bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐234: Application of a Breathing Motion Model to ViewRay Cine MR Images
Authors: O'Connell D. P. , Thomas D. H. , Dou T. H. , Lamb J. M. , Yang L. , Low D. A.
Published: June 2015

Purpose:

A respiratory motion model previously used to generate breathing‐gated CT images was used with cine MR images. Accuracy and predictive ability of the in‐plane models were evaluated.

Methods:

Sagittalplane cine MR images of a patient undergoing treatment on a ViewRay MRI/radiotherapy system were acquired before and during treatment. Images were acquired at 4 frames/second with 3.5 × 3.5 mm resolution and a slice thickness of 5 mm. The first cine frame was deformably registered to following frames. Superior/inferior component of the tumor centroid position was used as a breathing surrogate. Deformation vectors and surrogate measurements were used to determine motion model parameters. Model error was evaluated and subsequent treatment cines were predicted from breathing surrogate data. A simulated CT cine was created by generating breathing‐gated volumetric images at 0.25 second intervals along the measured breathing trace, selecting a sagittal slice and downsampling to the resolution of the MR cines. A motion model was built using the first half of the simulated cine data. Model accuracy and error in predicting the remaining frames of the cine were evaluated.

Results:

Mean difference between model predicted and deformably registered lung tissue positions for the 28 second preview MR cine acquired before treatment was 0.81 +/− 0.30 mm. The model was used to predict two minutes of the subsequent treatment cine with a mean accuracy of 1.59 +/− 0.63 mm.

Conclusion:

Inplane motion models were built using MR cine images and evaluated for accuracy and ability to predict future respiratory motion from breathing surrogate measurements. Examination of long term predictive ability is ongoing. The technique was applied to simulated CT cines for further validation, and the authors are currently investigating use of in‐plane models to update pre‐existing volumetric motion models used for generation of breathing‐gated CT planning images.
SU‐E‐J‐237: Real‐Time 3D Anatomy Estimation From Undersampled MR Acquisitions
Authors: Glitzner M , de Senneville B Denis , Lagendijk J , Raaymakers B , Crijns S
Published: June 2015

Recent developments made MRI guided radiotherapy feasible. Performing simultaneous imaging during fractions can provide information about changing anatomy by means of deformable image registration for either immediate plan adaptations or accurate dose accumulation on the changing anatomy. In 3D MRI, however, acquisition time is considerable and scales with resolution. Furthermore, intra‐scan motion degrades image quality.In this work, we investigate the sensitivity of registration quality on imageresolution: potentially, by employing spatial undersampling, the acquisition timeof MR images for the purpose of deformable image registration can be reducedsignificantly.On a volunteer, 3D‐MR imaging data was sampled in a navigator‐gated manner, acquiring one axial volume (360×260×100mm3) per 3s during exhale phase. A T1‐weighted FFE sequence was used with an acquired voxel size of (2.5mm3) for a duration of 17min. Deformation vector fields were evaluated for 100 imaging cycles with respect to the initial anatomy using deformable image registration based on optical flow. Subsequently, the imaging data was downsampled by a factor of 2, simulating a fourfold acquisition speed. Displacements of the downsampled volumes were then calculated by the same process.In kidneyliver boundaries and the region around stomach/duodenum, prominent organ drifts could be observed in both the original and the downsampled imaging data. An increasing displacement of approximately 2mm was observed for the kidney, while an area around the stomach showed sudden displacements of 4mm. Comparison of the motile points over time showed high reproducibility between the displacements of high‐resolution and downsampled volumes: over a 17min acquisition, the componentwise RMS error was not more than 0.38mm.Based on the synthetic experiments, 3D nonrigid image registration shows little sensitivity to image resolution and the displacement information is preserved even when halving the resolution. This can be employed to greatly reduce image acquisition times for interventional applications in real‐time.

This work was funded by the SoRTS consortium, which includes the industry partners Elekta, Philips and Technolution
SU‐E‐J‐238: First‐Order Approximation of Time‐Resolved 4DMRI From Cine 2DMRI and Respiratory‐Correlated 4DMRI
Authors: Li G , Wei J , Tyagi N , Hunt M , Deasy J
Published: June 2015

Purpose:

Cine 2DMRI is useful in MR‐guided radiotherapy but it lacks volumetric information. We explore the feasibility of estimating timeresolved (TR) 4DMRI based on cine 2DMRI and respiratory‐correlated (RC) 4DMRI though simulation.

Methods:

We hypothesize that a volumetric image during free breathing can be approximated by interpolation among 3DMRI image sets generated from a RC‐4DMRI. Two patients’ RC‐4DMRI with 4 or 5 phases were used to generate additional 3DMRI by interpolation. For each patient, six libraries were created to have total 5‐to‐35 3DMRI images by 0–6 equi‐spaced tri‐linear interpolation between adjacent and full‐inhalation/full‐exhalation phases. Sagittal cine 2DMRI were generated from reference 3DMRIs created from separate, unique interpolations from the original RC‐4DMRI. To test if accurate 3DMRI could be generated through rigid registration of the cine 2DMRI to the 3DMRI libraries, each sagittal 2DMRI was registered to sagittal cuts in the same location in the 3DMRI within each library to identify the two best matches: one with greater lung volume and one with smaller. A final interpolation between the corresponding 3DMRI was then performed to produce the first‐order‐approximation (FOA) 3DMRI. The quality and performance of the FOA as a function of library size was assessed using both the difference in lung volume and average voxel intensity between the FOA and the reference 3DMRI.

