bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐F‐R‐39: Effects of Radiation Dose Reduction On Renal Cell Carcinoma Discrimination Using Multi‐Phasic CT Imaging
Authors: Wahi‐Anwar M , Young S , Lo P , Raman S , Kim H , Brown M , McNitt‐Gray M , Coy H , Ashen‐Garry D , Pace‐Soler E 
Published: July 2016

Purpose:A method to discriminate different types of renal cell carcinoma (RCC) was developed using attenuation values observed in multiphasic contrast‐enhanced CT. This work evaluates the sensitivity of this RCC discrimination task at different CT radiation dose levels.Methods:We selected 5 cases of kidney lesion patients who had undergone four‐phase CT scans covering the abdomen to the lilac crest. Through an IRB‐approved study, the scans were conducted on 64‐slice CT scanners (Definition AS/Definition Flash, Siemens Healthcare) using automatic tube‐current modulation (TCM). The protocol included an initial baseline unenhanced scan, followed by three post‐contrast injection phases. CTDIvol (32 cm phantom) measured between 9 to 35 mGy for any given phase. As a preliminary study, we limited the scope to the cortico‐medullary phase—shown previously to be the most discriminative phase. A previously validated method was used to simulate a reduced dose acquisition via adding noise to raw CT sinogram data, emulating corresponding images at simulated doses of 50%, 25%, and 10%. To discriminate the lesion subtype, ROIs were placed in the most enhancing region of the lesion. The mean HU value of an ROI was extracted and used to discriminate to the worst‐case RCC subtype, ranked in the order of clear cell, papillary, chromophobe and the benign oncocytoma.Results:Two patients exhibited a change of worst case RCC subtype between original and simulated scans, at 25% and 10% doses. In one case, the worst‐case RCC subtype changed from oncocytoma to chromophobe at 10% and 25% doses, while the other case changed from oncocytoma to clear cell at 10% dose.Conclusion:Based on preliminary results from an initial cohort of 5 patients, worst‐case RCC subtypes remained constant at all simulated dose levels except for 2 patients. Further study conducted on more patients will be needed to confirm our findings.Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant Support from: U01 CA181156
SU‐F‐R‐40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment
Authors: Lee S , Markel D , Hegyi G , El Naqa I 
Published: July 2016

Purpose:The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting.Methods:We investigated 11 CT texture features for radiation‐induced lung disease from 3 categories (first‐order, grey level co‐ocurrence matrix (GLCM), and Law's filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2‐dimensional sub‐regions with a grid spacing of 31 mm. The distribution of the evaluated texture features from these sub‐regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non‐significant dependence on the factor.Results:Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law's filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First‐order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%.Conclusion:Amongst the studied features, GLCM and Law's filter texture features were more robust than first‐order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.
SU‐F‐R‐45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre‐Treatment and Post‐Treatment FDG PET Image for NSCLC Patients
Authors: Ma C , Yin Y 
Published: July 2016

Purpose:The purpose of this research is investigating which texture features extracted from FDG‐PET images by gray‐level co‐occurrence matrix(GLCM) have a higher prognostic value than the other texture features.Methods:21 non‐small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F‐FDG PET/CT scans with both pre‐treatment and post‐treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculated by using post‐treatment features to subtract pre‐treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features.Results:The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942.Conclusion:This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.
SU‐F‐R‐46: Predicting Distant Failure in Lung SBRT Using Multi‐Objective Radiomics Model
Authors: Zhou Z , Folkert M , Iyengar P , Zhang Y , Wang J 
Published: July 2016

Purpose:To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non‐small cell lung cancer (NSCLC) by using a new multi‐objective radiomics model.Methods:Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi‐objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi‐objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting‐based multi‐objective evolutionary computation algorithm II (NSGA‐II) is used for solving the multi‐objective optimization.Results:The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%.Conclusion:A new multi‐objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.
SU‐F‐R‐48: Early Prediction of Pathological Response of Locally Advanced Rectal Cancer Using Perfusion CT:A Prospective Clinical Study
Authors: Nie K , Shi L , Yue N , Jabbour S , Kim S , Mao T , Qian L , Hu X , Sun X , Niu T 
Published: July 2016

