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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Investigating the role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 assessment of clinically significant peripheral zone prostate lesions as defined at radical prostatectomy

Purpose: PI-RADS v2 dictates that dynamic contrast-enhanced (DCE) imaging be used to further classify peripheral zone (PZ) cases that receive a diffusion-weighted imaging equivocal score of three (DWI3), a positive DCE resulting in an increase in overall assessment score to a four, indicative of clinically significant prostate cancer (csPCa). However, the accuracy of DCE in predicting csPCa in DWI3 PZ cases is unknown. This study sought to determine the frequency with which DCE changes the PI-RADS v2 DWI3 assessment category, and to determine the overall accuracy of DCE-MRI in equivocal PZ DWI3 lesions. Materials and Methods: This is a retrospective study of patients with pathologically proven PCa who underwent prostate mpMRI at 3T and subsequent radical prostatectomy. PI-RADS v2 assessment categories were determined by a radiologist, aware of a diagnosis of PCa, but blinded to final pathology. csPCa was defined as a Gleason score ≥ 7 or extra prostatic extension at pathology review. Performance characteristics and diagnostic accuracy of DCE in assigning a csPCa assessment in PZ lesions were calculated. Results: A total of 271 men with mean age of 59 ± 6 years mean PSA 6.7 ng/mL were included. csPCa was found in 212/271 (78.2%) cases at pathology, 209 of which were localized in the PZ. DCE was necessary to further classify (45/209) of patients who received a score of DWI3. DCE was positive in 29/45 cases, increasing the final PI-RADS v2 assessment category to a category 4, with 16/45 having a negative DCE. When compared with final pathology, DCE was correct in increasing the assessment category in 68.9% ± 7% (31/45) of DWI3 cases. Conclusion: DCE increases the accuracy of detection of csPCa in the majority of PZ lesions that receive an equivocal PI-RADS v2 assessment category using DWI.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Validation of SE-EPI-based T2 mapping for characterization of prostate cancer: a new method compared with the traditional CPMG method

Purpose: We aim to compare the results of spin echo–echo planar imaging (SE-EPI)-based T2 mapping with those of the conventional Carr–Purcell–Meiboom–Gill (CPMG) method and to investigate the potential validity of SE-EPI-T2 mapping for the characterization of prostate cancer (PCa). Methods: Our retrospective study included 42 PCa patients and 42 noncancer patients who underwent 3.0T MRI with b values ranging from 0 to 2000 s/mm{sup 2} and echo times (TEs) ranging from 32 to 100 ms before biopsies. Bland–Altman analysis was used to compare the agreement between the two methods. The correlations between CPMG-T2 values and SE-EPI-T2 values at different b values were determined by Spearman’s rho analysis or Pearson analysis. The Mann–Whitney U test and two-sample t tests were used to analyze the differences between the cancerous and noncancerous groups. Results: Substantial agreement regarding the measurements was observed between the two methods. The average correlation between the CPMG-T2 values and SE-EPI-T2 values was moderate and positive, and the best correlations were found at b = 200 s/mm{sup 2} in the noncancer group (r = 0.557, P = 0.000) and at b = 100 s/mm{sup 2} in the cancer group (r = 0.537, P = 0.000). In addition, statistically significant differences were found between the noncancer and cancer groups in T2 values and ADC values (diff TEs) (P = 0.000). Conclusions: Substantial agreement in the measurements was found between the SE-EPI method and CPMG method. SE-EPI-based T2 mapping has potential clinical value for the prostate and can be considered an alternative to the traditional CPMG-T2 mapping method.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Digestive Tract Complications of Renal Cryoablation

We report a case each of duodenorenal and colorenal fistula that arose after computed tomography-guided percutaneous cryoablation (PCA) for renal cell carcinoma and use imaging and endoscopic findings to analyze their causes and mechanisms. Both complications occurred though the edge of the iceball did not touch the intestinal wall, and patients’ symptoms and fistula formation occurred several days after the PCA procedure. Based on imaging and endoscopy findings, we suspected the colorenal fistula resulted from bowel injury caused by ischemia from the occlusion of small vessels at the procedure’s low temperature. Both cases were resolved conservatively without surgical intervention.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Ablation Volume Measurement After Percutaneous Cryoablation Using a Two-cryo-probe Technique for Small Hepatocellular Carcinomas