Results:

The discrepancy between the FOA and reference 3DMRI decreases as the library size increases. The 3D lung volume difference decreases from 5–15% to 1–2% as the library size increases from 5 to 35 image sets. The average difference in lung voxel intensity decreases from 7–8 to 5–6 with the lung intensity being 0–135.

Conclusion:

This study indicates that the quality of FOA 3DMRI improves with increasing 3DMRI library size. On‐going investigations will test this approach using actual cine 2DMRI and introduce a higher order approximation for improvements.

This study is in part supported by NIH (U54CA137788 and U54CA132378).
SU‐E‐J‐240: Development of a Novel 4D MRI Sequence for Real‐Time Liver Tumor Tracking During Radiotherapy
Authors: Zhuang L , Ye Y , Burmeister J
Published: June 2015

Purpose:

To develop a Novel 4D MRI Technique that is feasible for realtime liver tumor tracking during radiotherapy.

Methods:

A volunteer underwent an abdominal 2D fast EPI coronal scan on a 3.0T MRI scanner (Siemens Inc., Germany). An optimal set of parameters was determined based on image quality and scan time. A total of 23 slices were scanned to cover the whole liver in the test scan. For each scan position, the 2D images were retrospectively sorted into multiple phases based on breathing signal extracted from the images. Consequently the 2D slices with same phase numbers were stacked to form one 3D image. Multiple phases of 3D images formed the 4D MRI sequence representing one breathing cycle.

Results:

The optimal set of scan parameters were: TR= 57ms, TE= 19ms, FOV read= 320mm and flip angle= 30°, which resulted in a total scan time of 14s for 200 frames (FMs) per slice and image resolution of (2.5mm,2.5mm,5.0mm) in three directions. Ten phases of 3D images were generated, each of which had 23 slices. Based on our test scan, only 100FMs were necessary for the phase sorting process which may lower the scan time to 7s/100FMs/slice. For example, only 5 slices/35s are necessary for a 4D MRI scan to cover liver tumor size ≤ 2cm leading to the possibility of tumor trajectory tracking every 35s during treatment.

Conclusion:

The novel 4D MRI technique we developed can reconstruct a 4D liver MRI sequence representing one breathing cycle (7s/ slice) without an external monitor. This technique can potentially be used for real‐time liver tumor tracking during radiotherapy.
SU‐E‐J‐244: Development and Validation of a Knowledge Based Planning Model for External Beam Radiation Therapy of Locally Advanced Non‐Small Cell Lung Cancer
Authors: Liu Z , Larsen E , Bahamondes S. , Choi M , Li J , Cranford K , Hayes C , Grow A , Zheng Y , Wu X , Pai S , Kennedy A
Published: June 2015

Purpose:

The study aims to develop and validate a knowledge based planning (KBP) model for external beam radiation therapy of locally advanced non‐small cell lung cancer (LA‐NSCLC).

Methods:

RapidPlan™ technology was used to develop a lung KBP model. Plans from 65 patients with LA‐NSCLC were used to train the model. 25 patients were treated with VMAT, and the other patients were treated with IMRT. Organs‐at‐risk (OARs) included right lung, left lung, heart, esophagus, and spinal cord. DVH and geometric distribution DVH were extracted from the treated plans. The model was trained using principal component analysis and step‐wise multiple regression. Box plot and regression plot tools were used to identify geometric outliers and dosimetry outliers and help fine‐tune the model. The validation was performed by (a) comparing predicted DVH boundaries to actual DVHs of 63 patients and (b) using an independent set of treatment planning data.

Results:

63 out of 65 plans were included in the final KBP model with PTV volume ranging from 102.5cc to 1450.2cc. Total treatment dose prescription varied from 50Gy to 70Gy based on institutional guidelines. One patient was excluded due to geometric outlier where 2.18cc of spinal cord was included in PTV. The other patient was excluded due to dosimetric outlier where the dose sparing to spinal cord was heavily enforced in the clinical plan. Target volume, OAR volume, OAR overlap volume percentage to target, and OAR out‐of‐field volume were included in the trained model. Lungs and heart had two principal component scores of GEDVH, whereas spinal cord and esophagus had three in the final model. Predicted DVH band (mean ±1 standard deviation) represented 66.2±3.6% of all DVHs.

