Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Amyloid Positron Emission Tomography and Subsequent Health Care Use Among Medicare Beneficiaries With Mild Cognitive Impairment or Dementia.

Importance: Results of amyloid positron emission tomography (PET) have been shown to change the management of patients with mild cognitive impairment (MCI) or dementia who meet Appropriate Use Criteria (AUC). Objective: To determine if amyloid PET is associated with reduced hospitalizations and emergency department (ED) visits over 12 months in patients with MCI or dementia. Design, Setting, and Participants: This nonrandomized controlled trial analyzed participants in the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) study, an open-label, multisite, longitudinal study that enrolled participants between February 2016 and December 2017 and followed up through December 2018. These participants were recruited at 595 clinical sites that provide specialty memory care across the US. Eligible participants were Medicare beneficiaries 65 years or older with a diagnosis of MCI or dementia within the past 24 months who met published AUC for amyloid PET. Each IDEAS study participant was matched to a control Medicare beneficiary who had not undergone amyloid PET. Data analysis was conducted on December 13, 2022. Exposure: Participants underwent amyloid PET at imaging centers. Main Outcomes and Measures: The primary end points were the proportions of patients with 12-month inpatient hospital admissions and ED visits. One of 4 secondary end points was the rate of hospitalizations and rate of ED visits in participants with positive vs negative amyloid PET results. Health care use was ascertained from Medicare claims data. Results: The 2 cohorts (IDEAS study participants and controls) each comprised 12 684 adults, including 6467 females (51.0%) with a median (IQR) age of 77 (73-81) years. Over 12 months, 24.0% of the IDEAS study participants were hospitalized, compared with 25.1% of the matched control cohort, for a relative reduction of -4.49% (97.5% CI, -9.09% to 0.34%). The 12-month ED visit rates were nearly identical between the 2 cohorts (44.8% in both IDEAS study and control cohorts) for a relative reduction of -0.12% (97.5% CI, -3.19% to 3.05%). Both outcomes fell short of the prespecified effect size of 10% or greater relative reduction. Overall, 1467 of 6848 participants (21.4%) with positive amyloid PET scans were hospitalized within 12 months compared with 1081 of 4209 participants (25.7%) with negative amyloid PET scans (adjusted odds ratio, 0.83; 95% CI, 0.78-0.89). Conclusions and Relevance: Results of this nonrandomized controlled trial showed that use of amyloid PET was not associated with a significant reduction in 12-month hospitalizations or ED visits. Rates of hospitalization were lower in patients with positive vs negative amyloid PET results.

Impact of Muscle Measures on Outcome in Patients Receiving Endocrine Therapy for Metastatic Breast Cancer: Analysis of ECOG-ACRIN E2112.

BACKGROUND: Observational data investigating the relationship between body habitus and outcomes in breast cancer have been variable and inconsistent, largely centered in the curative setting and focused on weight-based metrics. This study evaluated the impact of muscle measures on outcomes in patients with metastatic breast cancer receiving endocrine-based therapy. METHODS: Baseline CT scans were collected from ECOG-ACRIN E2112, a randomized phase III placebo-controlled study of exemestane with or without entinostat. A CT cross-sectional image at the L3 level was extracted to obtain skeletal muscle mass and attenuation. Low muscle mass (LMM) was defined as skeletal muscle index <41 cm2/m2 and low muscle attenuation (LMA) as muscle density <25 HU or <33 HU if overweight/obese by body mass index (BMI). Multivariable Cox proportional hazard models determined the association between LMM or LMA and progression-free survival (PFS) and overall survival (OS). Correlations between LMM, LMA, and patient-reported outcomes were determined using 2-sample t tests. RESULTS: Analyzable CT scans and follow-up data were available for 540 of 608 patients. LMM was present in 39% (n=212) of patients and LMA in 56% (n=301). Those with LMA were more likely to have obesity and worse performance status. LMM was not associated with survival (PFS hazard ratio [HR]: 1.13, P=.23; OS HR: 1.05, P=.68), nor was LMA (PFS HR: 1.01, P=.93; OS HR: 1.00, P=.99). BMI was not associated with survival. LMA, but not LMM, was associated with increased frequency of patient-reported muscle aches. CONCLUSIONS: Both low muscle mass and density are prevalent in patients with hormone receptor-positive metastatic breast cancer. Muscle measures correlated with obesity and performance status; however, neither muscle mass nor attenuation were associated with prognosis. Further work is needed to refine body composition measurements and select optimal cutoffs with meaningful endpoints in specific breast cancer populations, particularly those living with metastatic disease.

Utilization of Diagnostic Procedures After Lung Cancer Screening in the National Lung Screening Trial.

