Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources.

Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution-combining data from multiple sources-faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus(T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average(WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.

Estimation of global soil respiration by accounting for land-use changes derived from remote sensing data.

Soil respiration is one of the largest carbon fluxes from terrestrial ecosystems. Estimating global soil respiration is difficult because of its high spatiotemporal variability and sensitivity to land-use change. Satellite monitoring provides useful data for estimating the global carbon budget, but few studies have estimated global soil respiration using satellite data. We provide preliminary insights into the estimation of global soil respiration in 2001 and 2009 using empirically derived soil temperature equations for 17 ecosystems obtained by field studies, as well as MODIS climate data and land-use maps at a 4-km resolution. The daytime surface temperature from winter to early summer based on the MODIS data tended to be higher than the field-observed soil temperatures in subarctic and temperate ecosystems. The estimated global soil respiration was 94.8 and 93.8 Pg C yr-1 in 2001 and 2009, respectively. However, the MODIS land-use maps had insufficient spatial resolution to evaluate the effect of land-use change on soil respiration. The spatial variation of soil respiration (Q10) values was higher but its spatial variation was lower in high-latitude areas than in other areas. However, Q10 in tropical areas was more variable and was not accurately estimated (the values were >7.5 or <1.0) because of the low seasonal variation in soil respiration in tropical ecosystems. To solve these problems, it will be necessary to validate our results using a combination of remote sensing data at higher spatial resolution and field observations for many different ecosystems, and it will be necessary to account for the effects of more soil factors in the predictive equations.

Monitoring of biophysical parameters of cashew plants in Cambodia using ALOS/PALSAR data.

An accurate estimation of a plant's age is required for the prediction of yield and management practices. This study demonstrates the relationship between backscattering properties (σ°) of Phased Array type L-band Synthetic Aperture Radar (PALSAR) dual polarimetric data with cashew plants' biophysical parameters (height, age, crown diameter, diameter at breast height, basal area, tree density, and biomass) in Cambodia. PALSAR σ° has shown a positive correlation with the biophysical parameters of cashew plants. The value of σ° increases with the age of cashew plants. At a young stage, the cashew plants show a higher rate of an increase in σ° compared to that at the mature stage. The σ° horizontal polarization transmitted and vertical received (HV) shows higher sensitivity to the plant's growth than σ° horizontal polarization transmitted and received (HH). High backscattering and low variations were observed at mature stage (8-12 years) of cashew plantation. Saturation in backscattering has shown from the age of about 13 years. The validation results indicate strong coefficient of determination (R(2) = 0.86 and 0.88) for PALSAR-predicted age and biomass of cashew plants with root mean square error = 1.8 years and 16.3 t/ha for age and biomass, respectively. The correlations of σ° (HH) with biophysical parameters observed in the dry season were better than those of the rainy season because soil moisture interferes with backscattering in the rainy season. Biomass accumulation rate of cashew plants has been predicted that would be useful for selection of plants species to enhance carbon sequestration. This study provides an insight to use PALSAR for the monitoring of growth stages of plants at the regional level.

Investigation of time profile of FEV1 across the onset of potential COPD: a retrospective cohort study using medical checkup data in Japan.

This study compared the time profile of FEV1 after COPD diagnosis among rapid decliners, slow decliners, and sustainers in the year of COPD diagnosis. COPD subjects were identified from the annual medical checkup records of Hitachi, Ltd., employees in Japan (April 1998-March 2019). Subjects were categorized into 3 groups (rapid decliner [decrease of FEV1 ≥ 63 mL/year], slow decliner [< 63 and ≥ 31 mL/year], and sustainer [< 31 mL/year]) for 5 years. The time profile of FEV1 was compared using mixed-effects model for 5 years after diagnosis; risk factors of rapid decliner were detected using logistic model/gradient boosting decision tree. Of 1294 eligible subjects, 18.6%, 25.7%, and 55.7% were classified as rapid decliners, slow decliners, and sustainers, respectively. The annual rates of FEV1 decline were similar 3 years before and until COPD diagnosis. The mean FEV1 in rapid decliners was 2.82 ± 0.04 L in year 0 and 2.41 ± 0.05 L in year 5, and in sustainers, it was 2.67 ± 0.02 L and 2.72 ± 0.02 L (year 0, p = 0.0004). In conclusion, FEV1 declined yearly before diagnosis and the time profiles of FEV1 were different in the 3 groups after COPD diagnosis. Therefore, appropriate treatment of the 3 groups with regular lung function tests is necessary to follow FEV1 decline after COPD onset.

Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area.

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

Human-elephant conflict risk assessment under coupled climatic and anthropogenic changes in Thailand.

As natural resources decrease, competition between humans and large endangered wildlife increases, hindering the sustainability of animal conservation and human development. Despite the multi-dimensional nature of such interactions, proactive assessments that consider both the biosphere and anthroposphere remain limited. In this study, we proposed a human elephant conflict risk assessment framework and analyzed the spatial distribution of risk at the baseline (2000-2019) and in the near future (2025-2044) for Thailand, so that it may address the multifaceted characteristics and impending effects of climate change. Future scenarios were based on the combination of RCP45/SSP2 or RCP85/SSP5 and spatial policy, with or without elephant buffer zones. The composite risk index, comprised of hazard, exposure, and vulnerability, was constructed using the geometric mean, and validation was performed with the area under the curve (AUC). Our results projected a shift with increasing future risk toward higher latitudes and altitudes. Increasing future risk (average +1.7% to +7.4%) in the four forest complexes (FCs) in northwestern regions was a result of higher hazard and vulnerability from more favorable habitat conditions and increasing drought probability, respectively. Reduction in future risk (average -3.1% to -57.9%) in other FCs in lower regions was mainly due to decreasing hazard because of decreasing habitat suitability. Our results also highlight geographically explicit strategies to support long-term planning of conservation resources. Areas with increasing future risk are currently facing low conflict; hence it is recommended that future strategies should enhance adaptive capacity and coexistence awareness. Conversely, areas with lowering future risk from a decrease in habitat quality are recommended to identify buffer strategies around protected areas to support existing large elephant populations.

Machine Learning Methods for the Diagnosis of Chronic Obstructive Pulmonary Disease in Healthy Subjects: Retrospective Observational Cohort Study.

BACKGROUND: Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances, including tobacco smoke, is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. OBJECTIVE: This study aimed to predict the risk factors for COPD diagnosis using machine learning in an annual medical check-up database. METHODS: In this retrospective observational cohort study (ARTDECO [Analysis of Risk Factors to Detect COPD]), annual medical check-up records for all Hitachi Ltd employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30 to 75 years without a prior diagnosis of COPD/asthma or a history of cancer were included. The database included clinical measurements (eg, pulmonary function tests) and questionnaire responses. To predict the risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning (XGBoost) method was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the prebronchodilator forced expiratory volume in 1 second (FEV1) to prebronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations. RESULTS: Of the 26,101 individuals screened, 1213 met the exclusion criteria, and thus, 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity, and percent predicted value of FEV1. The areas under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively. CONCLUSIONS: Using a machine learning model in this longitudinal database, we identified a number of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan.

Nitrogen oxides concentration and emission change detection during COVID-19 restrictions in North India.

COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25-April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.

OBJECTIVES: Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS: Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS: The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION: A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.

A new PET scanner with semiconductor detectors enables better identification of intratumoral inhomogeneity.

UNLABELLED: An autoradiography method revealed intratumoral inhomogeneity in various solid tumors. It is becoming increasingly important to estimate intratumoral inhomogeneity. However, with low spatial resolution and high scatter noise, it is difficult to detect intratumoral inhomogeneity in clinical settings. We developed a new PET system with CdTe semiconductor detectors to provide images with high spatial resolution and low scatter noise. Both phantom images and patients' images were analyzed to evaluate intratumoral inhomogeneity. METHODS: This study was performed with a cold spot phantom that had 6-mm-diameter cold sphenoid defects, a dual-cylinder phantom with an adjusted concentration of 1:2, and an "H"-shaped hot phantom. These were surrounded with water. Phantom images and (18)F-FDG PET images of patients with nasopharyngeal cancer were compared with conventional bismuth germanate PET images. Profile curves for the phantoms were measured as peak-to-valley ratios to define contrast. Intratumoral inhomogeneity and tumor edge sharpness were evaluated on the images of the patients. RESULTS: The contrast obtained with the semiconductor PET scanner (1.53) was 28% higher than that obtained with the conventional scanner (1.20) for the 6-mm-diameter cold sphenoid phantom. The contrast obtained with the semiconductor PET scanner (1.43) was 27% higher than that obtained with the conventional scanner (1.13) for the dual-cylinder phantom. Similarly, the 2-mm cold region between 1-mm hot rods was identified only by the new PET scanner and not by the conventional scanner. The new PET scanner identified intratumoral inhomogeneity in more detail than the conventional scanner in 6 of 10 patients. The tumor edge was sharper on the images obtained with the new PET scanner than on those obtained with the conventional scanner. CONCLUSION: These phantom and clinical studies suggested that this new PET scanner has the potential for better identification of intratumoral inhomogeneity, probably because of its high spatial resolution and low scatter noise.