Conclusion:

A KBP model was developed and validated for radiotherapy of LA‐NSCLC in a commercial treatment planning system. The clinical implementation may improve the consistency of IMRT/VMAT planning.
SU‐E‐J‐246: CT‐Based Volumetric Features Are Associated with Somatic Mutations in Lung Cancer
Authors: Coroller T , Grossmann P , Hou Y , Lee S , Mak R , Aerts H
Published: June 2015

Purpose:

Subsets of non‐small cell lung cancer (NSCLC) are driven by mutations in key oncogenes, with unique biology including susceptibility to targeted treatment. Additionally, those mutations could lead to phenotypic differences of the primary tumor that can be assess with quantitative imaging. In this study, we investigated whether somatic mutation are associated with, and hence can be predicted by CT tumor volume‐based features of NSCLC patients.

Methods:

We included 117 NSCLC patients with treatment‐planning CT scans in our analysis and clinical genotyping for the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) oncogenes. We extracted four volumetric features describing volume and diameters (x/y axis and 3D) of the primary tumor. Volumetric differences between mutant and wild‐type tumors were assessed using Wilcoxon test. Predictive value of the volumetric features for mutation status was assessed using the area under the curve (AUC).

Results:

Genotype distribution included 14 (12%) EGFR mutant, 35 (30%) KRAS mutant, and 68 (58%) wild‐type tumors. All volumetric features for EGFR mutant were significantly (p‐value <0.05) lower than for KRAS mutant and Wild‐Type. No volumetric features were significantly different between KRAS and Wild‐Type. The median (Q1–Q3) for volume was 10.2(6.1– 29.6), 39.3(14.3–89.7) and 49(10.7–119) for EGFR, KRAS and Wild‐Type respectively. All volumetric features were also significant predictive features for EGFR mutation with median (range) AUC of 0.69(0.67–0.70) and all p‐value<0.05. However, the AUC was only 0.51(0.50–0.51) for KRAS mutation.

Conclusion:

EGFR mutant primary tumors were significantly smaller (for all volumetric features) than KRAS or Wild‐Type. Moreover, all volumetric features were significantly predictive for EGFR. KRAS and Wild‐type could not be discriminated only based on volumetric features. A larger set of imaging features (e.g. Radiomics) would help find more predictive biomarkers for tumor mutation status.
SU‐E‐J‐248: Contributions of Tumor and Stroma Phenotyping in Computer‐Aided Diagnosis
Authors: Li H , Lan L , Sennett C , Giger M
Published: June 2015

Purpose:

To gain insight into the role of parenchyma stroma in the characterization of breast tumors by incorporating computerized mammographic parenchyma assessment into breast CADx in the task of distinguishing between malignant and benign lesions.

Methods:

This study was performed on 182 biopsy‐proven breast mass lesions, including 76 benign and 106 malignant lesions. For each full‐field digital mammogram (FFDM) case, our quantitative imaging analysis was performed on both the tumor and a region‐of‐interest (ROI) from the normal contralateral breast. The lesion characterization includes automatic lesion segmentation and feature extraction. Radiographic texture analysis (RTA) was applied on the normal ROIs to assess the mammographic parenchymal patterns of these contralateral normal breasts. Classification performance of both individual computer extracted features and the output from a Bayesian artificial neural network (BANN) were evaluated with a leave‐one‐lesion‐out method using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the figure of merit.

Results:

Lesion characterization included computer‐extracted phenotypes of spiculation, size, shape, and margin. For parenchymal pattern characterization, five texture features were selected, including power law beta, contrast, and edge gradient. Merging of these computer‐selected features using BANN classifiers yielded AUC values of 0.79 (SE=0.03) and 0.67 (SE=0.04) in the task of distinguishing between malignant and benign lesions using only tumor phenotypes and texture features from the contralateral breasts, respectively. Incorporation of tumor phenotypes with parenchyma texture features into the BANN yielded improved classification performance with an AUC value of 0.83 (SE=0.03) in the task of differentiating malignant from benign lesions.

Conclusion:

Combining computerized tumor and parenchyma phenotyping was found to significantly improve breast cancer diagnostic accuracy highlighting the need to consider both tumor and stroma in decision making.

Funding: University of Chicago Dean Bridge Fund, NCI U24‐CA143848‐05, P50‐CA58223 Breast SPORE program, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a cofounder and stockholder in Quantitative Insights.
SU‐E‐J‐249: Characterization of Gynecological Tumor Heterogeneity Using Texture Analysis in the Context of An 18F‐FDG PET Adaptive Protocol
Authors: Nawrocki J , Chino J , Das S , Craciunescu O
Published: June 2015

Purpose:

We propose a method to examine gynecological tumor heterogeneity using texture analysis in the context of an adaptive PET protocol in order to establish if texture metrics from baseline PET‐CT predict tumor response better than SUV metrics alone as well as determine texture features correlating with tumor response during radiation therapy.

