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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Global volcanic aerosol properties derived from emissions, 1990-2014, using CESM1(WACCM): VOLCANIC AEROSOLS DERIVED FROM EMISSIONS

Accurate representation of global stratospheric aerosol properties from volcanic and non-volcanic sulfur emissions is key to understanding the cooling effects and ozone-loss enhancements of recent volcanic activity. Attribution of climate and ozone variability to volcanic activity is of particular interest in relation to the post-2000 slowing in the apparent rate of global average temperature increases, and variable recovery of the Antarctic ozone hole. We have developed a climatology of global aerosol properties from 1990 to 2014 calculated based on volcanic and non-volcanic emissions of sulfur sources. We have complied a database of volcanic SO2 emissions and plume altitudes for eruptions between 1990 and 2014, and a new prognostic capability for simulating stratospheric sulfate aerosols in version 5 of the Whole Atmosphere Community Climate Model, a component of the Community Earth System Model. Our climatology shows remarkable agreement with ground-based lidar observations of stratospheric aerosol optical depth (SAOD), and with in situ measurements of aerosol surface area density (SAD). These properties are key parameters in calculating the radiative and chemical effects of stratospheric aerosols. Our SAOD climatology represents a significant improvement over satellite-based analyses, which ignore aerosol extinction below 15 km, a region that can contain the vast majority of stratospheric aerosol extinction at mid- and high-latitudes. Our SAD climatology significantly improves on that provided for the Chemistry-Climate Model Initiative, which misses 60% of the SAD measured in situ. Our climatology of aerosol properties is publicly available on the Earth System Grid.

Mills, Michael J.↗
DOE OSTI2015

Complex functionality with minimal computation: Promise and pitfalls of reduced‐tracer ocean biogeochemistry models

Abstract Earth System Models increasingly include ocean biogeochemistry models in order to predict changes in ocean carbon storage, hypoxia, and biological productivity under climate change. However, state‐of‐the‐art ocean biogeochemical models include many advected tracers, that significantly increase the computational resources required, forcing a trade‐off with spatial resolution. Here, we compare a state‐of‐the art model with 30 prognostic tracers (TOPAZ) with two reduced‐tracer models, one with 6 tracers (BLING), and the other with 3 tracers (miniBLING). The reduced‐tracer models employ parameterized, implicit biological functions, which nonetheless capture many of the most important processes resolved by TOPAZ. All three are embedded in the same coupled climate model. Despite the large difference in tracer number, the absence of tracers for living organic matter is shown to have a minimal impact on the transport of nutrient elements, and the three models produce similar mean annual preindustrial distributions of macronutrients, oxygen, and carbon. Significant differences do exist among the models, in particular the seasonal cycle of biomass and export production, but it does not appear that these are necessary consequences of the reduced tracer number. With increasing CO 2 , changes in dissolved oxygen and anthropogenic carbon uptake are very similar across the different models. Thus, while the reduced‐tracer models do not explicitly resolve the diversity and internal dynamics of marine ecosystems, we demonstrate that such models are applicable to a broad suite of major biogeochemical concerns, including anthropogenic change. These results are very promising for the further development and application of reduced‐tracer biogeochemical models that incorporate “sub‐ecosystem‐scale” parameterizations.

58 GEOSCIENCES↗
DOE OSTI2016

On the variability of the Priestley‐Taylor coefficient over water bodies

Abstract Deviations in the Priestley‐Taylor (PT) coefficient α PT from its accepted 1.26 value are analyzed over large lakes, reservoirs, and wetlands where stomatal or soil controls are minimal or absent. The data sets feature wide variations in water body sizes and climatic conditions. Neither surface temperature nor sensible heat flux variations alone, which proved successful in characterizing α PT variations over some crops, explain measured deviations in α PT over water. It is shown that the relative transport efficiency of turbulent heat and water vapor is key to explaining variations in α PT over water surfaces, thereby offering a new perspective over the concept of minimal advection or entrainment introduced by PT. Methods that allow the determination of α PT based on low‐frequency sampling (i.e., 0.1 Hz) are then developed and tested, which are usable with standard meteorological sensors that filter some but not all turbulent fluctuations. Using approximations to the Gram determinant inequality, the relative transport efficiency is derived as a function of the correlation coefficient between temperature and water vapor concentration fluctuations ( R Tq ). The proposed approach reasonably explains the measured deviations from the conventional α PT = 1.26 value even when R Tq is determined from air temperature and water vapor concentration time series that are Gaussian‐filtered and subsampled to a cutoff frequency of 0.1 Hz. Because over water bodies, R Tq deviations from unity are often associated with advection and/or entrainment, linkages between α PT and R Tq offer both a diagnostic approach to assess their significance and a prognostic approach to correct the 1.26 value when using routine meteorological measurements of temperature and humidity.