Purpose:To prospectively evaluate the tumor vascularity assessed by perfusion CT for prediction of chemo‐radiation treatment (CRT) response in locally advanced rectal cancer (LARC).Methods:Eighteen consecutive patients (61.9±8.8 years, from March–June 2015) diagnosed with LARC who underwent 6–8 weeks CRT followed by surgery were included. The pre‐treatment perfusion CT was acquired after a 5s delay of contrast agent injection for 45s with 1s interval. A total of 7‐cm craniocaudal range covered the tumor region with 3‐mm slice thickness. The effective radiation dose is around 15mSv, which is about 1.5 the conventional abdomen/pelvis CT dose. The parametric map of blood flow (BF), blood volume (BV), mean transit time (MTT), permeability (PMB), and maximum intensity map (MIP) were obtained from commercial software (Syngo‐CT 2011A, Siemens). An experienced radiation oncologist outlined the tumor based on the pre‐operative MR and pathologic residual region, but was blinded with regards to pathological tumor stage. The perfusion parameters were compared to histopathological response quantified by tumor regression grade as good‐responder (GR, TRG 0‐1) vs. non‐good responder (non‐GR). Furthermore, the predictive value for pathological complete response (pCR) was also investigated.Results:Both BV (p=0.02) and MTT (P=0.02) was significantly higher and permeambility was lower (p=0.004) in the good responders. The BF was higher in GR group but not statistically significant. Regarding the discrimination of pCR vs non‐pCR, the BF was higher in the pCR group (p=0.08) but none of those parameters showed statistically significant differences.Conclusion:BV and MTT can discriminate patients with a favorable response from those that fail to respond well, potentially selecting high‐risk patients with resistant tumors that may benefit from an aggressive preoperative treatment approach. However, future studies with more patient data are needed to verify the prognostic value of perfusion CT especially for pCR prediction.This work is supported by the National High‐tech R&D program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917), Natural Science Foundation of China (NSFC Grant No. 81201091).
SU‐F‐R‐49: A Novel Kinetic Model for Prediction of Tumor Local Control for Patients with Lung Cancer
Authors: Zhong H , Siddiqui S , Zhao B , Li H , Barton K , Siddiqui F , Movsas B , Chetty I 
Published: July 2016

Purpose:Modeling tumor control probability (TCP) can help optimize treatment plans for better treatment outcomes. This study sought to quantify the radiobiological parameters of the TCP model for patients with lung cancer.Methods:A two‐compartment kinetic model was developed to model tumor regression for five NSCLC patients. The model has three parameters: cell survival fraction, dead‐cell‐resolving time and tumor doubling time. The last one was extended to a function of tumor volume. Each of these patients was treated with 2 Gyx33 fractions. Daily CBCT images were acquired during the course of treatment. Gross tumor volume (GTV) was delineated on each of these CBCT images, and the visible tumor volumes were fitted to the kinetic model to optimize the parameters, where the Jacobian of this model was constrained by daily tumor volumetric changes.Results:Among the five patients, three had tumor recurrence: 455, 520 and 590 days after the completion of treatment. Recurrent tumor volumes were, respectively, 28.9, 13.7 and 4.8 cm3, measured at distant locations. With the assumption of cell density = 108 cells/cm3, tumor doubling times required for circulating tumor cells to progress to the recurrent volumes are 16.1, 19.2 and 23.1 days. By fitting the kinetic model with 33 measured tumor volumes, the average cell survival fraction for the five patients is 0.5±0.21, and tumor doubling times are in the range of 10∼35 days, comparable to the doubling time derived from tumor recurrence data. The doubling times modeled from invisible cells are different from the median (357 days) or mean (166 days) of the doubling time measured directly from visible tumor volumes.Conclusion:A kinetic model has been developed to simulate the process of tumor progressing from invisible cells to visible tumor volumes. This model may be useful for TCP prediction for patients with locally advanced lung cancer.
SU‐F‐R‐52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients
Authors: Huynh E , Coroller T , Narayan V , Agrawal V , Romano J , Franco I , Hou Y , Mak R , Aerts H 
Published: July 2016

Purpose:There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non‐invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lung cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence.Methods:FB and AIP images of 113 Stage I‐II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p‐values were corrected for multiple testing using the false discovery rate.Results:All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66).Conclusion:Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments.R.M. had consulting interest with Amgen (ended in 2015).
SU‐F‐R‐53: CT‐Based Radiomics Analysis of Non‐Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy
Authors: Huynh E , Coroller T , Narayan V , Agrawal V , Hou Y , Romano J , Franco I , Mak R , Aerts H 
Published: July 2016

Purpose:Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable non‐small cell lung cancer (NSCLC) patients and has demonstrated excellent local control and survival. However, some patients still develop distant metastases and local recurrence, and therefore, there is a clinical need to identify patients at high‐risk of disease recurrence. The aim of the current study is to use a radiomics approach to identify imaging biomarkers, based on tumor phenotype, for clinical outcomes in SBRT patients.Methods:Radiomic features were extracted from free breathing computed tomography (CT) images of 113 Stage I‐II NSCLC patients treated with SBRT. Their association to and prognostic performance for distant metastasis (DM), locoregional recurrence (LRR) and survival was assessed and compared with conventional features (tumor volume and diameter) and clinical parameters (e.g. performance status, overall stage). The prognostic performance was evaluated using the concordance index (CI). Multivariate model performance was evaluated using cross validation. All p‐values were corrected for multiple testing using the false discovery rate.Results:Radiomic features were associated with DM (one feature), LRR (one feature) and survival (four features). Conventional features were only associated with survival and one clinical parameter was associated with LRR and survival. One radiomic feature was significantly prognostic for DM (CI=0.670, p<0.1 from random), while none of the conventional and clinical parameters were significant for DM. The multivariate radiomic model had a higher median CI (0.671) for DM than the conventional (0.618) and clinical models (0.617).Conclusion:Radiomic features have potential to be imaging biomarkers for clinical outcomes that conventional imaging metrics and clinical parameters cannot predict in SBRT patients, such as distant metastasis. Development of a radiomics biomarker that can identify patients at high‐risk of recurrence could facilitate personalization of their treatment regimen for an optimized clinical outcome.R.M. had consulting interest with Amgen (ended in 2015).
SU‐F‐R‐54: CT‐Texture Based Early Tumor Treatment Response Assessment During Radiation Therapy Delivery: Small Cell Versus Non‐Small Cell Lung Cancers
Authors: Paul J , Gore E , Li X 
Published: July 2016