Methods:

This IRB approved protocol included 29 women with node positive gynecological cancers visible on FDG‐PET treated with EBRT to the PET positive nodes. A baseline and intra‐treatment PET‐CT was obtained. Tumor outcome was determined based on RECIST on posttreatment PET‐CT. Primary GTVs were segmented using 40% threshold and a semi‐automatic gradient‐based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). SUV histogram features, Metabolic Volume (MV), and Total Lesion Glycolysis (TLG) were calculated. Four 3D texture matrices describing local and regional relationships between voxel intensities in the GTV were generated: co‐occurrence, run length, size zone, and neighborhood difference. From these, 39 texture features were calculated. Prognostic power of baseline features derived from gradientbased and threshold GTVs were determined using the Wilcoxon rank‐sum test. Receiver Operating Characteristics and logistic regression was performed using JMP (SAS Institute Inc., Cary, NC) to find probabilities of predicting response. Changes in features during treatment were determined using the Wilcoxon signed‐rank test.

Results:

Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non‐responders. Comparing CR/PR vs. NR for gradient‐based GTVs, 7 texture values, TLG, and SUV kurtosis had a p < 0.05. Threshold GTVs yielded 4 texture features and TLG with p < 0.05. From baseline to intra‐treatment, 14 texture features, SUVmean, SUVmax, MV, and TLG changed with p < 0.05.

Conclusion:

Texture analysis of PET imaged gynecological tumors is an effective method for early prognosis and should be used complimentary to SUV metrics, especially when using gradient based segmentation.
SU‐E‐J‐250: A Machine Learning Approach for Creating Texture‐Preserved MRI Tumor Models From Clinical Sequences
Authors: Vallieres M , Boustead A , Laberge S , Levesque I R. , El Naqa I
Published: June 2015

Purpose:

We hypothesize that MRI texture‐based tumor outcome prediction models could be optimized via numerical simulations of image acquisitions. These simulations require knowledge of T1 and T2 relaxation times as inputs. The goal of this study is to evaluate the feasibility of using machine learning techniques to infer T1 and T2 tumor maps with accurate texture preservation for simulation inputs from clinical sequences.

Methods:

Clinical T1‐weighted (T1w) and T2‐weighted fat‐saturated (T2FS) scans, and measured T1 and T2 maps from eight patients with soft‐tissue sarcomas were used in this study. Measured T1 and T2 maps were computed using pulse sequences with variable flip angles and echo times, respectively. General regression neural networks (GRNNs) were trained on these data to infer T1 and T2 relaxation times from T1w and T2FS images. Four texture features were extracted to evaluate texture preservation: GLCM/Entropy, GLRLM/Gray‐Level Variance (GLV), GLSZM/Zone Size Variance (ZSV) and NGTDM/Complexity. The GRNN ability to estimate T1 and T2 relaxation times was assessed using leave‐one‐out cross‐validation.

Results:

The average T1 and T2 relaxation times within the tumor region of all patients were (1515 ± 542) ms and (226 ± 151) ms in the measured cases, and (1546 ± 546) ms and (249 ± 145) ms in the estimated cases, respectively. The average root‐mean‐square errors between measured and estimated relaxation times were 573 ms for T1 and 160 ms for T2. The average absolute percentage differences between measured and estimated GLCM/Entropy, GLRLM/GLV, GLSZM/ZSV and NGTDM/Complexity features were 5.1%, 0.02%, 0.0% and 16.2% for T1 maps, and 7.7%, 0.04%, 0.0% and 10.9% for T2 maps, respectively.

Conclusion:

From a texture preservation perspective, this work demonstrates the feasibility to create MRI numerical models using GRNNs from T1w and T2FS clinical scans. Further work is required to obtain higher accuracy for T1 and T2 absolute relaxation times.

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under the scholarship CGSD3‐426742‐2012, as well as it was supported by the Canadian Institutes of Health Research (CIHR) under grant MOP‐136774.
SU‐E‐J‐251: Incorporation of Pre‐Therapy 18F‐FDG Uptake with CT Texture Features in a Predictive Model for Radiation Pneumonitis Development
Authors: Anthony G , Cunliffe A , Castillo R , Pham N , Guerrero T , Armato S , Al‐Hallaq H
Published: June 2015

Purpose:

To determine whether the addition of standardized uptake value (SUV) statistical variables to CT lung texture features can improve a predictive model of radiation pneumonitis (RP) development in patients undergoing radiation therapy.

Methods:

Anonymized data from 96 esophageal cancer patients (18 RP‐positive cases of Grade ≥ 2) were retrospectively collected including pre‐therapy PET/CT scans, pre‐/posttherapy diagnostic CT scans and RP status. Twenty texture features (firstorder, fractal, Laws’ filter and gray‐level co‐occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. The mean, maximum, standard deviation, and 50th–95th percentiles of the SUV values for all lung voxels in the corresponding PET scans were acquired. For each texture feature, a logistic regression‐based classifier consisting of (1) the average change in that texture feature value between the pre‐ and post‐therapy CT scans and (2) the pre‐therapy SUV standard deviation (SUVSD) was created. The RP‐classification performance of each logistic regression model was compared to the performance of its texture feature alone by computing areas under the receiver operating characteristic curves (AUCs). T‐tests were performed to determine whether the mean AUC across texture features changed significantly when SUVSD was added to the classifier.