Assouline, Shmuel↗
DOE OSTI2016

Vertical overlap of probability density functions of cloud and precipitation hydrometeors: CLOUD AND PRECIPITATION PDF OVERLAP

Coarse-resolution climate models increasingly rely on probability density functions (PDFs) to represent subgrid-scale variability of prognostic variables. While PDFs characterize the horizontal variability, a separate treatment is needed to account for the vertical structure of clouds and precipitation. When sub-columns are drawn from these PDFs for microphysics or radiation parameterizations, appropriate vertical correlations must be enforced via PDF overlap specifications. This study evaluates the representation of PDF overlap in the Subgrid Importance Latin Hypercube Sampler (SILHS) employed in the assumed PDF turbulence and cloud scheme called the Cloud Layers Unified By Binormals (CLUBB). PDF overlap in CLUBB-SILHS simulations of continental and tropical oceanic deep convection is compared with overlap of PDF of various microphysics variables in cloud-resolving model (CRM) simulations of the same cases that explicitly predict the 3D structure of cloud and precipitation fields. CRM results show that PDF overlap varies significantly between different hydrometeor types, as well as between PDFs of mass and number mixing ratios for each species, - a distinction that the current SILHS implementation does not make. In CRM simulations that explicitly resolve cloud and precipitation structures, faster falling species, such as rain and graupel, exhibit significantly higher coherence in their vertical distributions than slow falling cloud liquid and ice. These results suggest that to improve the overlap treatment in the sub-column generator, the PDF correlations need to depend on hydrometeor properties, such as fall speeds, in addition to the currently implemented dependency on the turbulent convective length scale.

58 GEOSCIENCES↗
DOE OSTI2017

Radiative and Chemical Response to Interactive Stratospheric Sulfate Aerosols in Fully Coupled CESM1(WACCM)

We present a new version of the Community Earth System Model, version 1 (CESM1) with the Whole Atmosphere Community Climate Model (WACCM) featuring numerous improvements that are unique among earth system models. Improved horizontal resolution, dynamics, and chemistry now provide the development of an internally generated quasi-biennial oscillation, and significant improvements to temperatures and ozone throughout the stratosphere. The prognostic treatment of stratospheric sulfate aerosols is shown to represent well the evolution of stratospheric aerosol optical depth and perturbations to solar and longwave radiation following volcanic eruptions. We identify the inclusion of interactive OH chemistry as crucial to the study of aerosol formation following large inputs of SO 2 to the stratosphere. We show that depletion of OH levels within the dense SO 2 cloud in the first weeks following the June 1991 eruption of Mt. Pinatubo significantly prolonged the e-folding decay time for SO 2 oxidation to 47 days. Previous observational and model studies showing a 30-day decay time have not accounted for the large initial losses of SO 2 on ash and ice in the first 7-9 days following the eruption, and have not correctly accounted for OH depletion. The completeness of the chemistry, dynamics, and aerosol microphysics in WACCM uniquely qualify it for studies of stratospheric sulfate aerosol geoengineering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Effects of a Simple Convective Organization Scheme in a Two‐Plume GCM

Abstract A set of experiments is described with the Community Atmosphere Model (CAM5) using a two‐plume convection scheme. To represent the differences of organized convection from General Circulation Model (GCM) assumptions of isolated plumes in uniform environments, a dimensionless prognostic “organization” tracer Ω is invoked to lend the second plume a buoyancy advantage relative to the first, as described in Mapes and Neale (2016). When low‐entrainment plumes are unconditionally available (Ω = 1 everywhere), deep convection occurs too easily, with consequences including premature (upstream) rainfall in inflows to the deep tropics, excessive convective versus large‐scale rainfall, poor relationships to the vapor field, stable bias in the mean state, weak and poor tropical variability, and midday peak in diurnal rainfall over land. Some of these are shown to also be characteristic of CAM4 with its separated deep and shallow convection schemes. When low‐entrainment plumes are forbidden by setting Ω = 0 everywhere, some opposite problems can be discerned. In between those extreme cases, an interactive Ω driven by the evaporation of precipitation acts as a local positive feedback loop, concentrating deep convection: In areas of little recent rain, only highly entraining plumes can occur, unfavorable for rain production. This tunable mechanism steadily increases precipitation variance in both space and time, as illustrated here with maps, time‐longitude series, and spectra, while avoiding some mean state biases as illustrated with process‐oriented diagnostics such as conserved variable profiles and vapor‐binned precipitation curves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
DOE OSTI2024

