Cancer screening with multicancer detection tests: A translational science review.

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Free-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model.

PURPOSE: To develop a new method for free-breathing 3D extracellular volume (ECV) mapping of the whole heart at 3 T. METHODS: A free-breathing 3D cardiac ECV mapping method was developed at 3 T. T1 mapping was performed before and after contrast agent injection using a free-breathing electrocardiogram-gated inversion recovery sequence with spoiled gradient echo readout. A linear tangent space alignment model-based method was used to reconstruct high-frame-rate dynamic images from (k,t)-space data sparsely sampled along a random stack-of-stars trajectory. Joint T1 and transmit B1 estimation were performed voxel-by-voxel for pre- and post-contrast T1 mapping. To account for the time-varying T1 after contrast agent injection, a linearly time-varying T1 model was introduced for post-contrast T1 mapping. ECV maps were generated by aligning pre- and post-contrast T1 maps through affine transformation. RESULTS: The feasibility of the proposed method was demonstrated using in vivo studies with six healthy volunteers at 3 T. We obtained 3D ECV maps at a spatial resolution of 1.9 × 1.9 × 4.5 mm3 and a FOV of 308 × 308 × 144 mm3, with a scan time of 10.1 ± 1.4 and 10.6 ± 1.6 min before and after contrast agent injection, respectively. The ECV maps and the pre- and post-contrast T1 maps obtained by the proposed method were in good agreement with the 2D MOLLI method both qualitatively and quantitatively. CONCLUSION: The proposed method allows for free-breathing 3D ECV mapping of the whole heart within a practically feasible imaging time. The estimated ECV values from the proposed method were comparable to those from the existing method.

Prevalence and Predictors of Physician-Patient Discordance in Prognostic Perceptions in Advanced Cancer.

BACKGROUND: Discordance between physicians' and patients' prognostic perceptions in advanced cancer care threatens informed medical decision-making and end-of-life preparation, yet this phenomenon is poorly understood. We sought to: (1) describe the extent and direction of prognostic discordance, patients' prognostic information preferences in cases of prognostic discordance, and physicians' awareness of prognostic discordance; and (2) examine which patient, physician, and caregiver factors predict prognostic discordance. MATERIALS AND METHODS: Oncologists and advanced cancer patients (median survival ≤12 months; n = 515) from 7 Dutch hospitals completed structured surveys in a cross-sectional study. Prognostic discordance was operationalized by comparing physicians' and patients' perceptions of the likelihood of cure, 2-year mortality risk, and 1-year mortality risk. RESULTS: Prognostic discordance occurred in 20% (likelihood of cure), 24%, and 35% (2-year and 1-year mortality risk) of physician-patient dyads, most often involving patients with more optimistic perceptions than their physician. Among patients demonstrating prognostic discordance, the proportion who preferred not knowing prognosis varied from 7% (likelihood of cure) to 37% (1-year mortality risk), and 45% (2-year mortality risk). Agreement between physician-perceived and observed prognostic discordance or concordance was poor (kappa = 0.186). Prognostic discordance was associated with several patient factors (stronger fighting spirit, self-reported absence of prognostic discussions, an information source other than the healthcare provider), and greater physician-reported uncertainty about prognosis. CONCLUSION: Up to one-third of the patients perceive prognosis discordantly from their physician, among whom a substantial proportion prefers not knowing prognosis. Most physicians lack awareness of prognostic discordance, raising the need to explore patients' prognostic information preferences and perceptions, and to tailor prognostic communication.

Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.

