐J‐275: Review ‐ Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy
Authors: Lu W , Wang J , Zhang H 
Published: June 2015

Purpose:To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy.Methods:We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation.Results:Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi‐automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi‐automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi‐modality segmentation methods have been shown to improve accuracy compared to single‐modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response.Conclusion:This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response.This work was supported in part by the National Cancer Institute Grant R01CA172638.
SU‐E‐T‐08: A Beam Source Model for Monte Carlo Simulations of a Double‐Scattering Proton Beam Delivery System Using Beam Current Modulation
Authors: Shin J , Merchant T E , Lee S , Li Z , Shin D , Farr J B 
Published: June 2015

Purpose:To reconstruct phase‐space information upstream of patient specific collimators for Monte Carlo simulations using only radiotherapy planning system data.Methods:The proton energies are calculated based on residual ranges, e.g., sum of prescribed ranges in a patient and SSD. The Kapchinskij and Vladimirskij (KV) distribution was applied to sample proton's x‐y positions and momentum direction and the beam shape was assumed to be a circle. Free parameters, e.g., the initial energy spread and the emittance of KV distribution were estimated from the benchmarking with commissioning data in a commercial treatment planning system for an operational proton therapy center. The number of histories, which defines the height of individual pristine Bragg peaks (BP) of Spread‐out Bragg peak (SOBP), are weighted based on beam current modulation and a correction factor is applied to take into account the fluence reduction as the residual range decreases due to the rotation of the range modulator wheel. The timedependent behaviors, e.g., the changes of the residual range and histories per a pristine BP, are realized by utilizing TOPAS (Tool for Particle Simulation).Results:Benchmarking simulations for selected SOBPs ranging 7.5 cm to 15.5 cm matched within 2 mm in range and up to 5 mm in SOBP width against measurement data in water phantom. We found this model tends to underestimate entrance dose by about 5 % in comparison to measurement. This was attributed to the situation that the energy distribution used in the model was limited in its granularity at the limit of single energy spectrum for the narrow angle modulator steps used in the proximal pull back region of the SOBPs.Conclusion:Within these limitations the source modeling method proved itself an acceptable alternative of a full treatment head simulation when the machine geometry and materials information are not available.
SU‐E‐T‐10: A Clinical Implementation and the Dosimetric Evidence in High Dose Rate Vaginal Multichannel Applicator Brachytherapy
Authors: Syh J , Syh J , Patel B , Zhang J , Wu H , Rosen L 
Published: June 2015

Purpose:The multichannel cylindrical applicator has a distinctive modification of the traditional single channel cylindrical applicator. The novel multichannel applicator has additional peripheral channels that provide more flexibility both in treatment planning process and outcomes. To protect by reducing doses to adjacent organ at risk (OAR) while maintaining target coverage with inverse plan optimization are the goals for such novel Brachytherapy device. Through a series of comparison and analysis of reults in more than forty patients who received HDR Brachytherapy using multichannel vaginal applicator, this procedure has been implemented in our institution.Methods:Multichannel planning was CT image based. The CTV of 5mm vaginal cuff rind with prescribed length was well reconstructed as well as bladder and rectum. At least D95 of CTV coverage is 95% of prescribed dose. Multichannel inverse plan optimization algorithm not only shapes target dose cloud but set dose avoids to OAR's exclusively. The doses of D2cc, D5cc and D5; volume of V2Gy in OAR's were selected to compare with single channel results when sole central channel is only possibility.Results:Study demonstrates plan superiorly in OAR's doe reduction in multi‐channel plan. The D2cc of the rectum and bladder were showing a little lower for multichannel vs. single channel. The V2Gy of the rectum was 93.72% vs. 83.79% (p=0.007) for single channel vs. multichannel respectively. Absolute reduced mean dose of D5 by multichannel was 17 cGy (s.d.=6.4) and 44 cGy (s.d.=15.2) in bladder and rectum respectively.Conclusion:The optimization solution in multichannel was to maintain D95 CTV coverage while reducing the dose to OAR's. Dosimetric advantage in sparing critical organs by using a multichannel applicator in HDR Brachytherapy treatment of the vaginal cuff is so promising and has been implemented clinically.
SU‐E‐T‐17: A Comparison of Forward and Field in Field Intensity Modulation Radiotherapy Planning for Breast Cancer
Authors: Liu T , Sun T , Chen J , Zhang G 
Published: June 2015

Purpose:To compare the dosimetric difference in forward intensity modulation radiotherapy(fIMRT) and field in field IMRT (FIF IMRT)planning for breast cancer.Methods:Ten patients received radiotherapy are selected.For each patient,two treatment plans(fIMRT and FIF IMRT) were designed with Varian Eclipse ver11.0 treatment planning system.Evaluate the dose parameters of targets, organs at risk (OAR), monitor units and treatment time, using dose‐volume histogram (DVH).Results:There were no significant difference were found in conformal index (CI), homogeneity index (HI) of PTV,V5,V10,V20,V30,V40,V50 of heart, lung and monitor unit(MU)(P>0.05).The differences were significant in the treatment time(fIMRT=8.3min,FIF IMRT=2.5,p<0.05).Conclusion:FIF IMRT is equal to fIMRT in dosimetril evaluation. Due to much less delivery time,FIF IMRT is an efficient technique in treating patients by reduceing the uncomfortable influnce which could effect the treatment.
SU‐E‐T‐20: A Correlation Study of 2D and 3D Gamma Passing Rates for Prostate IMRT Plans
Authors: Zhang D , Wang B , Deng X , Ma C 
Published: June 2015

Purpose:To investigate the correlation between the two‐dimensional gamma passing rate (2D %GP) and three‐dimensional gamma passing rate (3D %GP) in prostate IMRT quality assurance.Methods:Eleven prostate IMRT plans were randomly selected from the clinical database and were used to obtain dose distributions in the phantom and patient. Three types of delivery errors (MLC bank sag errors, central MLC errors and monitor unit errors) were intentionally introduced to modify the clinical plans through an in‐house Matlab program. This resulted in 187 modified plans. The 2D %GP and 3D %GP were analyzed using different dose‐difference and distance‐toagreement (1%‐1mm, 2%‐2mm and 3%‐3mm) and 20% dose threshold. The 2D %GP and 3D %GP were then compared not only for the whole region, but also for the PTVs and critical structures using the statistical Pearson's correlation coefficient (γ).Results:For different delivery errors, the average comparison of 2D %GP and 3D %GP showed different conclusions. The statistical correlation coefficients between 2D %GP and 3D %GP for the whole dose distribution showed that except for 3%/3mm criterion, 2D %GP and 3D %GP of 1%/1mm criterion and 2%/2mm criterion had strong correlations (Pearson's γ value >0.8). Compared with the whole region, the correlations of 2D %GP and 3D %GP for PTV were better (the γ value for 1%/1mm, 2%/2mm and 3%/3mm criterion was 0.959, 0.931 and 0.855, respectively). However for the rectum, there was no correlation between 2D %GP and 3D %GP.Conclusion:For prostate IMRT, the correlation between 2D %GP and 3D %GP for the PTV is better than that for normal structures. The lower dose‐difference and DTA criterion shows less difference between 2D %GP and 3D %GP. Other factors such as the dosimeter characteristics and TPS algorithm bias may also influence the correlation between 2D %GP and 3D %GP.