Pan‐Cancer Survival Impact of Immune Checkpoint Inhibitors in a National Healthcare System

ABSTRACT Background The cumulative, health system‐wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real‐world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system‐wide survival impact of ICIs. Methods We identified all patients receiving PD‐1/PD‐L1 or CTLA‐4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non‐ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer‐related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life‐years gained system‐wide were calculated from the difference in adjusted 5‐year restricted mean survival times. Results There were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54–0.58,p < 0.001; urothelial: aHR 0.91, 95% CI 0.83–1.01,p = 0.066). The relative benefit of ICI was stable across patient age, comorbidity, and self‐reported race subgroups. Across VHA, 15,859 life‐years gained were attributable to ICI within 5‐years of treatment, with NSCLC contributing the most life‐years gained. Conclusion We demonstrated substantial increase in survival due to ICIs across a national health system, including in patient subgroups poorly represented on clinical trials.

Oncology↗
DOE OSTI2024

Methodology for Good Machine Learning with Multi‐Omics Data

In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4‐year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio‐genomics‐based prognostic and predictive factors for HR+/HER− metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi‐omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as “maintaining effective communication” and “following good data science practices,” followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.

Pharmacology & Pharmacy↗
DOE OSTI2013

Improving Bulk Microphysics Parameterizations in Simulations of Aerosol Effects

To improve the microphysical parameterizations for simulations of the aerosol indirect effect (AIE) in regional and global climate models, a double-moment bulk microphysical scheme presently implemented in the Weather Research and Forecasting (WRF) model is modified and the results are compared against atmospheric observations and simulations produced by a spectral bin microphysical scheme (SBM). Rather than using prescribed aerosols as in the original bulk scheme (Bulk-OR), a prognostic doublemoment aerosol representation is introduced to predict both the aerosol number concentration and mass mixing ratio (Bulk-2M). The impacts of the parameterizations of diffusional growth and autoconversion and the selection of the embryonic raindrop radius on the performance of the bulk microphysical scheme are also evaluated. Sensitivity modeling experiments are performed for two distinct cloud regimes, maritime warm stratocumulus clouds (SC) over southeast Pacific Ocean from the VOCALS project and continental deep convective clouds (DCC) in the southeast of China from the Department of Energy/ARM Mobile Facility (DOE/AMF) - China field campaign. The results from Bulk-2M exhibit a much better agreement in the cloud number concentration and effective droplet radius in both the SC and DCC cases with those from SBM and field measurements than those from Bulk-OR. In the SC case particularly, Bulk-2M reproduces the observed drizzle precipitation, which is largely inhibited in Bulk-OR. Bulk-2M predicts enhanced precipitation and invigorated convection with increased aerosol loading in the DCC case, consistent with the SBM simulation, while Bulk-OR predicts the opposite behaviors. Sensitivity experiments using four different types of autoconversion schemes reveal that the autoconversion parameterization is crucial in determining the raindrop number, mass concentration, and drizzle formation for warm 2 stratocumulus clouds. An embryonic raindrop size of 40 μm is determined as a more realistic setting in the autoconversion parameterization. The saturation adjustment employed in calculating condensation/evaporation in the bulk scheme is identified as the main factor responsible for the large discrepancies in predicting cloud water in the SC case, suggesting that an explicit calculation of diffusion growth with predicted supersaturation is necessary for further improvements of the bulk microphysics scheme. Lastly, a larger rain evaporation rate below cloud is found in the bulk scheme in comparison to the SBM simulation, which could contribute to a lower surface precipitation in the bulk scheme.

aerosol↗
DOE OSTI2020

Validation of Stage N3 of the Eighth Edition AJCC Staging System for Nasopharyngeal Carcinoma

Objectives To validate stage nodal (N)3 of the 8th edition American Joint Committee on Cancer (AJCC) staging system for nasopharyngeal carcinoma (NPC). Methods This retrospective cohort study extracted NPC patients from the Surveillance, Epidemiology, and End Results database between 2004 and 2016. Pathologically confirmed patients with complete data of level IV, N3a, and N3b lymph node metastasis were investigated. The included patients were divided into level IV, N3a, and N3b groups. Five‐year overall survival (OS) and cancer‐specific survival (CSS) were compared among the three groups. Results A total of 693 patients were included: 285 (41.13%) patients in the level IV group, 124 (17.89%) patients in the N3a group, and 284 (40.98%) patients in the N3b group. The 5‐year OS (57.1%, 55.0%, and 55.2%) and CSS (64.4%, 63.5%, and 64.4%) were not different among the level IV, N3a, and N3b groups. Multivariate regression analysis revealed that N stage was not an independent prognostic factor for OS (hazard ratio [HR] = 1.03, 95% confidence interval [CI]: 0.91–1.17; P = .65) or CSS (HR = 1.03, 95% CI: 0.89–1.19; P = .70). Conclusion Stage N3 of the 8th edition AJCC staging system for NPC is reasonable. Level of Evidence III Laryngoscope , 131:535–540, 2021