PURPOSE: To develop a manifold learning-based method that leverages the intrinsic low-dimensional structure of MR Spectroscopic Imaging (MRSI) signals for joint spectral quantification. METHODS: A linear tangent space alignment (LTSA) model was proposed to represent MRSI signals. In the proposed model, the signals of each metabolite were represented using a subspace model and the local coordinates of the subspaces were aligned to the global coordinates of the underlying low-dimensional manifold via linear transform. With the basis functions of the subspaces predetermined via quantum mechanics simulations, the global coordinates and the matrices for the local-to-global coordinate alignment were estimated by fitting the proposed LTSA model to noisy MRSI data with a spatial smoothness constraint on the global coordinates and a sparsity constraint on the matrices. RESULTS: The performance of the proposed method was validated using numerical simulation data and in vivo proton-MRSI experimental data acquired on healthy volunteers at 3T. The results of the proposed method were compared with the QUEST method and the subspace-based method. In all the compared cases, the proposed method achieved superior performance over the QUEST and the subspace-based methods both qualitatively in terms of noise and artifacts in the estimated metabolite concentration maps, and quantitatively in terms of spectral quantification accuracy measured by normalized root mean square errors. CONCLUSION: Joint spectral quantification using linear tangent space alignment-based manifold learning improves the accuracy of MRSI spectral quantification.

B1 inhomogeneity-corrected T1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch-Siegert shift and magnetization transfer effects.

PURPOSE: To introduce a method of inducing Bloch-Siegert shift and magnetization Transfer Simultaneously (BTS) and demonstrate its utilization for measuring binary spin-bath model parameters free pool spin-lattice relaxation ( T 1 F $$ {T}_1^{\mathrm{F}} $$ ), macromolecular fraction ( f $$ f $$ ), magnetization exchange rate ( k F $$ {k}_{\mathrm{F}} $$ ) and local transmit field ( B 1 + $$ {B}_1^{+} $$ ). THEORY AND METHODS: Bloch-Siegert shift and magnetization transfer is simultaneously induced through the application of off-resonance irradiation in between excitation and acquisition of an RF-spoiled gradient-echo scheme. Applying the binary spin-bath model, an analytical signal equation is derived and verified through Bloch simulations. Monte Carlo simulations were performed to analyze the method's performance. The estimation of the binary spin-bath parameters with B 1 + $$ {B}_1^{+} $$ compensation was further investigated through experiments, both ex vivo and in vivo. RESULTS: Comparing BTS with existing methods, simulations showed that existing methods can significantly bias T 1 $$ {T}_1 $$ estimation when not accounting for transmit B 1 $$ {B}_1 $$ heterogeneity and MT effects that are present. Phantom experiments further showed that the degree of this bias increases with increasing macromolecular proton fraction. Multi-parameter fit results from an in vivo brain study generated values in agreement with previous literature. Based on these studies, we confirmed that BTS is a robust method for estimating the binary spin-bath parameters in macromolecule-rich environments, even in the presence of B 1 + $$ {B}_1^{+} $$ inhomogeneity. CONCLUSION: A method of estimating Bloch-Siegert shift and magnetization transfer effect has been developed and validated. Both simulations and experiments confirmed that BTS can estimate spin-bath parameters ( T 1 F $$ {T}_1^{\mathrm{F}} $$ , f $$ f $$ , k F $$ {k}_{\mathrm{F}} $$ ) that are free from B 1 + $$ {B}_1^{+} $$ bias.

Promoting Informed Decisions About Colorectal Cancer Screening in Older Adults (PRIMED Study): a Physician Cluster Randomized Trial.