Results:

The AUC for single‐texturefeature classifiers ranged from 0.58–0.81 in high‐dose (≥ 30 Gy) regions of the lungs and from 0.53–0.71 in low‐dose (< 10 Gy) regions. Adding SUVSD in a logistic regression model using a 50/50 data partition for training and testing significantly increased the mean AUC by 0.08, 0.06 and 0.04 in the low‐, medium‐ and high‐dose regions, respectively.

Conclusion:

Addition of SUVSD from a pre‐therapy PET scan to a single CT‐based texture feature improves RP‐classification performance on average. These findings demonstrate the potential for more accurate prediction of RP using information from multiple imaging modalities.

Supported, in part, by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number T32 EB002103; SGA receives royalties and licensing fees through the University of Chicago for computer‐aided diagnosis technology. HA receives royalties through the University of Chicago for computer‐aided diagnosis technology.
SU‐E‐J‐254: Evaluating the Role of Mid‐Treatment and Post‐Treatment FDG‐PET/CT in Predicting Progression‐Free Survival and Distant Metastasis of Anal Cancer Patients Treated with Chemoradiotherapy
Authors: Zhang H , Wang J , Chuong M , D'souza W , Latifi K , Saeed Nadia , Tan S , Choi W , Hoffe S , Shridhar R , Moros E , Lu W
Published: June 2015

Purpose:

To evaluate the role of mid‐treatment and post‐treatment FDG‐PET/CT in predicting progression‐free survival (PFS) and distant metastasis (DM) of anal cancer patients treated with chemoradiotherapy (CRT).

Methods:

17 anal cancer patients treated with CRT were retrospectively studied. The median prescription dose was 56 Gy (range, 50–62.5 Gy). All patients underwent FDG‐PET/CT scans before and after CRT. 16 of the 17 patients had an additional FDG‐PET/CT image at 3–5 weeks into the treatment (denoted as mid‐treatment FDG‐PET/CT). 750 features were extracted from these three sets of scans, which included both traditional PET/CT measures (SUVmax, SUVpeak, tumor diameters, etc.) and spatialtemporal PET/CT features (comprehensively quantify a tumor's FDG uptake intensity and distribution, spatial variation (texture), geometric property and their temporal changes relative to baseline). 26 clinical parameters (age, gender, TNM stage, histology, GTV dose, etc.) were also analyzed. Advanced analytics including methods to select an optimal set of predictors and a model selection engine, which identifies the most accurate machine learning algorithm for predictive analysis was developed.

Results:

Comparing baseline + mid‐treatment PET/CT set to baseline + posttreatment PET/CT set, 14 predictors were selected from each feature group. Same three clinical parameters (tumor size, T stage and whether 5‐FU was held during any cycle of chemotherapy) and two traditional measures (pre‐ CRT SUVmin and SUVmedian) were selected by both predictor groups. Different mix of spatial‐temporal PET/CT features was selected. Using the 14 predictors and Naive Bayes, mid‐treatment PET/CT set achieved 87.5% accuracy (2 PFS patients misclassified, all local recurrence and DM patients correctly classified). Post‐treatment PET/CT set achieved 94.0% accuracy (all PFS and DM patients correctly predicted, 1 local recurrence patient misclassified) with logistic regression, neural network or support vector machine model.

Conclusion:

Applying radiomics approach to either midtreatment or post‐treatment PET/CT could achieve high accuracy in predicting anal cancer treatment outcomes.

This work was supported in part by the National Cancer Institute Grant R01CA172638.
SU‐E‐J‐255: Automatic Segmentation Refined, Multiple Slice‐Wise Voting Based Classification of Tumors From MRI
Authors: Veeraraghavan H , Lakhman Y , Feier D , Vargas H , Hricak H , Deasy J , Sala E
Published: June 2015

Purpose:

Development of technical methods to optimally classify tumors via magnetic resonance imaging.

Methods:

The methods were developed and tested on unusual appearing leiomyomas (ULM) from leiomyosarcomas (LMS). We developed a fully automated method for distinguishing between ULM and LMS from T2‐w MR images. Data consisted of 39 patients with histologically proven ULM(=22) and LMS(=17) who underwent preoperative ≥1.5 MRI. 13 MR images were obtained at our institution and rest from elsewhere. Our method consists of several steps. First, all the images were histogram matched, following which the manually segmented tumors were refined through automatic volumetric image segmentation. Next, several image features consisting of 5 Haralick textures, 4 Gabor edges at (0°,45°,90°,135°) and bandwidth 1.414 and Haralick textures computed on each Gabor image resulting in 25 different features was computed from inside the segmented tumors. The features were preweighted by their relevance determined using a paired t‐test and trained using a random forest classifier. We improved the generalization performance of the classifier by boosting the number of samples by using slice‐wise computed textures resulting in a total of 604 samples with ULM (n=351) and LMS (n=253). In the slice‐wise method, classifier is built that applies to individual slices; the slice results are then combined using voting to determine an overall per‐patient classification.

Results:

We evaluated the performance of the classifier for volumetric texture‐based classification with Leave One Out Cross Validation and slice‐wise textures‐based classification with K=5 fold classification. The volumetric method achieved a classification accuracy of 72% in comparison to 87% using slice‐wise texture‐based classification. Gabor textures were more relevant than intensity‐based textures for distinguishing the ULM from LMS.

