bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐CD‐BRB‐02: BEST IN PHYSICS (JOINT IMAGING‐THERAPY): Identification of Molecular Phenotypes by Integrating Radiomics and Genomics
Authors: Grossmann P , Grove O , El‐Hachem N , Velazquez E Rios , Parmar C , Leijenaar R , Haibe‐Kains B , Lambin P , Gillies R , Aerts H 
Published: June 2015

Purpose:To uncover the mechanistic connections between radiomic features, molecular pathways, and clinical outcomes, to develop radiomic based predictors of pathway activation states in individual patients, and to assess whether combining radiomic with clinical and genomic data improves prognostication.Methods:We analyzed two independent lung cancer cohorts totaling 351 patients, for whom diagnostic computed tomography (CT) scans, gene‐expression profiles, and clinical outcomes were available. The tumor phenotype was characterized based on 636 radiomic features describing tumor intensity, texture, shape and size. We performed an integrative analysis by developing and independently validating association modules of coherently expressed radiomic features and molecular pathways. These modules were statistically tested for significant associations to overall survival (OS), TNM stage, and pathologic histology.Results:We identified thirteen radiomic‐pathway association modules (p < 0.05), the most prominent of which were associated with the immune system, p53 pathway, and other pathways involved in cell cycle regulation. Eleven modules were significantly associated with clinical outcomes (p < 0.05). Strong predictive power for pathway activation states in individual patients was observed using radiomics; the strongest per module predictions ranged from an intra‐tumor heterogeneity feature predicting RNA III polymerase transcription (AUC 0.62, p = 0.03), to a tumor intensity dispersion feature predicting pyruvate metabolism and citric acid TCA cycle (AUC 0.72, p < 10−⁶). Stepwise combinations of radiomic data with clinical outcomes and gene expression profiles resulted in consistent increases of prognostic power to predict OS (concordance index max = 0.73, p < 10−9).Conclusion:This study demonstrates that radiomic approaches permit a non‐invasive assessment of molecular and clinical characteristics of tumors, and therefore have the unprecedented potential to cost‐effectively advance clinical decision‐making using routinely acquired, standard‐of‐care imaging data. We show that prognostic value complementary to clinical and genomic information can be obtained by radiomic strategies.
TU‐CD‐BRB‐03: Radiomics Investigation in the Distinction Between in Situ and Invasive Breast Cancers
Authors: Drukker K , Schram J , Burda S , Li H , Lan L , Giger M 
Published: June 2015

Purpose:To determine the capability of machine learning and computer‐extracted image phenotypes to distinguish between ductal carcinoma in situ and invasive breast cancers, which may facilitate timely assessment of disease prognosis and treatment.Methods:This study used a HIPAA compliant data set of 248 breast dynamic contrast‐enhanced MR images of breast cancers collected under IRB‐approved protocols (58 in situ and 190 invasive cancers). After automated 3D tumor segmentation, 38 image phenotypes were computer‐extracted, describing tumor size, shape, morphology, enhancement texture, variance kinetics, and kinetic curve characteristics. A random forest classifier was used in a leave‐one‐case‐out training/testing paradigm for the distinction between in situ and invasive breast cancer. Performance for this task was assessed using the area under the receiver operating characteristic curve (AUC). The random forest classifier also automatically determined the relevance of all 38 phenotypes to the task at hand.Results:The random forest classifier obtained an AUC of 0.90 (95% confidence interval [0.86;0.95]) in the task of distinguishing between in situ and invasive breast cancers. Phenotypes most important here were shape (sphericity), enhancement texture (variance, difference variance, heterogeneity, contrast), and kinetic curve characteristics (normalized total rate variance, washout rate, time to peak). Phenotypes least effective were size (maximum diameter, surface area), morphology (margin gradient, margin sharpness), variance kinetics, and a kinetic curve characteristic (maximum enhancement).Conclusion:We obtained promising results in automated MR image‐based assessment of breast tumor invasiveness. In the current era of personalized medicine such analysis may positively impact patient care by identifying which breast tumors require timely work‐up because of invasive components, and which findings could potentially be monitored without aggressive treatment. The image‐based phenotypes that emerged in this study are markedly different from those relevant to the distinction between benign and malignant breast tumors, where mainly size and morphology influenced diagnostic decision making.Funding: NIH S10 RR021039, University of Chicago Dean Bridge Fund. and Carole Segal. COI: M.L.G. 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 of and stockholder in Quantitative Insights.
TU‐CD‐BRB‐08: Radiomic Analysis of FDG‐PET Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated with SBRT
Authors: Cui Y , Song J , Pollom E , Shirato H , Chang D , Koong A , Li R 
Published: June 2015