BACKGROUND: For adults aged 76-85, guidelines recommend individualizing decision-making about whether to continue colorectal cancer (CRC) testing. These conversations can be challenging as they need to consider a patient's CRC risk, life expectancy, and preferences. OBJECTIVE: To promote shared decision-making (SDM) for CRC testing decisions for older adults. DESIGN: Two-arm, multi-site cluster randomized trial, assigning physicians to Intervention and Comparator arms. Patients were surveyed shortly after the visit to assess outcomes. Analyses were intention-to-treat. PARTICIPANTS AND SETTING: Primary care physicians affiliated with 5 academic and community hospital networks and their patients aged 76-85 who were due for CRC testing and had a visit during the study period. INTERVENTIONS: Intervention arm physicians completed a 2-h online course in SDM communication skills and received an electronic reminder of patients eligible for CRC testing shortly before the visit. Comparator arm received reminders only. MAIN MEASURES: The primary outcome was patient-reported SDM Process score (range 0-4 with higher scores indicating more SDM); secondary outcomes included patient-reported discussion of CRC screening, knowledge, intention, and satisfaction with the visit. KEY RESULTS: Sixty-seven physicians (Intervention n=34 and Comparator n=33) enrolled. Patient participants (n=466) were on average 79 years old, 50% with excellent or very good self-rated overall health, and 66% had one or more prior colonoscopies. Patients in the Intervention arm had higher SDM Process scores (adjusted mean difference 0.36 (95%CI (0.08, 0.64), p=0.01) than in the Comparator arm. More patients in the Intervention arm reported discussing CRC screening during the visit (72% vs. 60%, p=0.03) and had higher intention to follow through with their preferred approach (58.0% vs. 47.1, p=0.03). Knowledge scores and visit satisfaction did not differ significantly between arms. CONCLUSION: Physician training plus reminders were effective in increasing SDM and frequency of CRC testing discussions in an age group where SDM is essential. TRIAL REGISTRATION: The trial is registered on clinicaltrials.gov (NCT03959696).

Stakeholder Research Priorities to Promote Implementation of Shared Decision-Making for Lung Cancer Screening: An American Thoracic Society and Veterans Affairs Health Services Research and Development Statement.

Rationale: Shared decision-making (SDM) for lung cancer screening (LCS) is recommended in guidelines and required by Medicare, yet it is seldom achieved in practice. The best approach for implementing SDM for LCS remains unknown, and the 2021 U.S. Preventive Services Task Force calls for implementation research to increase uptake of SDM for LCS. Objectives: To develop a stakeholder-prioritized research agenda and recommended outcomes to advance implementation of SDM for LCS. Methods: The American Thoracic Society and VA Health Services Research and Development Service convened a multistakeholder committee with expertise in SDM, LCS, patient-centered care, and implementation science. During a virtual State of the Art conference, we reviewed evidence and identified research questions to address barriers to implementing SDM for LCS, as well as outcome constructs, which were refined by writing group members. Our committee (n = 34) then ranked research questions and SDM effectiveness outcomes by perceived importance in an online survey. Results: We present our committee's consensus on three topics important to implementing SDM for LCS: 1) foundational principles for the best practice of SDM for LCS; 2) stakeholder rankings of 22 implementation research questions; and 3) recommended outcomes, including Proctor's implementation outcomes and stakeholder rankings of SDM effectiveness outcomes for hybrid implementation-effectiveness studies. Our committee ranked questions that apply innovative implementation approaches to relieve primary care providers of the sole responsibility of SDM for LCS as highest priority. We rated effectiveness constructs that capture the patient experience of SDM as most important. Conclusions: This statement offers a stakeholder-prioritized research agenda and outcomes to advance implementation of SDM for LCS.

Perceptions and tolerance of uncertainty: relationship to trust in COVID-19 health information and vaccine hesitancy.

The COVID-19 crisis has exposed the public to considerable scientific uncertainty, which may promote vaccine hesitancy among individuals with lower tolerance of uncertainty. In a national sample of US adults in May-June 2020, we examined how both perceptions of uncertainty about COVID-19 and trait-level differences in tolerance of uncertainty arising from various sources (risk, ambiguity, and complexity) are related to vaccine hesitancy-related outcomes, including trust in COVID-19 information, COVID-19 vaccine intentions, and beliefs that COVID-19 vaccines should undergo a longer testing period before being released to the public. Overall, perceptions of COVID-19 uncertainty were not associated with trust in information, vaccine intentions, or beliefs about vaccine testing. However, higher tolerance of risk was associated with lower intentions to get vaccinated, and lower tolerance of ambiguity was associated with lower intentions to get vaccinated and preferring a longer period of vaccine testing. Critically, perceptions of COVID-19 uncertainty and trait-level tolerance for uncertainty also interacted as predicted, such that greater perceived COVID-19 uncertainty was more negatively associated with trust in COVID-19 information among individuals with lower tolerance for risk and ambiguity. Thus, although perceptions of uncertainty regarding COVID-19 may not reduce trust and vaccine hesitancy for all individuals, trait-level tolerance of uncertainty arising from various sources may have both direct and moderating effects on these outcomes. These findings can inform public health communication or other interventions to increase COVID-19 vaccination uptake.