Pan, Xin‐Bin↗
DOE OSTI2018

Teleconnection between mean sea level pressure in the North Atlantic for September, the AMO phase and mean temperature in Central Europe for December (1896–2015)

According to the present study, an atmospheric teleconnection is likely to exist between mean sea level pressure (MSLP) in the North Atlantic at early autumn (September) and temperature in Central Europe at early winter (December) based on the last 120 years. Atlantic Multidecadal Oscillation (AMO) might play an important role in this teleconnection, determining its direction. During a positive AMO phase, a North Atlantic MSLP above average in September is frequently followed by a colder-than-average December in Central Europe (in 80% of cases) whereas a warmer-than-average December is often observed in Central Europe after an under-average MSLP in the North Atlantic in September. According to the present investigation, this is caused by a strong tendency for blocking patterns over Northern Europe (mostly over the British Isles and Scandinavia) in the last month of the year, in the first case, and a weak tendency for that, in the second case. During a negative AMO phase, this connection is less certain. In the strong negative AMO phase between 1903 and 1925, the connection became reversed compared with that observed during the positive AMO phase. However, it was significantly strong. Within the much weaker negative AMO phase between 1963 and 1994, the connection was weaker, but rather similar to that observed during positive AMO phases. Since the revealed atmospheric teleconnection is characterized by a three-month delay, it possesses a prognostic value and can be used to predict the probability of temperature anomaly.

Meteorology & Atmospheric Sciences↗
DOE OSTI2012

The effect of time steps and time-scales on parametrization suites

The problem of intense, truncation-scale storms that form in high-resolution versions of the Community Atmosphere Model Version 4 (CAM4) is studied. These storms are characterized by extreme vertical motion and heavy precipitation. This problem arises when some individual parametrizations do not produce an atmospheric-like state because they are restrained by the time-scales assumed in their formulation; other unconstrained parametrizations that follow then work in unintended ways. The behaviour of the moist parametrization components is examined in CAM4 for one typical, strong cell. At T340 spectral truncation with a 5 min time step, the deep and shallow convection parametrizations do not remove instabilities and supersaturation because they have time-scales of 1 h and 30 min, respectively. Then the prognostic cloud-water scheme, which is not constrained by a time-scale, does remove supersaturation. That local release of latent heat drives very strong vertical motion and horizontal convergence, which transports even more water vapour into the column, exacerbating the problem. Two simple model problems are introduced that illustrate the ramifications of the time-scale and time-step mismatch. When either the time-scales are shortened or the time step is lengthened, the convection parametrizations are more active and strong storms do not form. Copyright © 2012 Royal Meteorological Society

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

A two-fluid single-column model of turbulent shallow convection. Part II: Single-column model formulation and numerics

The two-fluid single-column model of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) is extended to include moisture and horizontal wind shear. Turbulent kinetic energy is introduced as a prognostic variable, dependence on a diagnosed boundary-layer height is removed, and subfilter fluxes are approximated using a two-fluid version of a Mellor–Yamada scheme. Three mechanisms for entrainment and detrainment processes are introduced, which represent entrainment of unstable air at the surface, forced detrainment of air at the top of the boundary/cloud layers, and turbulent mixing that relaxes the convective fluid to a reference profile. A semi-implicit Eulerian discretization replaces the semi-implicit semi-Lagrangian implementation of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) to improve numerical stability and conservation. The equations for the implicit time step are solved using a quasi-Newton method, which is shown to perform well in numerical tests for conservation and convergence. The two-fluid single-column model presented in this article will be applied to simulations of shallow cumulus convection in Part III.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fibrotic activity quantified in serum by measurements of type III collagen pro-peptides can be used for prognosis across different solid tumor types

Due to activation of fibroblast into cancer-associated fibroblasts, there is often an increased deposition of extracellular matrix and fibrillar collagens, e.g. type III collagen, in the tumor microenvironment (TME) that leads to tumor fibrosis (desmoplasia). Tumor fibrosis is closely associated with treatment response and poor prognosis for patients with solid tumors. To assure that the best possible treatment option is provided for patients, there is medical need for identifying patients with high (or low) fibrotic activity in the TME. Measuring unique collagen fragments such as the pro-peptides released into the bloodstream during fibrillar collagen deposition in the TME can provide a non-invasive measure of the fibrotic activity. Based on data from 8 previously published cohorts, this review provides insight into the prognostic value of quantifying tumor fibrosis by measuring the pro-peptide of type III collagen in serum of a total of 1692 patients with different solid tumor types and discusses the importance of tumor fibrosis for understanding prognosis and for potentially guiding future drug development efforts that aim at overcoming the poor outcome associated with a fibrotic TME.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Imaging in the pre-operative staging of ovarian cancer