Purpose: To calculate the ablation volume of percutaneous cryoablation (PCA) with a two-cryo-probe technique for small hepatocellular carcinomas (HCCs) and to assess risk factors for local tumor recurrence (LTR). Materials and Methods: Between January 2013 and June 2017, 96 patients (mean age, 60.7 years; range, 37–83 years) with 106 small HCCs were retrospectively analyzed. The ablation volume, technical success, LTR rates, and complications were evaluated. Ablation volume was measured after the first freezing and immediately after completing PCA twice via computed tomography imaging. Prognostic factors associated with LTR were analyzed. Results: Technical success was achieved in all patients. The mean final ablation volume was 19.1 ± 4.8 cm{sup 3}, which was significantly higher than the first freezing ablation volume (14.7 ± 4.3 cm{sup 3}, p < 0.001). During the median follow-up period of 16.7 months (range, 5–52 months), LTR-free survival rates based on Kaplan–Meier analyses at 6 months, 1 year, and 2 years were 87.7%, 84.0%, and 80.2%, respectively. Only one major complication of post-procedural arterial bleeding occurred. A final ablation volume/tumor volume < 10 was a significant risk factor for LTR (p = 0.044). Conclusions: A ratio of final ablation volume to tumor volume < 10 was a significant prognostic factor for LTR. Therefore, the measurement and prediction of the final ablation volume are important to reduce LTR.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Automated Classification and Analysis of Non-metallic Inclusion Data Sets

The aim of this study is to utilize principal component analysis (PCA), clustering methods, and correlation analysis to condense and examine large, multivariate data sets produced from automated analysis of non-metallic inclusions. Non-metallic inclusions play a major role in defining the properties of steel and their examination has been greatly aided by automated analysis in scanning electron microscopes equipped with energy dispersive X-ray spectroscopy. The methods were applied to analyze inclusions on two sets of samples: two laboratory-scale samples and four industrial samples from a near-finished 4140 alloy steel components with varying machinability. The laboratory samples had well-defined inclusions chemistries, composed of MgO-Al{sub 2}O{sub 3}-CaO, spinel (MgO-Al{sub 2}O{sub 3}), and calcium aluminate inclusions. The industrial samples contained MnS inclusions as well as (Ca,Mn)S + calcium aluminate oxide inclusions. PCA could be used to reduce inclusion chemistry variables to a 2D plot, which revealed inclusion chemistry groupings in the samples. Clustering methods were used to automatically classify inclusion chemistry measurements into groups, i.e., no user-defined rules were required.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Characterization of a non-phosphotransferase system for cis,cis-muconic acid production in Corynebacterium glutamicum

Highlights: • Engineered C. glutamicum produced CCM, valued for plastic and polymer production. • The CCM yield from glucose in the optimized strain was about 22% (mol/mol). • CCM productivity was enhanced by redirecting carbon flux in non-PTS C. glutamicum. Cis,cis-muconic acid (CCM) is a biochemical material that can be used for the production of various plastics and polymers and is particularly gaining attention as an adipic acid precursor for the synthesis of nylon-6,6. In the current study, the production of CCM was first attempted by introducing a newly developed protocatechuate (PCA) decarboxylase from Corynebacterium glutamicum 13032 to inha103, which completed the biosynthetic pathway therein. To improve CCM productivity, a phosphoenol pyruvate (PEP)-dependent phosphotransferase system (PTS) that consumed the existing glucose was developed, in the form of a strain with a non-PTS that did not consume PEP. To improve glucose uptake, we developed P25 strain, in which iolR (a transcriptional regulator gene) was additionally deleted. Strain P28, a P25 derivative expressing PCA decarboxylase, produced 4.01 g/L of CCM, which was 14% more than that produced by the parental strain. Moreover, strains P29 and P30, with an active pentose phosphate pathway and overexpressing important genes (qsuB) in the metabolic pathway, produced 4.36 and 4.5 g/L of CCM, respectively. Particularly, the yield per glucose in strain P30 was similar to that of the fed-batch culture of Escherichia coli, which has the highest reported yield of 22% (mol/mol). These results are underpinned by the characteristics of the non-PTS with increased PEP availability and a strain with deletion of the iolR gene, which greatly increased glucose uptake.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
DOE OSTI2018

State space parameterization of explosive eigenvalues during autoignition

Explosive modes such as ignition and extinction are characterized by an eigenvalue of the chemical Jacobian matrix with positive real part, representing the transient instability of chain-branching chemistry and thermal feedback. Formation and eigen-decomposition of the Jacobian matrix are expensive operations whose cost increases cubically with chemical mechanism size. As an alternative to directly computing the eigenvalues of the Jacobian, we explore principal component analysis (PCA) along with nonlinear regression as a methodology to parameterize the eigenvalues by state variables (or linear combinations thereof). We evaluate this modeling strategy using homogeneous autoignition data on two different applications: pseudotransient continuation (Ψtc)-based ODE solvers and chemical explosive mode analysis (CEMA). Results indicate that the PCA-based parameterization of the eigenvalues appears feasible for Ψtc solvers in autoignition calculations over a range of temperatures and pressures. Our results also show that eigenvalue models are capable of tracking sharp discontinuities (such as ignition or extinction) in the eigenvalue for computational flame diagnostics such as CEMA.