Conclusion:

We have demonstrated that a classifier method using automatic refinement of human drawn contours, along with voting on slice‐based classification, results in an effective tumor image classifier.
SU‐E‐J‐259; How Does CT Reconstruction Kernel Affect the Radiogenomic Features in Non‐Small Cell Lung Cancer?
Authors: Jin H , Ahn C , Kuo M , Kim J
Published: June 2015

Purpose:

Radiogenomics promises to discover quantitative imaging features which are associated with genomic profiles and of prognostic in cancer patients. However, the CT imaging features are known to be sensitive to noise characteristic and affected by CT parameters. We investigate the variability of CT imaging features which are previously reported as radiogenomic markers in non‐small cell lung cancer (NSCLC).

Methods:

Three NSCLC cases of CT exams with 2 reconstruction kernels (B30f, B60f) were selected. We set ten ROIs within tumor mask. Run length nonuniformity (RLN) and histogram energy, which were previously reported to be predictive of patient survival, were calculated for each ROI. For establishing the intrinsic variability of image feature depending on the reconstruction kernel, we carried out the same feature extraction procedure on the COPDGene phantom CT images. The coefficient of variation (CoV) was compared between imaging features from images of single reconstruction kernel and mixed kernel image set.

Results:

RLN and Energy of standard kernel for phantom data showed higher values up to 366% than that of sharp kernel. For patient cases, the image features of standard kernel was higher 196% than that of sharp kernel on an average. The CoV of RLN was 0.142±0.035 for B30f data set and 0.060±0.015 for B60f data set in patient case study. For the mixed kernel dataset, the CoV of RLN increased to 0.407±0.061. The CoV of Energy feature values showed similar trends with RLN.

Conclusion:

Out study revealed high variability of CT image features depending on reconstruction kernel. These characteristic should be considered in feature extraction procedure when different kernels are used in the patient dataset. Use of the same CT protocol is preferred. Otherwise, application of kernel normalization techniques is necessary in the radiogenomic study.
SU‐E‐J‐260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy
Authors: Choi W , Kang M , Wang J , Lu W
Published: June 2015

Purpose:

To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy.

Methods:

Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver on pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student's t‐test to rank the effectiveness of features for the prediction of response.

Results:

The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09).

Conclusion:

Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT.

This work was supported in part by the National Cancer Institute Grant R01CA172638.
SU‐E‐J‐262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F‐FDG PET Adaptive Protocol
Authors: Nawrocki J , Chino J , Das S , Craciunescu O
Published: June 2015

Purpose:

This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra‐treatment PET‐CT, we can isolate unreliable texture features due to large variation.

Methods:

A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET‐CT were obtained on a Siemens Biograph mCT. Each clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra‐treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi‐automatic gradient‐based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in‐house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co‐occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra‐treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed‐rank test in order to isolate variations due to reconstruction parameters.

Results:

For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05.

Conclusion:

Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.
SU‐E‐J‐264: Using Magnetic Resonance Imaging‐Derived Features to Quantify Radiotherapy‐Induced Normal Tissue Morbidity
Authors: Thor M , Tyagi N , Deasy J
Published: June 2015

Purpose:

The aim of this study was to explore the use of Magnetic Resonance Imaging (MRI)‐derived features as indicators of Radiotherapy (RT)‐induced normal tissue morbidity. We also investigate the relationship between these features and RT dose in four critical structures.

Methods:

We demonstrate our approach for four patients treated with RT for base of tongue cancer in 2005–2007. For each patient, two MRI scans (T1‐weighted pre (T1pre) and post (T1post) gadolinium contrast‐enhancement) were acquired within the first six months after RT. The assessed morbidity endpoint observed in 2/4 patients was Grade 2+ CTCAEv.3 trismus. Four ipsilateral masticatory‐related structures (masseter, lateral and medial pterygoid, and the temporal muscles) were delineated on both T1pre and T1post and these scans were co‐registered to the treatment planning CT using a deformable demons algorithm. For each structure, the maximum and mean RT dose, and six MRI‐derived features (the second order texture features entropy and homogeneity, and the first order mean, median, kurtosis, and skewness) were extracted and compared structure‐wise between patients with and without trismus. All MRI‐derived features were calculated as the difference between T1pre and T1post, ΔS.

Results:

For 5/6 features and all structures, ΔS diverged between trismus and non‐trismus patients particularly for the masseter, lateral pterygoid, and temporal muscles using the kurtosis feature (−0.2 vs. 6.4 for lateral pterygoid). Both the maximum and mean RT dose in all four muscles were higher amongst the trismus patients (with the maximum dose being up to 25 Gy higher).