First year junior doctors and medical uncertainty - a qualitative study. / LIS1-leger og medisinsk usikkerhet ­ en kvalitativ studie.

BACKGROUND: Few studies have investigated how doctors in Norway deal with medical uncertainty. The purpose of the study was to explore how first year junior doctors perceive and manage uncertainty in clinical practice. MATERIAL AND METHODS: Ten first year junior doctors at two hospitals in Norway were recruited for interviews following response pattern analysis from a mapping questionnaire. The interviews were analysed using systematic text condensation. RESULTS: The analysis revealed three main themes in the interviews: dealing with medical uncertainty, personal response to medical uncertainty, and working environment, feedback and preparation. Within all three thematic areas, the informants used the words 'certain/uncertain' and 'secure/insecure' interchangeably. INTEPRETATION: The first year junior doctors struggled with the inherent uncertainty of medicine and felt a marked sense of insecurity, particularly at the start of their training period. How the doctors were welcomed in the workplace and the feedback they were given were important factors. Their undergraduate medical education had not sufficiently prepared the first year junior doctors for how to deal with medical uncertainty in clinical practice.

Free-breathing 3D cardiac T1 mapping with transmit B1 correction at 3T.

PURPOSE: To develop a cardiac T1 mapping method for free-breathing 3D T1 mapping of the whole heart at 3 T with transmit B1 ( B1+ ) correction. METHODS: A free-breathing, electrocardiogram-gated inversion-recovery sequence with spoiled gradient-echo readout was developed and optimized for cardiac T1 mapping at 3 T. High-frame-rate dynamic images were reconstructed from sparse (k,t)-space data acquired along a stack-of-stars trajectory using a subspace-based method for accelerated imaging. Joint T1 and flip-angle estimation was performed in T1 mapping to improve its robustness to B1+ inhomogeneity. Subject-specific timing of data acquisition was used in the estimation to account for natural heart-rate variations during the imaging experiment. RESULTS: Simulations showed that accuracy and precision of T1 mapping can be improved with joint T1 and flip-angle estimation and optimized electrocardiogram-gated spoiled gradient echo-based inversion-recovery acquisition scheme. The phantom study showed good agreement between the T1 maps from the proposed method and the reference method. Three-dimensional cardiac T1 maps (40 slices) were obtained at a 1.9-mm in-plane and 4.5-mm through-plane spatial resolution from healthy subjects (n = 6) with an average imaging time of 14.2 ± 1.6 minutes (heartbeat rate: 64.2 ± 7.1 bpm), showing myocardial T1 values comparable to those obtained from modified Look-Locker inversion recovery. The proposed method generated B1+ maps with spatially smooth variation showing 21%-32% and 11%-15% variations across the septal-lateral and inferior-anterior regions of the myocardium in the left ventricle. CONCLUSION: The proposed method allows free-breathing 3D T1 mapping of the whole heart with transmit B1 correction in a practical imaging time.

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.

Pilot Study of an Encounter Decision Aid for Lung Cancer Screening.

The Centers for Medicare and Medicaid Services has mandated in-person shared decision-making (SDM) counseling with the use of one or more decision aids (DAs) prior to lung cancer screening. We developed a single-page, paper-based, encounter DA (EDA) to be used within a clinician-patient encounter for lung cancer screening and conducted a pre-post pilot intervention study to evaluate its feasibility and effects on patient decisional conflict. Patients referred to a pulmonary practice-based lung cancer screening program were surveyed before and after an SDM visit with a pulmonologist, who used the EDA to counsel the patient. Patient knowledge of the mortality benefit from screening and the frequency of abnormal screening test results was evaluated after the visit, while decisional conflict was measured before and after the visit using the Decisional Conflict Scale (DCS). Twenty-three patients participated (mean age = 65.8 years; 43% female; mean smoking history = 57.8 pack-years; 48% currently smoking). Following the visit, 28% of participants correctly understood the mortality benefit of lung cancer screening, while 82% understood the frequency of abnormal screening tests. The mean total DCS score decreased from 35.0 to 0.2 after the visit (p < 0.001). These data suggest that a single-page, paper-based EDA is feasible and potentially effective in reducing decision conflict when used within a SDM visit, although more research is needed to establish the independent effects of the EDA, and future efforts to promote SDM may need to devote greater attention to improving patient understanding of the mortality benefit of screening.

