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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies {approx}80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million light curves, and found 1620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Bayesian approach to strong lens finding in the era of wide-area surveys

ABSTRACT The arrival of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide-area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal {O}(10^3)$ to $\mathcal {O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong lens experts to remove false positives which is only set to increase with these surveys. In this work, we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science, and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false-positive rate of 10−3 can be achieved with a completeness of 46 per cent, compared to 34 per cent for the best individual classifier. Given the large number of galaxy–galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses and to help prioritize candidates for follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

SEPATH: benchmarking the search for pathogens in human tissue whole genome sequence data leads to template pipelines

Background: Human tissue is increasingly being whole genome sequenced as we transition into an era of genomic medicine. With this arises the potential to detect sequences originating from microorganisms, including pathogens amid the plethora of human sequencing reads. In cancer research, the tumorigenic ability of pathogens is being recognized, for example, Helicobacter pylori and human papillomavirus in the cases of gastric non-cardia and cervical carcinomas, respectively. As of yet, no benchmark has been carried out on the performance of computational approaches for bacterial and viral detection within host-dominated sequence data. Results: We present the results of benchmarking over 70 distinct combinations of tools and parameters on 100 simulated cancer datasets spiked with realistic proportions of bacteria. mOTUs2 and Kraken are the highest performing individual tools achieving median genus-level F1 scores of 0.90 and 0.91, respectively. mOTUs2 demonstrates a high performance in estimating bacterial proportions. Employing Kraken on unassembled sequencing reads produces a good but variable performance depending on post-classification filtering parameters. These approaches are investigated on a selection of cervical and gastric cancer whole genome sequences where Alphapapillomavirus and Helicobacter are detected in addition to a variety of other interesting genera. Conclusions: We provide the top-performing pipelines from this benchmark in a unifying tool called SEPATH, which is amenable to high throughput sequencing studies across a range of high-performance computing clusters. SEPATH provides a benchmarked and convenient approach to detect pathogens in tissue sequence data helping to determine the relationship between metagenomics and disease.

Viral↗
DOE OSTI2024

Analysis and Benchmarking of feature reduction for classification under computational constraints

Abstract Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

A support vector machine (SVM) for predicting preferred treatment position in radiotherapy of patients with breast cancer

Purpose: NYU 05-181 protocol compared the CT simulation in both supine and prone positions for 400 patients with breast cancer (200 left-breast and 200 right-breast) to identify which setup is better at sparing heart and lung involvement in the treatment process. The results demonstrated that all right-breast patients benefited from the prone treatment position, while for left-breast patients, 85% were better treated prone and 15% were better treated supine. Using the clinical data collected from this protocol, the authors aimed at developing an automated tool capable of identifying which of the left-breast cancer patients are better treated supine without obtaining a second CT scan in the supine position. Methods: Prone CT scans from 198 of the 200 left-breast cancer patients enrolled in NYU 05-181 protocol were deidentified and exported to a dedicated research planning workstation. Three-dimensional geometric features of the organs at risk and tumor bed were extracted. A two-stage classifier was used to classify patients into the prone class or the supine class. In the first stage, the authors use simple thresholding to divide the patients into two groups based on their in-field heart volume. For patients with in-field heart volume {<=}0.1 cc, the prone position was chosen as the preferred treatment position. Patients with in-field heart volume >0.1 cc will be further classified in the second stage by a weighted support vector machine (SVM). The weight parameters of the SVM were adjusted to maximize the specificity [true-supine/(true-supine+false-prone)] at the cost of lowering but still maintaining reasonable sensitivity [true-prone/(true-prone+false-supine)]. The authors used K-fold cross validations to test the performance of the SVM classifier. A feature selection algorithm was also used to identify features that give the best classification performance. Results: After the first stage, 49 of the 198 left-breast cancer patients were found to have >0.1 cc of in-field heart volume. The three geometric features of heart orientation, distance between heart and tumor, and in-field lung were selected by the feature selection algorithm in the second stage of the two-stage classifier to give the best predefined weighted accuracy. The overall sensitivity and specificity of the proposed method were found to be 90.4% and 99.3%, respectively. Using two-stage classification, the authors reduced the proportion of prone-treated patients that need a second supine CT scan down to 16.3/170 or 9.6%, as compared to 21/170 or 12.4% when the authors use only the first stage (thresholding) for classification. Conclusions: The authors' study showed that a feature-based classifier is feasible for predicting the preferred treatment position, based on features extracted from prone CT scans. The two-stage classifier achieved very high specificity at an acceptable expense of sensitivity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗
DOE OSTI2013