Conclusion:

Using MRI‐derived features to quantify RT‐induced normal tissue complications is feasible. We showed that several features are different between patients with and without morbidity and that the RT dose in all investigated structures are higher amongst patients with morbidity. MRI‐derived features, therefore, has the potential to improve predictions of normal tissue morbidity.
SU‐E‐J‐265: Feasibility Study of Texture Analysis for Prognosis of Local Tumor Recurrence Within 5‐Years for Pharyngeal‐Laryngeal Carcinoma Patients Received Radiotherapy Treatment
Authors: Huang W , Tu S
Published: June 2015

Purpose:

Pharyngeal and laryngeal carcinomas (PLC) are among the top leading cancers in Asian populations. Typically the tumor may recur and progress in a short period of time if radiotherapy fails to deliver a successful treatment. Here we used image texture features extracted from images of computed tomography (CT) planning and conducted a retrospective study to evaluate whether texture analysis is a feasible approach to predict local tumor recurrence for PLC patients received radiotherapy treatment.

Methods:

CT planning images of 100 patients with PLC treated by radiotherapy at our facility between 2001 and 2010 are collected. These patients were received two separate CT scans, before and mid‐course of the treatment delivery. Before the radiotherapy, a CT scanning was used for the first treatment planning. A total of 30 fractions were used in the treatment and patients were scanned with a second CT around the end of the fifteenth delivery for an adaptive treatment planning. Only patients who were treated with intensity modulated radiation therapy and RapidArc were selected. Treatment planning software of Eclipse was used. The changes of texture parameters between two CT acquisitions were computed to determine whether they were correlated to the local tumor recurrence. The following texture parameters were used in the preliminary assessment: mean, variance, standard deviation, skewness, kurtosis, energy, entropy, inverse difference moment, cluster shade, inertia, cluster prominence, gray‐level co‐occurrence matrix, and gray‐level run‐length matrix. The study was reviewed and approved by the committee of our institutional review board.

Results:

Our calculations suggested the following texture parameters were correlated with the local tumor recurrence: skewness, kurtosis, entropy, and inertia (p<0.0.05).

Conclusion:

The preliminary results were positive. However some works remain crucial to be completed, including addition of texture parameters for different image features, sensitivity of tumor segmentation variations, and effect of image filtering.
SU‐E‐J‐267: Change in Mean CT Intensity of Lung Tumors During Radiation Treatment
Authors: Mahon R , Tennyson N , Weiss E , Hugo G
Published: June 2015

Purpose:

To evaluate CT intensity change of lung tumors during radiation therapy.

Methods:

Repeated 4D CT images were acquired on a CT simulator during the course of therapy for 27 lung cancer patients on IRB approved protocols. All subjects received definitive radiation treatment ± chemotherapy. CT scans were completed prior to treatment, and 2–7 times during the treatment course. Primary tumor was delineated by an experienced Radiation Oncologist. Contours were thresholded between −100 HU and 200 HU to remove airways and bone. Correlations between the change in the mean tumor intensity and initial tumor intensity, SUVmax, and tumor volume change rate were investigated. Reproducibility was assessed by evaluating the variation in mean intensity over all phases in 4DCT, for a subgroup of 19 subjects.

Results:

Reproducibility of tumor intensity between phases as characterized by the root mean square of standard deviation across 19 subjects was 1.8 HU. Subjects had a mean initial tumor intensity of 16.5 ± 11.6 HU and an overall reduction in HU by 10.3 ± 8.5 HU. Evaluation of the changes in tumor intensity during treatment showed a decrease of 0.3 ± 0.3 HU/day for all subjects, except three. No significant correlation was found between change in HU/day and initial HU intensity (p=0.53), initial PET SUVmax (p=0.69), or initial tumor volume (p=0.70). The rate of tumor volume change was weakly correlated (R^2=0.05) with HU change (p=0.01).

Conclusion:

Most lung cancer subjects showed a marked trend of decreasing mean tumor CT intensity throughout radiotherapy, including early in the treatment course. Change in HU/day is not correlated with other potential early predictors for response, such as SUV and tumor volume change. This Result supports future studies to evaluate change in tumor intensity on CT as an early predictor of response.
SU‐E‐J‐269: Tracking of Tumor Regression for Stage III Lung Cancer Using CBCT
Authors: Kang K , Biswas T , Podder T
Published: June 2015

Purpose:

This study is to evaluate the tumor regression over the course of EBRT treatment and to determine the difference of tumor reduction for stage III lung squamous cell cancer (SCC) and adenocarcinoma using CBCT.

Methods:

Twenty three stage III lung cancer patients treated in our clinic who had daily cone beam CT (CBCT) were selected for this study (16 adenocarcinoma and 7 SCC cases). Patients received prescription dose in the range of 50Gy–71.4Gy (mean =60.3Gy, median =50Gy) at 1.8Gy or 2Gy per fraction. Treatments spanned over a minimum of five weeks. Initial mean volume of the gross tumor volume (GTV) was 123cc (range = 14.7cc–353.3cc). For this study, we choose six sets of CBCTs at an interval of one week, starting from the first fraction of treatment. Daily CBCTs from treatment linac computer were transferred to MIM Software version 6.0. An experienced physician contoured the primary GTV on each slices of the CBCT for these patients.