The main prognostic factor in ovarian cancer is the stage of disease at diagnosis. The staging system in use (FIGO classification, updated in 2014) is based on the surgical-pathological findings. Although surgical staging is the gold standard in ovarian cancer, the initial patient management depends on the imaging-based pre-surgical staging assessment, in order to identify unresectable or difficult to resect disease. Radiologists need to be aware of the strengths of the available imaging modalities, as well as the imaging pitfalls. Clear understanding of pattern of disease spread and review areas are critical for accurate staging and treatment planning. The current standard of care for pre-surgical staging is CT of the thorax, abdomen, and pelvis. This allows a rapid evaluation of disease extent and is fairly accurate in identifying bulky disease but has definite limitations in assessing the extent of small volume disease and in the confirmation of certain sites of disease beyond the abdomen. Functional MRI has been reported to be superior in detecting small peritoneal deposits. PET/CT may be used as a problem-solving tool in some patients where determination remains unclear, particularly in confirmation of advanced stage beyond the abdomen.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters

Purpose: To assess preoperative short-course radiotherapy (SCR) tumor response in locally advanced rectal cancer (LARC) by means of Standardized Index of Shape (SIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters derived from diffusion-weighted MRI (DW-MRI). Materials and methods: Thirty-four patients with LARC who underwent MRI scans before and after SCR followed by delayed surgery, retrospectively, were enrolled. SIS, ADC, IVIM parameters [tissue diffusion (D{sub t}), pseudo-diffusion (D{sub p}), perfusion fraction (f{sub p})] and DKI parameters [mean diffusivity (MD), mean of diffusional kurtosis (MK)] were calculated for each patient. IVIM parameters were estimated using two methods, namely conventional biexponential fitting (CBFM) and variable projection (VARPRO). After surgery, the pathological TNM and tumor regression grade (TRG) were estimated. For each parameter, percentage changes between before and after SCR were evaluated. Furthermore, an artificial neural network was trained for outcome prediction. Nonparametric sample tests and receiver operating characteristic curve (ROC) analysis were performed. Results: Fifteen patients were classified as responders (TRG ≤ 2) and 19 as not responders (TRG > 3). Seven patients had TRG 1 (pathological complete response, pCR). Mean and standard deviation values of pre-treatment CBFM D{sub p} and mean value of VARPRO D{sub p} pre-treatment showed statistically significant differences to predict pCR. (p value at Mann–Whitney test was 0.05, 0.03 and 0.008, respectively.) Exclusively SIS percentage change showed significant differences between responder and non-responder patients after SCR (p value << 0.001) and to assess pCR after SCR (p value << 0.001). The best results to predict pCR were obtained by VARPRO Fp mean value pre-treatment with area under ROC of 0.84, a sensitivity of 96.4%, a specificity of 71.4%, a positive predictive value (PPV) of 92.9%, a negative predictive value (NPV) of 83.3% and an accuracy of 91.2%. The best results to assess after treatment complete pathological response were obtained by SIS with an area under ROC of 0.89, a sensitivity of 85.7%, a specificity of 92.6%, a PPV of 75.0%, a NPV of 96.1% and an accuracy of 91.2%. Moreover, the best results to differentiate after treatment responders vs. non-responders were obtained by SIS with an area under ROC of 0.94, a sensitivity of 93.3%, a specificity of 84.2%, a PPV of 82.4%, a NPV of 94.1% and an accuracy of 88.2%. Promising initial results were obtained using a decision tree tested with all ADC, IVIM and DKI extracted parameter: we reached high accuracy to assess pathological complete response after SCR in LARC (an accuracy of 85.3% to assess pathological complete response after SCR using VARPRO D{sub p} mean value post-treatment, ADC standard deviation value pre-treatment, MD standard deviation value post-treatment). Conclusion: SIS is a hopeful DCE-MRI angiogenic biomarker to assess preoperative treatment response after SCR with delayed surgery. Furthermore, an important prognostic role was obtained by VARPRO F{sub p} mean value pre-treatment and by a decision tree composed by diffusion parameters derived by DWI and DKI to assess pathological complete response.

62 RADIOLOGY AND NUCLEAR MEDICINE↗