Extended averaged learning subspace method for hyperspectral data classification.

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations.

The antegrade dissection and re-entry technique as preparation of intravascular ultrasound guided re-wiring.

Although the antegrade dissection and re-entry technique (ADR) with Stingray system is one of the procedures for percutaneous coronary intervention (PCI) of chronic total occlusion (CTO), it has some risk of side-branch occlusion. This article reports a CTO case in the left circumflex artery successfully treated with combination use of ADR subintimal tracking and intravascular ultrasound (IVUS)-guided re-wiring without side-branch occlusion. Antegrade approach with single-wire and parallel-wire technique was failed. Retrograde approach through ipsilateral collateral was also failed. Therefore, the ADR was attempted and Stingray wire crossed through at the distal site of posterolateral (PL) branch. To avoid PL branch occlusion, IVUS-guided re-wiring to the true lumen was attempted. Finally, the CTO lesion was recanalized without any complication and 1 year follow-up angiography had good result. ADR as preparation of IVUS-guided re-wiring might be one of the useful procedures for those complex CTO cases.

Dual Isotope SPECT Study With Epilepsy Patients Using Semiconductor SPECT System.

PURPOSE: We developed a prototype CdTe SPECT system with 4-pixel matched collimator for brain study. This system provides high-energy-resolution (6.6%), high-sensitivity (220 cps/MBq/head), and high-spatial-resolution images. The aim of this study was to evaluate dual-isotope study of CBF and central benzodiazepine receptor (BZR) images using Tc-ECD and I-IMZ with the new SPECT system in patients with epilepsy comparing with single-isotope study using the conventional scintillation gamma camera. METHODS: This study included 13 patients with partial epilepsy. The BZR images were acquired at 3 hours after I-IMZ injection for 20 minutes. The images of IMZ were acquired with a conventional 3-head scintillation gamma camera. After BZR image acquisition with the conventional camera, Tc-ECD was injected, and CBF and BZR images were acquired simultaneously 5 minutes after ECD injection with the new SPECT system. The CBF images were also acquired with the conventional camera on separate days. The findings were visually analyzed, and 3D-SSP maximum Z scores of lesions were compared between the 2 studies. RESULTS: There were 47 abnormal lesions on BZR images and 60 abnormal lesions on CBF images in the single-isotope study with the conventional camera. Dual-isotope study with the new system showed concordant abnormal findings of 46 of 47 lesions on BZR and 54 of 60 lesions on CBF images with the single-isotope study with the conventional camera. There was high agreement between the 2 studies in both BZR and CBF findings (Cohen κ values = 0.96 for BZR and 0.78 for CBF). In semiquantitative analysis, maximum Z scores of dual-isotope study with the new system strongly correlated with those of single-isotope study with the conventional camera (BZR: r = 0.82, P < 0.05, CBF: r = 0.87, P < 0.05). CONCLUSIONS: Our new SPECT system permits dual-isotope study for pixel-by-pixel analysis of CBF and BZR information with the same pathophysiological condition in patients with epilepsy.

Potential distribution of pine wilt disease under future climate change scenarios.