Results:

A consistent regression of the GTVs was observed in all patients, except in one patient (adeno case) where GTV did not change. Weekly volumetric reduction was in the range of 11.2%–16.6%. Maximum reductions were noticed in the first two weeks of the treatment cycle; mean overall (for adeno+SCC) reductions were 16.6%, 14.2% in week‐1 and week‐2, respectively. Mean reduction over five weeks of treatment was 49.8% (range = 0.1%–75.5%). Higher reduction was observed in SCC patients as compare to adenocarcinoma cases (54.9% vs. 47.6%); however, the difference was not statistically significant (p‐value > 0.05).

Conclusion:

Large regression of tumors over the course of EBRT for stage III lung cancer patients was observed. Both SCC and adenocarcinoma responded well; overall reduction for SCC cases was higher. A future study is warranted for determining the co‐relation between tumor volume reduction and treatment outcome.
SU‐E‐J‐270: Repeated 18F‐FDG PET/CTs Based Feature Analysis for the Predication of Anal Cancer Recurrence
Authors: Wang J , Chuong M , Latifi K , Saeed N , Tan S , Choi W , Hoffe S , Shridhar R , Moros E , Lu W
Published: June 2015

Purpose:

To identify PET/CT based imaging predictors of anal cancer recurrence and evaluate baseline vs. mid‐treatment vs. post‐treatment PET/CT scans in the tumor recurrence prediction.

Methods:

FDG‐PET/CT scans were obtained at baseline, during chemoradiotherapy (CRT, midtreatment), and after CRT (post‐treatment) in 17 patients of anal cancer. Four patients had tumor recurrence. For each patient, the mid‐treatment and post‐treatment scans were respectively aligned to the baseline scan by a rigid registration followed by a deformable registration. PET/CT image features were computed within the manually delineated tumor volume of each scan to characterize the intensity histogram, spatial patterns (texture), and shape of the tumors, as well as the changes of these features resulting from CRT. A total of 335 image features were extracted. An Exact Logistic Regression model was employed to analyze these PET/CT image features in order to identify potential predictors for tumor recurrence.

Results:

Eleven potential predictors of cancer recurrence were identified with p < 0.10, including five shape features, five statistical texture features, and one CT intensity histogram feature. Six features were indentified from posttreatment scans, 3 from mid‐treatment scans, and 2 from baseline scans. These features indicated that there were differences in shape, intensity, and spatial pattern between tumors with and without recurrence. Recurrent tumors tended to have more compact shape (higher roundness and lower elongation) and larger intensity difference between baseline and follow‐up scans, compared to non‐recurrent tumors.

Conclusion:

PET/CT based anal cancer recurrence predictors were identified. The post‐CRT PET/CT is the most important scan for the prediction of cancer recurrence. The baseline and mid‐CRT PET/CT also showed value in the prediction and would be more useful for the predication of tumor recurrence in early stage of CRT.

This work was supported in part by the National Cancer Institute Grant R01CA172638.
SU‐E‐J‐271: Correlation of CT Number Change with Radiation Treatment Response for Pancreatic Cancer
Authors: Dalah E , Tai A , Oshima K , Hall W , Knechtges P , Erickson B , Li X
Published: June 2015

Purpose:

It has been reported recently that radiation can induce CT number (CTN) change during radiation therapy (RT) delivery. In the effort to explore whether CTN can be used to assess RT response, we analyze the relationship between the pathological treatment response (PTR) and the changes of CTN, MRI, and PET before and after the neoadjuvant chemoradiation (nCR) for pancreatic adenocarcinoma.

Methods:

The preand post‐nCR CT, MRI, and PET data for a total of 8 patients with resectable, or borderline resectable pancreatic head adenocarcinoma treated with nCR were retrospectively analyzed. Radiographic characteristics were correlated to PTR data. The histograms, means and standard derivations (SD) of the CTNs in pancreatic head (CTNPH), the GTV defined by ADC (CTNGTV), and the rest of pancreatic head (CTNPH‐CTNGTV) were compared. Changes before and after nCR were correlated with the corresponding changes of ADC, lean body mass normalized SUV (SUVlb), and PTR using Pearson’ s correlation coefficient test.

Results:

The average mean and SD in CTPH for all the patients analyzed were higher in post‐nCR (53.17 ± 31.05 HU) compared to those at pre‐nCR (28.09 ± 4.253 HU). The CTNGTV were generally higher than CTNPH and CTNPH‐CTNGTV, though the differences were not significant. The post‐nCR changes of mean CTN, ADC, and SUVlb values in pancreatic head were correlated with PTR (R=0.3273/P=0.5357, R=−0.5455/P<0.0001, and R=0.7638/P=0.0357, respectively). The mean difference in the maximum tumor dimension measured from CTN, ADC, and SUVlb as compared with pathological measurements was −2.1, −0.5, and 0.22 cm, respectively.

Conclusion:

The radiation‐induced change of CTN in pancreas head after chemoradiation therapy of pancreatic cancer was observed, which may be related to treatment responses as assessed by biological imaging and pathology. More data are needed to determine whether the CTN can be used as a quantitative biomarker for response to neoadjuvant therapy.