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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Multi-institutional Pooled Analysis on Adjuvant Chemoradiation in Pancreatic Cancer

Purpose: To determine the impact of chemoradiation therapy (CRT) on overall survival (OS) after resection of pancreatic adenocarcinoma. Methods and Materials: A multicenter retrospective review of 955 consecutive patients who underwent complete resection with macroscopically negative margins (R0-1) for invasive carcinoma (T1-4; N0-1; M0) of the pancreas was performed. Exclusion criteria included metastatic or unresectable disease at surgery, macroscopic residual disease (R2), treatment with intraoperative radiation therapy (IORT), and a histological diagnosis of no ductal carcinoma, or postoperative death (within 60 days of surgery). In all, 623 patients received postoperative radiation therapy (RT), 575 patients received concurrent chemotherapy (CT), and 462 patients received adjuvant CT. Results: Median follow-up was 21.0 months. Median OS after adjuvant CRT was 39.9 versus 24.8 months after no adjuvant CRT (P<.001) and 27.8 months after CT alone (P<.001). Five-year OS was 41.2% versus 24.8% with and without postoperative CRT, respectively. The positive impact of CRT was confirmed by multivariate analysis (hazard ratio [HR] = 0.72; confidence interval [CI], 0.60-0.87; P=.001). Adverse prognostic factors identified by multivariate analysis included the following: R1 resection (HR = 1.17; CI = 1.07-1.28; P<.001), higher pT stage (HR = 1.23; CI = 1.11-1.37; P<.001), positive lymph nodes (HR = 1.27; CI = 1.15-1.41; P<.001), and tumor diameter >20 mm (HR = 1.14; CI = 1.05-1.23; P=.002). Multivariate analysis also showed a better prognosis in patients treated in centers with >10 pancreatic resections per year (HR = 0.87; CI = 0.78-0.97; P=.014) Conclusion: This study represents the largest comparative study on adjuvant therapy in patients after resection of carcinoma of the pancreas. Overall survival was better in patients who received adjuvant CRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Intraoperative Radiotherapy for Pancreatic Cancer: 30-Year Experience in a Single Institution in Japan

Purpose: To analyze retrospectively the results of intraoperative radiotherapy (IORT) with or without external beam radiotherapy ({+-} EBRT) for localized pancreatic cancer in the past three decades and to analyze prognostic factors by multivariate analysis. Methods and Materials: Records for 322 patients with pancreatic cancer treated by IORT {+-} EBRT in Tohoku University Hospital between 1980 and 2009 were reviewed. One hundred ninety-two patients who had no distant organ metastases or dissemination at the time of laparotomy were enrolled in the present study. Results: Eighty-three patients underwent gross total resection (R0: 48 patients, R1: 35 patients), and 109 patients underwent only biopsy or palliative resection. Fifty-five patients underwent adjuvant EBRT, and 124 underwent adjuvant chemotherapy. The median doses of IORT and EBRT were 25 and 40 Gy, respectively. The median follow-up period was 37.5 months. At the time of the analysis, 166 patients had disease recurrence, and 35 patients had local failure. The 2-year local control (LC) and overall survival (OS) rates were 71.0% and 16.9%, respectively. Comparison of the results for each decade showed that OS was significantly improved decade by decade (2-year: 25.0% vs. 18.8% vs. 4.2%, p < 0.001). Multivariate analysis showed that degree of resection (R0-1 vs. R2, hazard ratio = 1.97, p = 0.001) and adjuvant chemotherapy (yes vs. no, hazard ratio = 1.54, p = 0.028) had significant impacts on OS. Late gastrointestinal morbidity of Common Terminology Criteria for Adverse Events version 3.0 grade 4 or 5 was observed in four patients. Conclusion: Excellent local control for pancreatic cancer with few cases of severe late toxicity was achieved by using IORT. OS of patients with pancreatic cancer treated by IORT {+-} EBRT improved significantly decade by decade. Multivariate analysis showed that degree of resection and adjuvant chemotherapy had significant impacts on OS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Ten-Year Locoregional Recurrence Risks in Women With Nodal Micrometastatic Breast Cancer Staged With Axillary Dissection