Development of Human Factors Guidance for Human-Sy

This report characterizes a range of advanced HSI technologies and relevant performance measures. It also describes plans for further research required to formalize the technology classification framework in preparation for its submission to a selected standards organization. A second priority is a formal, quantitative model of innovation that can be used by DOE and planners to anticipate the introduction of advanced HSI in nuclear power plant control rooms. Much of the prior human factors inspired research stops at the interface and fails to provide a definitive process and integration of I&C. The potential merger of two different approaches in this area is discussed and also why an integrated approach that ties I&C and human factors together is warranted. The conclusion is that there is an urgent need for requirements and guidelines for HSI implementation at nuclear facilities to be formalized and this research should lead to the potential adoption of a national or international standard or guidance on the best methods to select and implement advanced technologies to achieve safe and efficient human-system interactions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) 3π survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of RR Lyrae stars in this data set poses a challenge, given PS1's sparse, asynchronous multi-band light curves (≲12 epochs in each of five bands, taken over a 4.5 year period). We present a novel template fitting technique that uses well-defined and physically motivated multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate period estimates, precise to 2 s in >80% of cases. We augment these light-curve fits with other features from photometric time-series and provide them to progressively more detailed machine-learned classification models. From these models, we are able to select the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR Lyrae stars to date. The PS1 sample of ∼45,000 RRab stars is pure (90%) and complete (80% at 80 kpc) at high galactic latitudes. It also provides distances that are precise to 3%, measured with newly derived period–luminosity relations for optical/near-infrared PS1 bands. With the addition of proper motions from Gaia and radial velocity measurements from multi-object spectroscopic surveys, we expect the PS1 sample of RR Lyrae stars to become the premier source for studying the structure, kinematics, and the gravitational potential of the Galactic halo. The techniques presented in this study should translate well to other sparse, multi-band data sets, such as those produced by the Dark Energy Survey and the upcoming Large Synoptic Survey Telescope Galactic plane sub-survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Optical Photometric Indicators of Galaxy Cluster Relaxation

Abstract The most dynamically relaxed clusters of galaxies play a special role in cosmological studies as well as astrophysical studies of the intracluster medium (ICM) and active galactic nucleus feedback. While high-spatial-resolution imaging of the morphology of the ICM has long been the gold standard for establishing a cluster’s dynamical state, such data are not available from current or planned surveys, and thus require separate, pointed follow-up observations. With optical and/or near-IR photometric imaging, and red-sequence cluster finding results from those data, expected to be ubiquitously available for clusters discovered in upcoming optical and millimeter-wavelength surveys, it is worth asking how effectively photometric data alone can identify relaxed cluster candidates, before investing in, e.g., high-resolution X-ray observations. Here we assess the ability of several simple photometric measurements, based on the redMaPPer cluster finder run on Sloan Digital Sky Survey data, to reproduce X-ray classifications of dynamical state for an X-ray selected sample of massive clusters. We find that two simple metrics contrasting the bright central galaxy (BCG) to other cluster members can identify a complete sample of relaxed clusters with a purity of ∼40% in our data set. Including minimal ICM information in the form of a center position increases the purity to ∼60%. However, all three metrics depend critically on correctly identifying the BCG, which is presently a challenge for optical red-sequence cluster finders.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Infrared and radar fusion detection method based on heterogeneous data preprocessing

The difficulty in infrared and radar fusion target detection lies in the pre-processing steps such as sample reduction and feature reduction of heterogeneous sensors. For the problem of data volume redundancy, HVS sample reduction is proposed to reduce the amount of data in the infrared data set. Infrared and radar joint feature vectors are constructed through nearest neighbor data association. For the problem of feature redundancy, a weighted mutual information feature selection method based on prior information is proposed according to the difference of feature sources. The fusion data set is then used for classification. The experiments show that the HVS-based sample reduction and the prior weighted feature selection achieved a higher detection probability and a shorter detection time, and improved the robustness in the case of large differences between the training set and the test set.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