Purpose:Tumor treatment response may potentially be assessed during radiation therapy (RT) by analyzing changes in CT‐textures. We investigated the different early RT‐responses between small cell (SCLC) and non‐small cell lung cancer (NSCLC) as assessed by CT‐texture.Methods:Daily diagnostic‐quality CT acquired during routine CT‐guided RT using a CT‐on‐Rails for 13‐NSCLC and 5‐SCLC patients were analyzed. These patient had ages ranging from 45–78 and 38–63 years, respectively, for NSCLC and SCLC groups, and tumor‐stages ranging from T2‐T4, and were treated with either RT or chemotherapy and RT with 45–66Gy/ 20–34 fractions. Gross‐tumor volume (GTV) contour was generated on each daily CT by populating GTV contour from simulation to daily CTs with manual editing if necessary. CT‐texture parameters, such as Hounsfield Unit (HU) histogram, mean HU, skewness, kurtosis, entropy, and short‐run high‐gray level emphasis (SRHGLE), were calculated in GTV from each daily CT‐set using an in house software tool. Difference in changes of these texture parameters during RT between NSCLC and SCLC was analyzed and compared with GTV volume changes.Results:Radiation‐induced changes in CT‐texture were different between SCLC and NSCLC. Average changes from first to the last fractions for NSCLC and SCLC in GTV were 28±10(12–44) and 30±15(11–47) HU (mean HU reduction), 12.7% and 18.3% (entropy), 50% and 55% (SRHGLE), 19% and 22% (kurtosis), and 5.2% and 22% (skewness), respectively. Good correlation in kurtosis changes and GTV was seen (R2=0.8923) for SCLC, but not for NSCLC (R2=0.4748). SCLC had better correlations between GTV volume reduction and entropy (SCLC R2=0.847; NSCLC R2=0.6485), skewness (SCLC R2=0.935; NSCLC R2=0.7666), or SRHGLE (SCLC R2=0.9619; NSCLC R2=0.787).Conclusion:NSCLC and SCLC exhibited different early RT‐responses as assessed by CT‐texture changes during RT‐delivery. The observed larger changes in various CT‐texture parameters for SCLC indicate that SCLC may respond to RT more rapid than NSCLC.
SU‐F‐I‐07: CBCT Denoising Based On Adaptive Dictionary Learning Algorithms
Authors: Qin Shaohua , Yin Juan , Li Hongsheng , Chen Jinhu , Yin Yong , Li Dengwang 
Published: July 2016

Purpose:We proposed a new dictionary learning algorithm (AK‐SVD) based on K‐SVD. AK‐SVD can denoise the CBCT image, and did not need the noise information as prior knowledge.Methods:The AK‐SVD had two steps: signal sparse representation, and then dictionary optimization. The CBCT image was sparse, and there were limited big coefficients. The other coefficients were zero or near zero. In the sparse representation step of traditional K‐SVD, the noise variance was used as a threshold to select the big representation coefficients. This increased the complexity of the algorithm. The denoising result also was affected by the accuracy of the noise variance estimation, especially in non‐Gaussian noise. In AK‐SVD we used the average of the existing big coefficients as a threshold. The new found coefficient was compared with the threshold. If it was bigger than this threshold, it will be determined as the big coefficient, and be added to the set of existing big coefficients. The finding process continued. If it was smaller than this threshold, the finding process was end.This threshold was not related to the noise variance, and based on this method we improved the traditional K‐SVD.Results:In the synthetic experiments about designing dictionary from synthetic signals, the correct rate of dictionary learning by the AK‐SVD was similar with the ideal results of the K‐SVD where the noise variance was known. However, the AK‐SVD algorithm did not need to evaluate the noise variance, so it had lower computational complexity and wider adaptability. In the denoising experiment about the CBCT image corrupted by the non‐Gaussian noise, AK‐SVD has an advantage in terms of texture.Conclusion:The AK‐SVD can work well with the noise variance unknown, and it had lower computational complexity and wider adaptability than K‐SVD.This work was jointly supported by National Natural Science Foundation of China (61471226), Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201516), China Postdoctoral Science Foundation (2015T80739, 2014M551949), and research funding from Jinan (201401221).