Purpose: To compare the locoregional recurrence (LRR) rates in patients with nodal mirometastases (pNmic) with those in patients with node-negative (pN0) and macroscopic node-positive (pNmac) breast cancer; and to evaluate the LRR rates according to locoregional treatment of pNmic disease. Methods and Materials: The subjects were 9,616 women diagnosed between 1989 and 1999 with Stage pT1-T2, pN0, pNmic, or pNmac, M0 breast cancer. All women had undergone axillary dissection. The Kaplan-Meier local recurrence, regional recurrence, and LRR rates were compared among those with pN0 (n = 7,977), pNmic (n = 490) and pNmac (n = 1,149) and according to locoregional treatment. Multivariate analysis was performed to identify the significant factors associated with LRR. Results: The median follow-up was 11 years. The 10-year Kaplan-Meier recurrence rate in the pN0, pNmic, and pNmac cohorts was 6.1%, 6.8%, and 8.7% for local recurrence; 3.1%, 6.2%, and 10.3% for regional recurrence; and 8.0%, 11.6%, and 15.2% for LRR, respectively (all p < .001). In the pNmic patients, the 10-year regional recurrence rate was 6.4% with breast-conserving surgery plus breast radiotherapy (RT), 5.4% with breast-conserving surgery plus locoregional RT, 4.6% with mastectomy alone, 11.1% with mastectomy plus chest wall RT, and 10.7% with mastectomy plus locoregional RT. In patients with pNmic disease and age <45 years, Grade 3 histologic features, lymphovascular invasion, nodal ratio >0.25, and estrogen receptor-negative disease, the 10-year LRR rates were 15-20%. On multivariate analysis of the entire cohort, pNmic was associated with greater LRR than Stage pN0 (hazard ratio [HR], 1.6; p = .002). On multivariate analysis of pNmic patients only, age <45 years was associated with significantly greater LRR (HR, 1.9; p = .03), and trends for greater LRR were observed with a nodal ratio >0.25 (HR, 2.0; p = .07) and lymphovascular invasion (HR, 1.7; p = .07). Conclusion: Women with pNmic had a greater risk of LRR than those with pN0 disease. Patients with pNmic in association with young age, Grade 3 histologic features, lymphovascular invasion, nodal ratio >0.25, and estrogen receptor-negative disease experienced 10-year LRR rates of {approx}15-20%, warranting consideration of locoregional RT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Classification of Malaysia aromatic rice using multivariate statistical analysis

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Intrinsic Kinetics of Polyethylene Terephthalate Pyrolysis via Micropyrolysis and Multivariate Chromatographic Analysis

This study provides an in-depth investigation of the primary decomposition of polyethylene terephthalate (PET) via pyrolysis, employing an experimental-analytic workflow that integrates design of experiments (DoE), micropyrolysis coupled with comprehensive two-dimensional gas chromatography (GC×GC), and multivariate data analysis to verify intrinsic kinetic conditions and elucidate evolving product distributions for mapping key reaction pathways. Peaks that could not be identified using commercial spectral libraries were assigned using Mass Frontier simulations, enabling the identification of divinyl terephthalate, ethyl vinyl terephthalate, and 2-(benzoyloxy)ethyl vinyl terephthalate. A polar×polar (non-orthogonal) column set tailored for the detection of carboxylic acids enhanced the quantification of benzoic acid, 4-vinylbenzoic acid, 4-ethylbenzoic acid, and methylbenzoic acid by up to 6-fold relative to an orthogonal column combination (non-polar×mid-polar). Moreover, pyrolysis variables were systematically evaluated using a Box- Behnken design (BBD), encompassing pyrolysis temperature (500−600 °C), sample weight (50−150 μg), and carrier gas flow rate (100−300 mL min −1 ). Among these, pyrolysis temperature was the only statistically significant factor influencing product yields, ranging from 58.78 to 84.26 wt %. In contrast, neither the sample weight nor the carrier gas flow rate had a significant effect on product yields within the evaluated experimental space. At 600 °C, the major pyrolysis products were benzoic acid (up to 20.20 ± 1.46 wt %) and CO 2 (up to 21.28 ± 1.46 wt %), which can be produced through decarboxylation reactions. These findings underscore the critical importance of selecting appropriate analytical columns for the accurate quantification of heteroatomcontaining products such as carboxylic acids, which may otherwise be underestimated or undetected due to their reactivity with the stationary phase of non-polar and mid-polar columns, as well as other GC components. They also highlight the importance of selecting pyrolysis conditions for investigating the primary decomposition of PET under an isothermal kinetically limited regime.