NEAR-INFRARED PROPERTIES OF THE X-RAY-EMITTING YOUNG STELLAR OBJECTS IN THE CARINA NEBULA

The Great Nebula in Carina (NGC 3372) is the best target to study in detail the process of violent massive star formation and the resulting feedback effects of cloud dispersal and triggered star formation. While the population of massive stars is rather well studied, the associated low-mass stellar population was largely unknown up to now. The near-infrared study in this paper builds on the results of the Chandra Carina Complex Project, that detected 14,368 X-ray sources in the 1.4 deg{sup 2} survey region, an automatic source classification study that classified 10,714 of these X-ray sources as very likely young stars in Carina, and an analysis of the clustering properties of the X-ray-selected Carina members. In order to determine physical properties of the X-ray-selected stars, most of which were previously unstudied, we used HAWK-I at the ESO Very Large Telescope to conduct a very deep near-IR survey with subarcsecond angular resolution, covering an area of about 1280 arcmin{sup 2}. The HAWK-I images reveal more than 600,000 individual infrared sources, whereby objects as faint as J {approx} 23, H {approx} 22, and K{sub s} {approx} 21 are detected at signal-to-noise ratio (S/N) {>=}3. While less than half of the Chandra X-ray sources have counterparts in the Two Micron All Sky Survey catalog, the {approx}5 mag deeper HAWK-I data reveal infrared counterparts to 6636 (=88.8%) of the 7472 Chandra X-ray sources in the HAWK-I field. We analyze near-infrared color-color and color-magnitude diagrams to derive information about the extinctions, infrared excesses (as tracers for circumstellar disks), ages, and masses of the X-ray-selected objects. The near-infrared properties agree well with the results of the automatic X-ray source classification, showing that the remaining contamination in the X-ray-selected sample of Carina members is very low ({approx}<7%). The shape of the K-band luminosity function of the X-ray-selected Carina members agrees well with that derived for the Orion Nebula Cluster, suggesting that, down to the X-ray detection limit around 0.5-1 M{sub sun}, the shape of the initial mass function (IMF) in Carina is consistent with that in Orion (and thus the field IMF). The fraction of stars with near-infrared excesses is rather small, {approx}<10%, but shows considerable variations between individual parts of the complex. The distribution of extinctions for the diskless stars ranges from {approx}1.6 mag to {approx}6.2 mag (central 80th percentile), clearly showing a considerable range of differential extinction between individual stars in the complex.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2010

Bayesian classification of partially observed outbreaks using time-series data.

Results show that a time-series based classification may be possible. For the test cases considered, the correct model can be selected and the number of index case can be captured within {+-} {sigma} with 5-10 days of data. The low signal-to-noise ratio makes the classification difficult for small epidemics. The problem statement is: (1) Create Bayesian techniques to classify and characterize epidemics from a time-series of ICD-9 codes (will call this time-series a 'morbidity stream'); and (2) It is assumed the morbidity stream has already set off an alarm (through a Kalman filter anomaly detector) Starting with a set of putative diseases: Identify which disease or set of diseases 'fit the data best' and, Infer associated information about it, i.e. number of index cases, start time of the epidemic, spread rate, etc.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

SU-F-R-14: PET Based Radiomics to Predict Outcomes in Patients with Hodgkin Lymphoma

Purpose: To identify PET-based radiomics features associated with high refractory/relapsed disease risk for Hodgkin lymphoma patients. Methods: A total of 251 Hodgkin lymphoma patients including 19 primary refractory and 9 relapsed patients were investigated. All patients underwent an initial pre-treatment diagnostic FDG PET/CT scan. All cancerous lymph node regions (ROIs) were delineated by an experienced physician based on thresholding each volume of disease in the anatomical regions to SUV>2.5. We extracted 122 image features and evaluated the effect of ROI selection (the largest ROI, the ROI with highest mean SUV, merged ROI, and a single anatomic region [e.g. mediastinum]) on classification accuracy. Random forest was used as a classifier and ROC analysis was used to assess the relationship between selected features and patient’s outcome status. Results: Each patient had between 1 and 9 separate ROIs, with much intra-patient variability in PET features. The best model, which used features from a single anatomic region (the mediastinal ROI, only volumes>5cc: 169 patients with 12 primary refractory) had a classification accuracy of 80.5% for primary refractory disease. The top five features, based on Gini index, consist of shape features (max 3D-diameter and volume) and texture features (correlation and information measure of correlation1&2). In the ROC analysis, sensitivity and specificity of the best model were 0.92 and 0.80, respectively. The area under the ROC (AUC) and the accuracy were 0.86 and 0.86, respectively. The classification accuracy was less than 60% for other ROI models or when ROIs less than 5cc were included. Conclusion: This study showed that PET-based radiomics features from the mediastinal lymph region are associated with primary refractory disease and therefore may play an important role in predicting outcomes in Hodgkin lymphoma patients. These features could be additive beyond baseline tumor and clinical characteristics, and may warrant more aggressive treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Luminosity and redshift dependence of the covering factor of active galactic nuclei viewed with WISE and Sloan digital sky survey