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

Dynamic analysis environment for nuclear forensic analyses

We report a Dynamic Analysis Environment (DAE) software package is introduced to facilitate group inclusion/exclusion method testing, evaluation and comparison for pre-detonation nuclear forensics applications. Employing DAE, the multivariate signatures of a questioned material can be compared to the signatures for different, known groups, enabling the linking of the questioned material to its potential process, location, or fabrication facility. Advantages of using DAE for group inclusion/exclusion include built-in query tools for retrieving data of interest from a database, the recording and documentation of all analysis steps, a clear visualization of the analysis steps intelligible to a non-expert, and the ability to integrate analysis tools developed in different programming languages. Two group inclusion/exclusion methods are implemented in DAE: principal component analysis, a parametric feature extraction method, and k nearest neighbors, a nonparametric pattern recognition method. Spent Fuel Isotopic Composition (SFCOMPO), an open source international database of isotopic compositions for spent nuclear fuels (SNF) from 14 reactors, is used to construct PCA and KNN models for known reactor groups, and 20 simulated SNF samples are utilized in evaluating the performance of these group inclusion/exclusion models. For all 20 simulated samples, PCA in conjunction with the Q statistic correctly excludes a large percentage of reactor groups and correctly includes the true reactor of origination. As a result, employing KNN, 14 of the 20 simulated samples are classified to their true reactor of origination.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005–2012

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. To discover obesity-related mixed chemical exposure patterns in the general U.S. population. Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adsorption and intracellular uptake of mercuric mercury and methylmercury by methanotrophs and methylating bacteria

The cell surface adsorption and intracellular uptake of mercuric mercury Hg(II) and methylmercury (MeHg) are important in determining the fate and transformation of Hg in the environment. However, current information is limited about their interactions with two important groups of microorganisms, i.e., methanotrophs and Hg(II)-methylating bacteria, in aquatic systems. This study investigated the adsorption and uptake dynamics of Hg(II) and MeHg by three strains of methanotrophs, Methylomonas sp. strain EFPC3, Methylosinus trichosporium OB3b, and Methylococcus capsulatus Bath, and two Hg(II)-methylating bacteria, Pseudodesulfovibrio mercurii ND132 and Geobacter sulfurreducens PCA. Distinctive behaviors of these microorganisms towards Hg(II) and MeHg adsorption and intracellular uptake were observed. The methanotrophs took up 55–80% of inorganic Hg(II) inside cells after 24 h incubation, lower than methylating bacteria (>90%). Approximately 80–95% of MeHg was rapidly taken up by all the tested methanotrophs within 24 h. In contrast, after the same time, G. sulfurreducens PCA adsorbed 70% but took up <20% of MeHg, while P. mercurii ND132 adsorbed <20% but took up negligible amounts of MeHg. These results suggest that microbial surface adsorption and intracellular uptake of Hg(II) and MeHg depend on the specific types of microbes and appear to be related to microbial physiology that requires further detailed investigation. Despite being incapable of methylating Hg(II), methanotrophs play important roles in immobilizing both Hg(II) and MeHg, potentially influencing their bioavailability and trophic transfer. Furthermore, methanotrophs are not only important sinks for methane but also for Hg(II) and MeHg and can influence the global cycling of C and Hg.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Distribution of rare earth elements and other critical elements in beneficiated Pennsylvania anthracites

The Pennsylvania Anthracite Fields are in a complex tectonic and metamorphic terrain, historically hosting one of the largest concentrations of coal mining in the USA. Anthracite mining now largely consists of the surface mining of the pillars remaining from the prime years of underground mining. The geochemical study of the sized coal products and the refuse (largely rock) from three preparation plants (breakers) demonstrates that Principal components analysis (PCA) of select major oxide, minor element, and rare earth elements illustrates some differentiation among the products from the individual plants. The rock samples, with abundant quartz and metamorphic Al-Si minerals and with a lower ash-basis REE concentration than the coals, were distinctly separated from the coal samples on the PCA plots. Plots of Gd N /Gd N * vs. Eu N /Eu N * and Ce N /Ce N * vs. Eu N / Eu N * showed differentiation between the individual suites of coals showed that the refuse samples had distinct REE distributions compared to the associated coals. Further, several minor and trace elements show enrichments in the coal samples. Lithium, with concentrations of up to 314 ppm on an ash basis, is among the most promising of the critical elements, exceeding the enrichment of the REY and Sc.