Patient Perspectives on the Risk-Based NLST Outcomes Tool for Lung Cancer Screening.

Researchers at the NCI have developed the Risk-Based NLST Outcomes Tool (RNOT), an online tool that calculates risk of lung cancer diagnosis and death with and without lung cancer screening, and false-positive risk estimates. This tool has the potential to facilitate shared decision making for screening. The objective of this study was to examine how current heavy and former smokers understand and respond to personalized risk estimates from the RNOT. Individuals who were eligible for lung cancer screening and were visiting Walter Reed National Military Medical Center were invited to participate in a semi-structured interview to assess their experiences with and perceptions of the RNOT. Results were analyzed using template analysis. Participants found their risk of lung cancer death to be lower than anticipated and were confused by changes in risk for lung cancer diagnosis with and without screening. Most participants indicated that the RNOT would be helpful in making screening decisions, despite reporting that there was no maximum risk for a false positive that would lead them to forgo lung cancer screening. Participants provided actionable needs and recommendations to optimize this tool. Risk-based screening tools may enhance shared decision making. The RNOT is being updated to incorporate these findings.

Impact of health literacy on shared decision making for prostate-specific antigen screening in the United States.

BACKGROUND: Current guidelines endorse shared decision making (SDM) for prostate-specific antigen (PSA) screening. The relationship between a patient's health literacy (HL) and SDM remains unclear. In the current study, the authors sought to identify the impact of HL on the rates of PSA screening and on the relationship between HL and SDM following the 2012 US Preventive Services Task Force recommendations against PSA screening. METHODS: Using data from the 2016 Behavioral Risk Factor Surveillance System, the authors examined PSA screening in the 13 states that administered the optional "Health Literacy" module. Men aged ≥50 years were examined. Complex samples multivariable logistic regression models were computed to assess the odds of undergoing PSA screening. The interactions between HL and SDM were also examined. RESULTS: A weighted sample of 12.249 million men with a rate of PSA screening of 33.4% were identified. Approximately one-third self-identified as having optimal HL. Rates of PSA screening were found to be highest amongst the highest HL group (42.2%). Being in this group was a significant predictor of undergoing PSA screening (odds ratio, 1.214; 95% confidence interval, 1.051-1.403). There was a significant interaction observed between HL and SDM (P for interaction, <.001) such that higher HL was associated with a lower likelihood of undergoing PSA screening when SDM was present. CONCLUSIONS: In the uncertain environment of multiple contradictory screening guidelines, men who reported higher levels of HL were found to have higher levels of screening. The authors demonstrated that increased HL may reduce the screening-promoting effect of SDM. These findings highlight the dynamic interplay between HL and SDM that should inform the creation and promulgation of SDM guidelines, specifically when considering patients with low HL.

National Recommendations against Prostate Specific Antigen Screening versus Statewide Medicaid Expansion Initiatives: A Battle of the Titans.