In this work, we investigate the dependence of the covering factor (CF) of active galactic nuclei (AGNs) on the mid-infrared (MIR) luminosity and the redshift. We constructed 12 and 22 μm luminosity functions (LFs) at 0.006 ≤z ≤ 0.3 using Wide-field Infrared Survey Explorer (WISE) data. Combining the WISE catalog with Sloan Digital Sky Survey (SDSS) spectroscopic data, we selected 223,982 galaxies at 12 μm and 25,721 galaxies at 22 μm for spectroscopic classification. We then identified 16,355 AGNs at 12 μm and 4683 AGNs at 22 μm by their optical emission lines and cataloged classifications in the SDSS. Following that, we estimated the CF as the fraction of Type 2 AGN in all AGNs whose MIR emissions are dominated by the active nucleus (not their host galaxies) based on their MIR colors. We found that the CF decreased with increasing MIR luminosity, regardless of the choice of Type 2 AGN classification criteria, and the CF did not change significantly with redshift for z ≤ 0.2. Furthermore, we carried out various tests to determine the influence of selection bias and confirmed that similar dependences exist, even when taking these uncertainties into account. The luminosity dependence of the CF can be explained by the receding torus model, but the 'modified' receding torus model gives a slightly better fit, as suggested by Simpson.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Wide cool and ultracool companions to nearby stars from Pan-STARRS 1

We present the discovery of 57 wide (>5'') separation, low-mass (stellar and substellar) companions to stars in the solar neighborhood identified from Pan-STARRS 1 (PS1) data and the spectral classification of 31 previously known companions. Our companions represent a selective subsample of promising candidates and span a range in spectral type of K7-L9 with the addition of one DA white dwarf. These were identified primarily from a dedicated common proper motion search around nearby stars, along with a few as serendipitous discoveries from our Pan-STARRS 1 brown dwarf search. Our discoveries include 23 new L dwarf companions and one known L dwarf not previously identified as a companion. The primary stars around which we searched for companions come from a list of bright stars with well-measured parallaxes and large proper motions from the Hipparcos catalog (8583 stars, mostly A-K dwarfs) and fainter stars from other proper motion catalogs (79170 stars, mostly M dwarfs). We examine the likelihood that our companions are chance alignments between unrelated stars and conclude that this is unlikely for the majority of the objects that we have followed-up spectroscopically. We also examine the entire population of ultracool (>M7) dwarf companions and conclude that while some are loosely bound, most are unlikely to be disrupted over the course of ∼10 Gyr. Our search increases the number of ultracool M dwarf companions wider than 300 AU by 88% and increases the number of L dwarf companions in the same separation range by 82%. Finally, we resolve our new L dwarf companion to HIP 6407 into a tight (0.''13, 7.4 AU) L1+T3 binary, making the system a hierarchical triple. Our search for these key benchmarks against which brown dwarf and exoplanet atmosphere models are tested has yielded the largest number of discoveries to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Historical Changes in Histological Diagnosis of Lung Cancer

Background: Histological classification of lung cancer is essential for investigations of carcinogenesis and treatment selection. We examined the temporal changes of lung cancer histological subtypes. Methods: Lung cancer cases diagnosed in the Life Span Study cohort between 1958 and 1999 were collected from tumor registries (TR), mainly consisting of population-based cancer registries. A total of 1,025 cases were histologically reviewed according to the World Health Organization 2004 Classification by a panel of pathologists (PP). Sensitivity and specificity of diagnoses in TR were calculated, assuming that the diagnosis by PP was the gold standard. Results: Sensitivity and specificity were 0.91 and 0.92 for adenocarcinoma (AD), respectively, and 0.92 and 0.94, respectively, for squamous cell carcinoma (SQ). They were similar for AD and SQ throughout the observation period. For small cell carcinoma (SM), sensitivity was low until about 1980 (0.47 in 1958–1969, and 0.61 in 1970–1979) and then became higher thereafter (0.98 in 1980–1989, and 0.95 in 1990–1999), whereas specificity was high during the whole period (range 0.99 to 1.00). Among 45 cases that were not reported as SM in TR but diagnosed as SM by PP, 16 cases were recorded as undifferentiated carcinoma in TR. Conclusion: Diagnosis of AD and SQ of lung cancer were generally consistent between TR records and PP review, but SMs tended to be coded as other histological types until the 1970s.

60 APPLIED LIFE SCIENCES↗