aromatic compounds↗
NASA NTRS1995

Multivariate Quantitative Chemical Analysis

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Kinchen, David G.↗
NASA NTRS1994

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Djorgovski, S. George↗
NASA NTRS2020

Identification of Anomalous Eucrites and Diogenites by Multivariable Discriminant Analysis

Howardite, eucrite and diogenite clan (HED) is the largest magmatic achondrite group and has been suggested to be derived from asteroid (4) Vesta (e.g., [1]). Previous geochemical studies have suggested HEDs were generated by asteroid melting followed by crystallization, metamorphism, and impact [1-6]. However, numerous eucrites and diogenites have anomalous compositional, isotopic or petrological characteristics compared to the norm. This diversity might indicate more complex magma processes on the parent asteroid, or they could be linked to different parental asteroids (e.g., [5]). The former case would imply that the parent asteroid is more heterogenous than currently thought, and that our petrologic models are too simple. The latter case would increase the number of asteroids known to have formed mafic crusts, and would allow for petrological comparisons across numerous bodies. We apply multivariable discrimination analysis (MDA) to a large database of HED pyroxene analyses. This technique allows all elemental information to be considered simultaneously to facilitate identification of groupings, trends, and outliers within the dataset.

Peng, Z. X.↗
NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗
DOE OSTI2021

Fast and Non-Destructive Determination of Water Content in Ionic Liquids at Varying Temperatures by Raman Spectroscopy and Multivariate Regression Analysis

Imidazolium acetate ionic liquids (ILs) have been utilized as promising solvents in many applications that involve varying water content and temperature. These experimental variables affect the anion-cation intermolecular interactions, which in turn influence the performance of the ILs in these applications. Here, this paper shows Raman spectroscopy can be used as an operando method to measure water content in IL solvents when simultaneous temperature changes may occur. The Raman spectra of 1-alkyl-3-methylimidazolium acetate ILs (alkyl chain length n = 2, 4, 6, 8) with varying water content (from 0.028 to 0.899 water mole fraction) and temperature (from 78.1 K to 423.1 K) were measured. Increasing the water content or decreasing the temperature of the tested ILs weakens the anion-cation intermolecular interactions. The water content of these ILs can be quantified even in conditions when the temperature is changing using Raman spectroscopy combined with multivariate regression analysis, including principal component regression (PCR), partial-least-squares regression (PLSR), and artificial neural networks (ANNs). The ANN model combined with partial-least-squares (PLS) achieves the highest prediction accuracy of water content in ILs at varying temperatures (RMSECV = 0.017, R 2 CV = 99.1%, RMSEP = 0.019, R 2 P = 98.8%, RPD = 8.93). Raman spectroscopy provides a potential fast non-destructive operando method to monitor the water content of ILs even in applications when the temperature may be simultaneously altered; this information can lead to the optimized use of these ILs in many applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Chemical Attribution of Fentanyl Using Multivariate Statistical Analysis of Orthogonal Mass Spectral Data

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. Here, the results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography–tandem mass spectrometry-time of-flight (LC–MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. Finally, this work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Elemental Variation and Petrogenesis of Pyroxenes in HED Meteorites by Multivariable Discriminant Analysis Method

HED (Howardite, Eucrite and Diogenite) are meteorites with mafic and ultramafic igneous composition. Previous studies suggested HED came from asteroid (4) Vesta and they were generated by magmatic melting followed by differentiation crystallization, metamorphic, and impact. Uniform oxygen isotopic composition of HED samples favors global magma ocean for (4) Vesta. However, the petrological diversity of HED may indicate more complex magma processes in the meteorites. In general, most of the geochemical studies are using traditional methods (e.g. element to element or ratio to ratio plots) to classify the different rock type and groups. With the traditional methods, only limited elements can be shown in a figure. As such, some meteorites may have been identified as HED by a traditional method but found different either in isotopic composition or other elemental characteristic. These anomalous HED meteorites may or may not come from asteroid (4) Vesta. In fact, magma is a unit system where any elemental change should affect to all other elements as a whole, it would be reasonable to consider all elements together to look for the systematical changes. Using multivariable discriminant analysis (MDA) method is one of such testing for their geochemical variation. The method may be able to help us to better understand the petrologic processes and the relationship among elements. This study is to test the MDA method by focus mainly on pyroxene composition of eucrite and diogenite.