PURPOSE: Medicaid expansion under the Patient Protection and Affordable Care Act occurred almost concurrently with 2012 U.S. Preventive Services Task Force recommendations against prostate specific antigen screening. Here the relative influence on prostate specific antigen screening rates by 2 concurrent and opposing system-level policy initiatives is investigated: improved access to care and change in clinical practice guidelines. MATERIALS AND METHODS: Behavioral Risk Factor Surveillance System data from years 2012 to 2018 were analyzed for trends in self-reported prostate specific antigen screening and insurance coverage. Subanalyses included state Medicaid expansion status and respondent federal poverty level. Multivariable logistic regression was performed to evaluate factors associated with prostate specific antigen screening. RESULTS: From 2012 to 2018 prostate specific antigen screening predominantly declined with a notable exception of an increase of 7.3% for men at <138% federal poverty level between 2011 and 2013 in early expansion states. Initial increases did not continue, and screening trends mirrored those of nonexpansion states by 2018. Notably, 2014 planned expansions states did not follow this trend with minimal change between 2015 and 2017 compared to declines in early expansion states and nonexpansion states (-0.4% vs -6.7% and -8.6%, respectively). CONCLUSIONS: Medicaid expansion was associated with increased rates of insured men at <138% federal poverty level from 2012 to 2018 in early expansion states. In this group, initial increases in prostate specific antigen screening were not durable and followed the trend of reduced screening seen across the United States. In planned expansions states the global drop in prostate specific antigen screening from 2016 to 2018 was offset in men at <138% federal poverty level by expanding access to care. Nonexpansion states showed a steady decline in prostate specific antigen screening rates. This suggests that policy such as U.S. Preventive Services Task Force recommendations against screening competes with and often outmatches access to care.

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.

PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging. METHODS: Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy. RESULTS: The Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices. CONCLUSION: Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.

In vivo quantitative mapping of human mitochondrial cardiac membrane potential: a feasibility study.

PURPOSE: Alteration in mitochondrial membrane potential (ΔΨm) is an important feature of many pathologic processes, including heart failure, cardiotoxicity, ventricular arrhythmia, and myocardial hypertrophy. We present the first in vivo, non-invasive, assessment of regional ΔΨm in the myocardium of normal human subjects. METHODS: Thirteen healthy subjects were imaged using [18F]-triphenylphosphonium ([18F]TPP+) on a PET/MR scanner. The imaging protocol consisted of a bolus injection of 300 MBq followed by a 120-min infusion of 0.6 MBq/min. A 60 min, dynamic PET acquisition was started 1 h after bolus injection. The extracellular space fraction (fECS) was simultaneously measured using MR T1-mapping images acquired at baseline and 15 min after gadolinium injection with correction for the subject's hematocrit level. Serial venous blood samples were obtained to calculate the plasma tracer concentration. The tissue membrane potential (ΔΨT), a proxy of ΔΨm, was calculated from the myocardial tracer concentration at secular equilibrium, blood concentration, and fECS measurements using a model based on the Nernst equation. RESULTS: In 13 healthy subjects, average tissue membrane potential (ΔΨT), representing the sum of cellular membrane potential (ΔΨc) and ΔΨm, was - 160.7 ± 3.7 mV, in excellent agreement with previous in vitro assessment. CONCLUSION: In vivo quantification of the mitochondrial function has the potential to provide new diagnostic and prognostic information for several cardiac diseases as well as allowing therapy monitoring. This feasibility study lays the foundation for further investigations to assess these potential roles. Clinical trial identifier: NCT03265431.

How Well Does the Surprise Question Predict 1-year Mortality for Patients Admitted with COPD?