Pine wilt disease (PWD) constitutes a serious threat to pine forests. Since development depends on temperature and drought, there is a concern that future climate change could lead to the spread of PWD infections. We evaluated the risk of PWD in 21 susceptible Pinus species on a global scale. The MB index, which represents the sum of the difference between the mean monthly temperature and 15 when the mean monthly temperatures exceeds 15°C, was used to determine current and future regions vulnerable to PWD (MB ≥ 22). For future climate conditions, we compared the difference in PWD risks among four different representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5) and two time periods (2050s and 2070s). We also evaluated the impact of climate change on habitat suitability for each Pinus species using species distribution models. The findings were then integrated and the potential risk of PWD spread under climate change was discussed. Within the natural Pinus distribution area, southern parts of North America, Europe, and Asia were categorized as vulnerable regions (MB ≥ 22; 16% of the total Pinus distribution area). Representative provinces in which PWD has been reported at least once overlapped with the vulnerable regions. All RCP scenarios showed expansion of vulnerable regions in northern parts of Europe, Asia, and North America under future climate conditions. By the 2070s, under RCP 8.5, an estimated increase in the area of vulnerable regions to approximately 50% of the total Pinus distribution area was revealed. In addition, the habitat conditions of a large portion of the Pinus distribution areas in Europe and Asia were deemed unsuitable by the 2070s under RCP 8.5. Approximately 40% of these regions overlapped with regions deemed vulnerable to PWD, suggesting that Pinus forests in these areas are at risk of serious damage due to habitat shifts and spread of PWD.

Local relapse of nasopharyngeal cancer and Voxel-based analysis of FMISO uptake using PET with semiconductor detectors.

BACKGROUND: Hypoxic cancer cells are thought to be radioresistant and could impact local recurrence after radiotherapy (RT). One of the major hypoxic imaging modalities is [18F]fluoromisonidazole positron emission tomography (FMISO-PET). High FMISO uptake before RT could indicate radioresistant sites and might be associated with future local recurrence. The predictive value of FMISO-PET for intra-tumoral recurrence regions was evaluated using high-resolution semiconductor detectors in patients with nasopharyngeal carcinoma after intensity-modulated radiotherapy (IMRT). METHODS: Nine patients with local recurrence and 12 patients without local recurrence for more than 3 years were included in this study. These patients received homogeneous and standard doses of radiation to the primary tumor irrespective of FMISO uptake. The FMISO-PET image before RT was examined via a voxel-based analysis, which focused on the relationship between the degree of FMISO uptake and recurrence region. RESULTS: In the pretreatment FMISO-PET images, the tumor-to-muscle ratio (TMR) of FMISO in the voxels of the tumor recurrence region was significantly higher than that of the non-recurrence region (p < 0.0001). In the recurrent patient group, a TMR value of 1.37 (95% CI: 1.36-1.39) corresponded to a recurrence rate of 30%, the odds ratio was 5.18 (4.87-5.51), and the area under the curve (AUC) of the receiver operating characteristic curve was 0.613. In all 21 patients, a TMR value of 2.42 (2.36-2.49) corresponded to an estimated recurrence rate of 30%, and the AUC was only 0.591. CONCLUSIONS: The uptake of FMISO in the recurrent region was significantly higher than that in the non-recurrent region. However, the predictive value of FMISO-PET before IMRT is not sufficient for up-front dose escalation for the intra-tumoral high-uptake region of FMISO. Because of the higher mean TMR of the recurrence region, a new hypoxic imaging method is needed to improve the sensitivity and specificity for hypoxia.

Pulmonary Tumor Thrombotic Microangiopathy Caused by Urothelial Carcinoma Expressing Vascular Endothelial Growth Factor, Platelet-derived Growth Factor, and Osteopontin.

Pulmonary tumor thrombotic microangiopathy (PTTM) is a fatal cancer-related pulmonary complication. It is generally caused by gastric adenocarcinoma, and several molecules produced by tumor cells are reported to play important roles in its pathogenesis. We herein report an autopsy case of PTTM caused by urothelial carcinoma. Vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), and osteopontin were found to be expressed in both the primary tumor cells and metastatic cells in the PTTM lesions. These findings implicate the possible involvement of VEGF, PDGF, and osteopontin in the pathogenesis of PTTM caused by urothelial carcinoma.

Simultaneous Tc-99m and I-123 dual-radionuclide imaging with a solid-state detector-based brain-SPECT system and energy-based scatter correction.