Purpose:This study aims to identify novel prognostic imaging biomarkers in locally advanced pancreatic cancer (LAPC) using quantitative, high‐throughput image analysis.Methods:86 patients with LAPC receiving chemotherapy followed by SBRT were retrospectively studied. All patients had a baseline FDG‐PET scan prior to SBRT. For each patient, we extracted 435 PET imaging features of five types: statistical, morphological, textural, histogram, and wavelet. These features went through redundancy checks, robustness analysis, as well as a prescreening process based on their concordance indices with respect to the relevant outcomes. We then performed principle component analysis on the remaining features (number ranged from 10 to 16), and fitted a Cox proportional hazard regression model using the first 3 principle components. Kaplan‐Meier analysis was used to assess the ability to distinguish high versus low‐risk patients separated by median predicted survival. To avoid overfitting, all evaluations were based on leave‐one‐out cross validation (LOOCV), in which each holdout patient was assigned to a risk group according to the model obtained from a separate training set.Results:For predicting overall survival (OS), the most dominant imaging features were wavelet coefficients. There was a statistically significant difference in OS between patients with predicted high and low‐risk based on LOOCV (hazard ratio: 2.26, p<0.001). Similar imaging features were also strongly associated with local progression‐free survival (LPFS) (hazard ratio: 1.53, p=0.026) on LOOCV. In comparison, neither SUVmax nor TLG was associated with LPFS (p=0.103, p=0.433) (Table 1). Results for progression‐free survival and distant progression‐free survival showed similar trends.Conclusion:Radiomic analysis identified novel imaging features that showed improved prognostic value over conventional methods. These features characterize the degree of intra‐tumor heterogeneity reflected on FDG‐PET images, and their biological underpinnings warrant further investigation. If validated in large, prospective cohorts, this method could be used to stratify patients based on individualized risk.
TU‐CD‐BRA‐04: Evaluation of An Atlas‐Based Segmentation Method for Prostate and Peripheral Zone Regions On MRI
Authors: Nelson AS , Piper J , Curry K , Swallen A , Padgett K , Pollack A , Stoyanova RS 
Published: June 2015

Purpose:Prostate MRI plays an important role in diagnosis, biopsy guidance, and therapy planning for prostate cancer. Prostate MRI contours can be used to aid in image fusion for ultrasound biopsy guidance and delivery of radiation. Our goal in this study is to evaluate an automatic atlas‐based segmentation method for generating prostate and peripheral zone (PZ) contours on MRI.Methods:T2‐weighted MRIs were acquired on 3T‐Discovery MR750 System (GE, Milwaukee). The Volumes of Interest (VOIs): prostate and PZ were outlined by an expert radiation oncologist and used to create an atlas library for atlas‐based segmentation. The atlas‐segmentation accuracy was evaluated using a leave‐one‐out analysis. The method involved automatically finding the atlas subject that best matched the test subject followed by a normalized intensity‐based free‐form deformable registration of the atlas subject to the test subject. The prostate and PZ contours were transformed to the test subject using the same deformation. For each test subject the three best matches were used and the final contour was combined using Majority Vote. The atlas‐segmentation process was fully automatic. Dice similarity coefficients (DSC) and mean Hausdorff values were used for comparison.Results:VOIs contours were available for 28 subjects. For the prostate, the atlas‐based segmentation method resulted in an average DSC of 0.88+/−0.08 and a mean Hausdorff distance of 1.1+/−0.9mm. The number of patients (#) in DSC ranges are as follows: 0.60–0.69(1), 0.70–0.79(2), 0.80–0.89(13), >0.89(11). For the PZ, the average DSC was 0.72+/−0.17 and average Hausdorff of 0.9+/−0.9mm. The number of patients (#) in DSC ranges are as follows: <0.60(4), 0.60–0.69(6), 0.70–0.79(7), 0.80–0.89(9), >0.89(1).Conclusion:The MRI atlas‐based segmentation method achieved good results for both the whole prostate and PZ compared to expert defined VOIs. The technique is fast, fully automatic, and has the potential to provide significant time savings for prostate VOI definition.AS Nelson and J Piper are partial owners of MIM Software, Inc. AS Nelson, J Piper, K Curry, and A Swallen are current employees at MIM Software, Inc.
TU‐CD‐BRA‐05: Atlas Selection for Multi‐Atlas‐Based Image Segmentation Using Surrogate Modeling
Authors: Zhao T , Ruan D 
Published: June 2015