Peng, Z. X.↗
DOE OSTI2016

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

multivariate experimental data↗
DOE OSTI2019

A New Approach to Multivariate Network Traffic Analysis

© 2019, Springer Science+Business Media, LLC & Science Press, China. Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers.

Kim, Jinoh↗
DOE OSTI2013

Long-term Survival and Toxicity in Patients Treated With High-Dose Intensity Modulated Radiation Therapy for Localized Prostate Cancer

Purpose: To report long-term survival and toxicity outcomes with the use of high-dose intensity modulated radiation therapy (IMRT) to 86.4 Gy for patients with localized prostate cancer. Methods and Materials: Between August 1997 and December 2008, 1002 patients were treated to a dose of 86.4 Gy using a 5-7 field IMRT technique. Patients were stratified by prognostic risk group based on National Comprehensive Cancer Network risk classification criteria. A total of 587 patients (59%) were treated with neoadjuvant and concurrent androgen deprivation therapy. The median follow-up for the entire cohort was 5.5 years (range, 1-14 years). Results: For low-, intermediate-, and high-risk groups, 7-year biochemical relapse-free survival outcomes were 98.8%, 85.6%, and 67.9%, respectively (P<.001), and distant metastasis-free survival rates were 99.4%, 94.1%, and 82.0% (P<.001), respectively. On multivariate analysis, T stage (P<.001), Gleason score (P<.001), and >50% of initial biopsy positive core (P=.001) were predictive for distant mestastases. No prostate cancer-related deaths were observed in the low-risk group. The 7-year prostate cancer-specific mortality (PCSM) rates, using competing risk analysis for intermediate- and high-risk groups, were 3.3% and 8.1%, respectively (P=.008). On multivariate analysis, Gleason score (P=.004), percentage of biopsy core positivity (P=.003), and T-stage (P=.033) were predictive for PCSM. Actuarial 7-year grade 2 or higher late gastrointestinal and genitourinary toxicities were 4.4% and 21.1%, respectively. Late grade 3 gastrointestinal and genitourinary toxicity was experienced by 7 patients (0.7%) and 22 patients (2.2%), respectively. Of the 427 men with full potency at baseline, 317 men (74%) retained sexual function at time of last follow-up. Conclusions: This study represents the largest cohort of patients treated with high-dose radiation to 86.4 Gy, using IMRT for localized prostate cancer, with the longest follow-up to date. Our findings indicate that this treatment results in excellent clinical outcomes with acceptable toxicity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Patterns of Failure After Radical Cystectomy for pT3-4 Bladder Cancer: Implications for Adjuvant Radiation Therapy

Purpose: In patients with muscle-invasive bladder cancer, local-regional failure (LF) has been reported to occur in up to 20% of patients following radical cystectomy. The goals of this study were to describe patterns of LF, as well as assess factors associated with LF in a cohort of patients with pT3-4 bladder cancer. This information may have implications towards the use of adjuvant radiation therapy. Methods and Materials: Patients with pathologic T3-4 N0-1 bladder cancer were examined from an institutional radical cystectomy database. Preoperative demographics and pathologic characteristics were examined. Outcomes included overall survival and LF. Local-regional failures were defined using follow-up imaging reports and scans, and the locations of LF were characterized. Variables were tested by univariate and multivariate analysis for association with LF and overall survival. Results: A total of 334 patients had pT3-4 and N0-1 disease after radical cystectomy and bilateral pelvic lymph node dissection. Of these, 46% received perioperative chemotherapy. The median age was 71 years old, and median follow-up was 11 months. On univariate analysis, margin status, pT stage, and pN stage, were all associated with LF (P<.05), however, on multivariate analysis, only pT and pN stages were significantly associated with LF (P<.05). Three strata of risk were defined, including low-risk patients with pT3N0 disease, intermediate-risk patients with pT3N1 or pT4N0 disease, and high-risk patients with pT4N1 disease, who had a 2-year incidence of LF of 12%, 33%, and 72%, respectively. The most common sites of pelvic relapse included the external and internal iliac lymph nodes (LNs) and obturator LN regions. Notably, 34% of patients with LF had local-regional only disease at the time of recurrence. Conclusions: Patients with pT4 or N1 disease have a 2-year risk of LF that exceeds 30%. These patients may be the most likely to benefit from local adjuvant therapies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