58 GEOSCIENCES↗
DOE OSTI2020

Isotope Exchange between Mercuric [Hg(II)] Chloride and Hg(II) Bound to Minerals and Thiolate Ligands: Implications for Enriched Isotope Tracer Studies

Enriched mercury (Hg) stable isotopes have been widely used as tracers in field and laboratory investigations of Hg(II) biogeochemical transformations such as methylation and demethylation. Few studies, however, have considered concurrent isotope exchange reactions between newly spiked and pre-existing Hg(II) in environmental matrices, which may alter redistribution and thus transformation of the spiked and pre-existing Hg(II). Using enriched 198 Hg [as mercuric Hg(II) or HgCl n species], this study investigated isotope exchange between 198 Hg and pre-existing Hg(II) bound to metacinnabar (β-HgS), sediments, low-molecular-weight (LMW) thiols, and dissolved organic matter (DOM). The impact of isotope exchange on methylmercury production in the presence of organic ligands was also evaluated with an iron-reducing bacterium Geobacter sulfurreducens PCA in a phosphate buffered solution (pH 7.4). Here, we found that spiked 198 Hg readily exchanged with mineral-bound ambient Hg(II) despite concurrent Hg(II) adsorption and immobilization on the solids. Rapid exchange (<3 min) was also observed between spiked 198 Hg and 200 Hg pre-equilibrated with LMW thiols and DOM in solution. While the exchange did not cause net changes in Hg(II) chemical speciation, it resulted in redistribution of Hg(II) isotopes bound to the ligands and thus an apparently similar methylation rate and magnitude of the spiked 198 Hg and pre-existing 200 Hg by PCA cells when 198 Hg and 200 Hg were added at 1:1 ratio. These observations underscore the importance of isotope exchange when an enriched Hg isotope is applied in environmental matrices, as the exchange could potentially lead to biased rate calculations of Hg(II) transformation and bioaccumulation and thus risk assessments of new Hg(II) input to the natural ecosystems.

58 GEOSCIENCES↗
DOE OSTI2022

Using real-time data analysis to conduct next-generation synchrotron fatigue studies

Next-generation experimental techniques, like high energy X-ray diffraction microscopy (HEDM), usher in new opportunities to collect the grain-scale data necessary for understanding the evolving processes that drive fatigue failure. In this study, we present a framework for monitoring the evolution of a deforming polycrystal, in real-time, by applying principal component analysis (PCA) to raw X-ray diffraction image data. We applied this framework to inform in-situ HEDM measurements of a cyclically loaded Inconel-718 superalloy. Further, we discovered correlations between PCA of the diffraction data and the physical processes in the polycrystal. Lastly, we discuss extending this framework in future HEDM fatigue studies.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Patterns and Predictors of Early Biochemical Recurrence After Radical Prostatectomy and Adjuvant Radiation Therapy in Men With pT{sub 3}N{sub 0} Prostate Cancer: Implications for Multimodal Therapies

Purpose: The aim of our study was to evaluate patterns and predictors of early biochemical recurrence (eBCR) after radical prostatectomy (RP) and adjuvant radiation therapy (aRT) in order to identify which individuals might benefit from additional treatments. Methods and Materials: We evaluated 390 patients with pT{sub 3}N{sub 0} prostate cancer (PCa) receiving RP and aRT at 6 European centers between 1993 and 2006. Patients who were free from BCR at <2 years' follow-up were excluded. This resulted in 374 assessable patients. Early BCR was defined as 2 consecutive prostate-specific antigen (PSA) test values >0.2 ng/mL within 2 or 3 years after aRT. Uni- and multivariable Cox regression analyses predicting overall and eBCR after aRT were fitted. Covariates consisted of preoperative PSA results, surgical margins, pathological stage, Gleason score, and aRT dose. Results: Overall, 5- and 8-year BCR-free survival rates were 77.1% and 70.8%, respectively. At a median follow-up of 86 months after aRT, 33 (8.8%) and 55 (14.6%) men experienced BCR within 2 or 3 years after aRT, respectively. In multivariable analyses, Gleason scores of 8 to 10 represented the only independent predictor of eBCR after aRT (all, P≤.01). The risk of BCR was significantly higher in patients with a Gleason score of 8 to 10 disease than in those with Gleason 2 to 6 within 24 months after treatment, after adjusting for all covariates (all, P≤.04). However, given a 24-month BCR free period, the risk of subsequent BCR for men with poorly differentiated disease was equal to that of men with less aggressive disease (all, P≥.3). Conclusions: High Gleason score represents the only predictor of eBCR after RP and aRT in patients affected by pT{sub 3}N{sub 0} PCa. Given the association between early PSA recurrence, clinical progression, and mortality, these patients might be considered candidates for adjuvant medical therapy and/or prophylactic whole-pelvis radiation therapy in addition to aRT, delivered to the prostatic bed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