Purpose:The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi‐atlas‐based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi‐atlas‐based image segmentation.Methods:We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non‐decreasing function with additive random perturbations. Subsequently, a surrogate's ability to prognosticate the oracle order for atlas subset selection is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images.Results:The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly‐used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89), compared to (0.81, 0.89). The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation.Conclusion:This work has developed an efficacious probabilistic model to characterize the image‐based surrogate metric on atlas selection. Analytical insights lead to valid guiding principles on fusion set size design.
TU‐CD‐BRA‐08: Single‐Energy Computed Tomography‐Based Pulmonary Perfusion Imaging: Proof‐Of‐Principle in a Canine Model
Authors: Yamamoto T , Kent M , Wisner E , Fujita Y , Boone J 
Published: June 2015

Purpose:Pulmonary perfusion imaging has provided significant insights into pulmonary diseases, and can be useful in radiotherapy. The purpose of this study was to prospectively establish proof‐of‐principle in a canine model for single‐energy CT‐based perfusion imaging, which has the potential for widespread clinical implementation.Methods:Single‐energy CT perfusion imaging is based on: (1) acquisition of inspiratory breath‐hold CT scans before and after intravenous injection of iodinated contrast medium, (2) deformable image registration (DIR) of the two CT image data sets, and (3) subtraction of the pre‐contrast image from post‐contrast image, yielding a map of Hounsfield unit (HU) enhancement. These subtraction image data sets hypothetically represent perfused blood volume, a surrogate for perfusion. In an IACUC‐approved clinical trial, we acquired pre‐ and post‐contrast CT scans in the prone posture for six anesthetized, mechanically‐ventilated dogs. The elastix algorithm was used for DIR. The registration accuracy was quantified using the target registration errors (TREs) for 50 pulmonary landmarks in each dog. The gradient of HU enhancement between gravity‐dependent (ventral) and non‐dependent (dorsal) regions was evaluated to quantify the known effect of gravity, i.e., greater perfusion in ventral regions.Results:The lung volume difference between the two scans was 4.3±3.5% on average (range 0.3%–10.1%). DIR demonstrated an average TRE of 0.7±1.0 mm. HU enhancement in lung parenchyma was 34±10 HU on average and varied considerably between individual dogs, indicating the need for improvement of the contrast injection protocol. HU enhancement in ventral (gravity‐dependent) regions was found to be greater than in dorsal regions. A population average ventral‐to‐dorsal gradient of HU enhancement was strong (R2=0.94) and statistically significant (p<0.01).Conclusion:This canine study demonstrated relatively accurate DIR and a strong ventral‐to‐dorsal gradient of HU enhancement, providing proof‐of‐principle for single‐energy CT pulmonary perfusion imaging. This ongoing study will enroll more dogs and investigate the physiological significance.This study was supported by a Philips Healthcare/Radiological Society of North America (RSNA) Research Seed Grant (RSD1458).
TU‐CD‐BRA‐10: Hybridized Deformable Registration Framework for Contrast‐Enhanced Dedicated Breast CT
Authors: Gazi P , Boone J 
Published: June 2015

Purpose:Utilization of contrast enhancement in dedicated breast CT (bCT) has been reported to be a reliable metric in determining the conspicuity of malignant breast lesions. In this study, we have designed and developed a framework of image segmentation and registration to align the structure of pre‐ and post‐contrast breast CT image.Methods:An iterative two‐means clustering method was used in image segmentation. The image segmentation algorithm results in segmenting the breast CT image to skin, adipose, fibroglandular and contrast‐enhanced lesions. These results are used in image registration method. A deformable image registration method code‐named Intensity Difference Adaptive Demons (IDAD) was developed based on the Demons image registration. Within the developed framework, the deformation field forces are calculated considering the contrast enhancement levels in the bCT image voxels. The performance of the developed framework was evaluated using mathematical simulations and patient breast CT images.Results:The proposed method outperformed conventional affine and other Demons variations for serial pre‐contrast and post‐contrast breast CT image alignment. In simulation studies, IDAD exhibited 1–11% improvement in Normalized Cross Correlation (NCC) compared to the conventional Demons approach with the improvement increasing with lesion size and contrast enhancement levels. Registration error measured by Target Registration Error (TRE) shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The implementation of the presented hybridized framework was implemented based on a parallel processing architecture, resulting in rapid execution time for the iterative segmentation and intensity‐adaptive registration techniques.Conclusion:Characterization of contrast‐enhanced lesions is improved using IDAD. Spatial subtraction of the aligned images yields useful diagnostic information with respect to lesion morphology.
TU‐CD‐BRA‐11: Application of Bone Suppression Technique to Inspiratory/expiratory Chest Radiography
Authors: Tanaka R , Sanada S , Sakuta K , Kawashima H , Kishitani Y 
Published: June 2015