BACKGROUND: A brain single-photon emission computed tomography (SPECT) system using cadmium telluride (CdTe) solid-state detectors was previously developed. This CdTe-SPECT system is suitable for simultaneous dual-radionuclide imaging due to its fine energy resolution (6.6 %). However, the problems of down-scatter and low-energy tail due to the spectral characteristics of a pixelated solid-state detector should be addressed. The objective of this work was to develop a system for simultaneous Tc-99m and I-123 brain studies and evaluate its accuracy. METHODS: A scatter correction method using five energy windows (FiveEWs) was developed. The windows are Tc-lower, Tc-main, shared sub-window of Tc-upper and I-lower, I-main, and I-upper. This FiveEW method uses pre-measured responses for primary gamma rays from each radionuclide to compensate for the overestimation of scatter by the triple-energy window method that is used. Two phantom experiments and a healthy volunteer experiment were conducted using the CdTe-SPECT system. A cylindrical phantom and a six-compartment phantom with five different mixtures of Tc-99m and I-123 and a cold one were scanned. The quantitative accuracy was evaluated using 18 regions of interest for each phantom. In the volunteer study, five healthy volunteers were injected with Tc-99m human serum albumin diethylene triamine pentaacetic acid (HSA-D) and scanned (single acquisition). They were then injected with I-123 N-isopropyl-4-iodoamphetamine hydrochloride (IMP) and scanned again (dual acquisition). The counts of the Tc-99m images for the single and dual acquisitions were compared. RESULTS: In the cylindrical phantom experiments, the percentage difference (PD) between the single and dual acquisitions was 5.7 ± 4.0 % (mean ± standard deviation). In the six-compartment phantom experiment, the PDs between measured and injected activity for Tc-99m and I-123 were 14.4 ± 11.0 and 2.3 ± 1.8 %, respectively. In the volunteer study, the PD between the single and dual acquisitions was 4.5 ± 3.4 %. CONCLUSIONS: This CdTe-SPECT system using the FiveEW method can provide accurate simultaneous dual-radionuclide imaging. A solid-state detector SPECT system using the FiveEW method will permit quantitative simultaneous Tc-99m and I-123 study to become clinically applicable.

Inferior vena cava filter misplacement in the right atrium and migration to the right ventricle followed by successful removal using the endovascular technique: A case report and review of the literature.

Inferior vena cava filters are effective for preventing the passage of thrombi into the pulmonary arteries in patients with pulmonary embolism and deep vein thrombosis. These filters are indicated in patients with contraindications to anticoagulant therapy or in patients with recurrent acute pulmonary embolism despite the administration of anticoagulant therapy. However, the occurrence of filter-related complications, such as filter migration to the heart, has been increasing. Herein, we report a case of OptEase inferior vena cava filter misplacement in the right atrium. Although the filter migrated to the right ventricle, it was successfully removed and repositioned in the inferior vena cava using endovascular techniques. Unfortunately, moderate tricuspid regurgitation developed, due to the damage to the tricuspid valve that was caused by the procedure. We have also reviewed the relevant literature and discussed the possible strategies for managing cases of filter migration to the heart and preventing filter misplacement.

High-resolution brain SPECT imaging by combination of parallel and tilted detector heads.

OBJECTIVE: To improve the spatial resolution of brain single-photon emission computed tomography (SPECT), we propose a new brain SPECT system in which the detector heads are tilted towards the rotation axis so that they are closer to the brain. In addition, parallel detector heads are used to obtain the complete projection data set. We evaluated this parallel and tilted detector head system (PT-SPECT) in simulations. METHODS: In the simulation study, the tilt angle of the detector heads relative to the axis was 45°. The distance from the collimator surface of the parallel detector heads to the axis was 130 mm. The distance from the collimator surface of the tilted detector heads to the origin on the axis was 110 mm. A CdTe semiconductor panel with a 1.4 mm detector pitch and a parallel-hole collimator were employed in both types of detector head. A line source phantom, cold-rod brain-shaped phantom, and cerebral blood flow phantom were evaluated. The projection data were generated by forward-projection of the phantom images using physics models, and Poisson noise at clinical levels was applied to the projection data. The ordered-subsets expectation maximization algorithm with physics models was used. We also evaluated conventional SPECT using four parallel detector heads for the sake of comparison. RESULTS: The evaluation of the line source phantom showed that the transaxial FWHM in the central slice for conventional SPECT ranged from 6.1 to 8.5 mm, while that for PT-SPECT ranged from 5.3 to 6.9 mm. The cold-rod brain-shaped phantom image showed that conventional SPECT could visualize up to 8-mm-diameter rods. By contrast, PT-SPECT could visualize up to 6-mm-diameter rods in upper slices of a cerebrum. The cerebral blood flow phantom image showed that the PT-SPECT system provided higher resolution at the thalamus and caudate nucleus as well as at the longitudinal fissure of the cerebrum compared with conventional SPECT. CONCLUSION: PT-SPECT provides improved image resolution at not only upper but also at central slices of the cerebrum.