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often receive burdensome care at end-of-life (EOL) and infrequently complete advance care planning (ACP). The surprise question (SQ) is a prognostic tool that may facilitate ACP. OBJECTIVE: To assess how well the SQ predicts mortality and prompts ACP for COPD patients. DESIGN: Retrospective cohort study. SUBJECTS: Patients admitted to the hospital for an acute exacerbation of COPD between July 2015 and September 2018. MAIN MEASURES: Emergency department (ED) and inpatient clinicians answered, "Would you be surprised if this patient died in the next 30 days (ED)/one year (inpatient)?" The primary outcome measure was the accuracy of the SQ in predicting 30-day and 1-year mortality. The secondary outcome was the correlation between SQ and ACP (palliative care consultation, documented goals-of-care conversation, change in code status, or completion of ACP document). KEY RESULTS: The 30-day SQ had a high specificity but low sensitivity for predicting 30-day mortality: sensitivity 12%, specificity 95%, PPV 11%, and NPV 96%. The 1-year SQ demonstrated better accuracy for predicting 1-year mortality: sensitivity 47%, specificity 75%, PPV 35%, and NPV 83%. After multivariable adjustment for age, sex, and prior 6-month admissions, 1-year SQ+ responses were associated with greater odds of 1-year mortality (OR 2.38, 95% CI 1.39-4.08) versus SQ-. One-year SQ+ patients were more likely to have a goals-of-care conversation (25% vs. 11%, p < 0.01) and complete an advance directive or POLST (46% vs. 23%, p < 0.01). After multivariable adjustment, SQ+ responses to the 1-year SQ were associated with greater odds of ACP receipt (OR 2.67, 95% CI 1.64-4.36). CONCLUSIONS: The 1-year surprise question may be an effective component of prognostication and advance care planning for COPD patients in the inpatient setting.

Community oncologists' perceptions and utilization of large-panel genomic tumor testing.

PURPOSE: Large-panel genomic tumor testing (GTT) is an emerging technology with great promise but uncertain clinical value. Previous research has documented variability in academic oncologists' perceptions and use of GTT, but little is known about community oncologists' perceptions of GTT and how perceptions relate to clinicians' intentions to use GTT. METHODS: Community oncology physicians (N = 58) participating in a statewide initiative aimed at improving access to large-panel GTT completed surveys assessing their confidence in using GTT, attitudes regarding the value of GTT, perceptions of barriers to GTT implementation, and future intentions to use GTTs. Descriptive and multivariable regression analyses were conducted to characterize these perceptions and to explore the relationships between them. RESULTS: There was substantial variability in clinicians' perceptions of GTT. Clinicians generally had moderate confidence in their ability to use GTT, but lower confidence in patients' ability to understand test results and access targeted treatment. Clinicians had positive attitudes regarding the value of GTT. Clinicians' future intentions to use GTT were associated with greater confidence in using GTT and greater perceived barriers to implementing GTT, but not with attitudes about the value of GTT. CONCLUSIONS: Community oncologists' perceptions of large-panel genomic tumor testing are variable, and their future intentions to use GTT are associated with both their confidence in and perceived barriers to its use, but not with their attitudes towards GTT. More research is needed to understand other factors that determine how oncologists perceive and use GTT in clinical practice.

A scoping review of practice recommendations for clinicians' communication of uncertainty.

BACKGROUND: Health-care providers increasingly have to discuss uncertainty with patients. Awareness of uncertainty can affect patients variably, depending on how it is communicated. To date, no overview existed for health-care professionals on how to discuss uncertainty. OBJECTIVE: To generate an overview of available recommendations on how to communicate uncertainty with patients during clinical encounters. SEARCH STRATEGY: A scoping review was conducted. Four databases were searched following the PRISMA-ScR statement. Independent screening by two researchers was performed of titles and abstracts, and subsequently full texts. INCLUSION CRITERIA: Any (non-)empirical papers were included describing recommendations for any health-care provider on how to orally communicate uncertainty to patients. DATA EXTRACTION: Data on provided recommendations and their characteristics (eg, target group and strength of evidence base) were extracted. Recommendations were narratively synthesized into a comprehensible overview for clinical practice. RESULTS: Forty-seven publications were included. Recommendations were based on empirical findings in 23 publications. After narrative synthesis, 13 recommendations emerged pertaining to three overarching goals: (a) preparing for the discussion of uncertainty, (b) informing patients about uncertainty and (c) helping patients deal with uncertainty. DISCUSSION AND CONCLUSIONS: A variety of recommendations on how to orally communicate uncertainty are available, but most lack an evidence base. More substantial research is needed to assess the effects of the suggested communicative approaches. Until then, health-care providers may use our overview of communication strategies as a toolbox to optimize communication about uncertainty with patients. PATIENT OR PUBLIC CONTRIBUTION: Results were presented to stakeholders (physicians) to check and improve their practical applicability.