Purpose:The bone suppression technique based on advanced image processing can suppress the conspicuity of bones on chest radiographs, creating soft tissue images normally obtained by the dual‐energy subtraction technique. This study was performed to investigate the usefulness of bone suppression technique in quantitative analysis of pulmonary function in inspiratory/expiratory chest radiography.Methods:Commercial bone suppression image processing software (ClearRead; Riverain Technologies) was applied to paired inspiratory/expiratory chest radiographs of 107 patients (normal, 33; abnormal, 74) to create corresponding bone suppression images. The abnormal subjects had been diagnosed with pulmonary diseases, such as pneumothorax, pneumonia, emphysema, asthma, and lung cancer. After recognition of the lung area, the vectors of respiratory displacement were measured in all local lung areas using a cross‐correlation technique. The measured displacement in each area was visualized as displacement color maps. The distribution pattern of respiratory displacement was assessed by comparison with the findings of lung scintigraphy.Results:Respiratory displacement of pulmonary markings (soft tissues) was able to be quantified separately from the rib movements on bone suppression images. The resulting displacement map showed a left‐right symmetric distribution increasing from the lung apex to the bottom region of the lung in many cases. However, patients with ventilatory impairments showed a nonuniform distribution caused by decreased displacement of pulmonary markings, which were confirmed to correspond to area with ventilatory impairments found on the lung scintigrams.Conclusion:The bone suppression technique was useful for quantitative analysis of respiratory displacement of pulmonary markings without any interruption of the rib shadows. Abnormal areas could be detected as decreased displacement of pulmonary markings. Inspiratory/expiratory chest radiography combined with the bone suppression technique has potential for predicting local lung function on the basis of dynamic analysis of pulmonary markings.This work was partially supported by Nakatani Foundation, Grant‐in‐aid for Scientific Research (C) of Ministry of Education, Culture, Sports, Science and Technology, JAPAN (Grant number : 24601007), and Nakatani Foundation, Mitsubishi Foundation, and the he Mitani Foundation for Research and Development. Yasushi Kishitani is a staff of TOYO corporation.
TU‐CD‐303‐00: In Memoriam of Warren Sinclair: Physics Applications for New Radiobiology
Authors: Chang Sha 
Published: June 2015

The Medical Physics and Radiation Protection communities lost one of their true pioneers in May, 2014 with the passing of Warren K. Sinclair, Ph.D. He received his Doctorate in Physics at the University of London, U.K. He was a fellow of the Institute of Physics and a Certified Health Physicist. After a number of posts at Hospitals and Universities in the U.K. and New Zealand, he became the Head, Physics Department at the M.D. Anderson Hospital, Houston, TX. He held a number of academic physics positions with the University of Texas, Austin at the main campus and at the Postgraduate School of Medicine, Houston. He later served as a Senior Biophysicist, Division Director, and then Associate Laboratory Director for Biomedical Division Director, and then Associate Laboratory Director for Biomedical and Environmental Research at the Argonne National Laboratory, IL. He had additional academic appointments at the University of California, Berkeley; Northern Illinois University; the University of Illinois (Chicago Circle Campus); and the University of Chicago.Warren was a charter member of the AAPM and a leader in many communities: President of the AAPM from 1960 to 1961 and the Journal Editor from 1964 to 1969, the 2nd President of the NCRP from 1977 to 1991, and the President of the Radiation Research Society from 1978 to 1979. His many awards included the William D. Coolidge Award in 1986. Even this barely scratches the surface of the many other organizations he served with and influenced including the ICRP, IOP, RSNA, SNM, and UNSCEAR. His research interests resulted in publications from 1948 to the 1990s in many aspects (physical and biological) of radiotherapy and the effects of ionizing radiation in general, leading to early definitions of RBE values for megavoltage radiotherapy as well as contributing to the development of the modern framework for radiation protection worldwide.Warren was not a retiring person and even after moving to NCRP President Emeritus status continued to provide advice and comments on publications and to mentor others until the time of his passing. Sadly, Warren's long‐time wife Joy passed away in December, 2014 but he is survived by his son Bruce and daughter Roslyn, 2 grandchildren and 4 great‐grandchildren.