bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.