OBJECTIVE: To examine utilization patterns of diagnostic procedures after lung cancer screening among participants enrolled in the National Lung Screening Trial. METHODS: Using a sample of National Lung Screening Trial participants with abstracted medical records, we assessed utilization of imaging, invasive, and surgical procedures after lung cancer screening. Missing data were imputed using multiple imputation by chained equations. For each procedure type, we examined utilization within a year after the screening or until the next screen, whichever came first, across arms (low-dose CT [LDCT] versus chest X-ray [CXR]) and by screening results. We also explored factors associated with having these procedures using multivariable negative binomial regressions. RESULTS: After baseline screening, our sample had 176.5 and 46.7 procedures per 100 person-years for those with a false-positive and negative result, respectively. Invasive and surgical procedures were relatively infrequent. Among those who screened positive, follow-up imaging and invasive procedures were 25% and 34% less frequent in those screened with LDCT, compared with CXR. Postscreening utilization of invasive and surgical procedures was 37% and 34% lower at the first incidence screen compared with baseline. Participants with positive results at baseline were six times more likely to undergo additional imaging than those with normal findings. DISCUSSION: Use of imaging and invasive procedures to evaluate abnormal findings varied by screening modality, with a lower rate for LDCT than CXR. Invasive and surgical workup were less prevalent after subsequent screening examinations compared with baseline screening. Utilization was associated with older age but not gender, race or ethnicity, insurance status, or income.

Survival analysis using deep learning with medical imaging.

There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed. We provide an overview of deep learning methods for time-to-event outcomes and compare several deep learning methods to Cox model based methods through the analysis of a histology dataset of gliomas.

Role of calibration in uncertainty-based referral for deep learning.

The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical evaluation of the role of model calibration in uncertainty-based referral, an approach that prioritizes referral of observations based on the magnitude of a measure of uncertainty. We consider several configurations of network architecture, methods for uncertainty estimation, and training data size. We identify a strong relationship between the effectiveness of uncertainty-based referral and having a well-calibrated model. This is especially relevant as complex deep neural networks tend to have high calibration errors. Finally, we show that post-calibration of the neural network helps uncertainty-based referral with identifying hard-to-classify observations.

Estimating the area under the ROC curve when transporting a prediction model to a target population.

We propose methods for estimating the area under the receiver operating characteristic (ROC) curve (AUC) of a prediction model in a target population that differs from the source population that provided the data used for original model development. If covariates that are associated with model performance, as measured by the AUC, have a different distribution in the source and target populations, then AUC estimators that only use data from the source population will not reflect model performance in the target population. Here, we provide identification results for the AUC in the target population when outcome and covariate data are available from the sample of the source population, but only covariate data are available from the sample of the target population. In this setting, we propose three estimators for the AUC in the target population and show that they are consistent and asymptotically normal. We evaluate the finite-sample performance of the estimators using simulations and use them to estimate the AUC in a nationally representative target population from the National Health and Nutrition Examination Survey for a lung cancer risk prediction model developed using source population data from the National Lung Screening Trial.

Transporting a Prediction Model for Use in a New Target Population.

We considered methods for transporting a prediction model for use in a new target population, both when outcome and covariate data for model development are available from a source population that has a different covariate distribution compared with the target population and when covariate data (but not outcome data) are available from the target population. We discuss how to tailor the prediction model to account for differences in the data distribution between the source population and the target population. We also discuss how to assess the model's performance (e.g., by estimating the mean squared prediction error) in the target population. We provide identifiability results for measures of model performance in the target population for a potentially misspecified prediction model under a sampling design where the source and the target population samples are obtained separately. We introduce the concept of prediction error modifiers that can be used to reason about tailoring measures of model performance to the target population. We illustrate the methods in simulated data and apply them to transport a prediction model for lung cancer diagnosis from the National Lung Screening Trial to the nationally representative target population of trial-eligible individuals in the National Health and Nutrition Examination Survey.

Racial and Ethnic Differences in Amyloid PET Positivity in Individuals With Mild Cognitive Impairment or Dementia: A Secondary Analysis of the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) Cohort Study.

Importance: Racial and ethnic groups with higher rates of clinical Alzheimer disease (AD) are underrepresented in studies of AD biomarkers, including amyloid positron emission tomography (PET). Objective: To compare amyloid PET positivity among a diverse cohort of individuals with mild cognitive impairment (MCI) or dementia. Design, Setting, and Participants: Secondary analysis of the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS), a single-arm multisite cohort study of Medicare beneficiaries who met appropriate-use criteria for amyloid PET imaging between February 2016 and September 2017 with follow-up through January 2018. Data were analyzed between April 2020 and January 2022. This study used 2 approaches: the McNemar test to compare amyloid PET positivity proportions between matched racial and ethnic groups and multivariable logistic regression to assess the odds of having a positive amyloid PET scan. IDEAS enrolled participants at 595 US dementia specialist practices. A total of 21 949 were enrolled and 4842 (22%) were excluded from the present analysis due to protocol violations, not receiving an amyloid PET scan, not having a positive or negative scan, or because of small numbers in some subgroups. Exposures: In the IDEAS study, participants underwent a single amyloid PET scan. Main Outcomes and Measures: The main outcomes were amyloid PET positivity proportions and odds. Results: Data from 17 107 individuals (321 Asian, 635 Black, 829 Hispanic, and 15 322 White) with MCI or dementia and amyloid PET were analyzed between April 2020 and January 2022. The median (range) age of participants was 75 (65-105) years; 8769 participants (51.3%) were female and 8338 (48.7%) were male. In the optimal 1:1 matching analysis (n = 3154), White participants had a greater proportion of positive amyloid PET scans compared with Asian participants (181 of 313; 57.8%; 95% CI, 52.3-63.2 vs 142 of 313; 45.4%; 95% CI, 39.9-50.9, respectively; P = .001) and Hispanic participants (482 of 780; 61.8%; 95% CI, 58.3-65.1 vs 425 of 780; 54.5%; 95% CI, 51.0-58.0, respectively; P = .003) but not Black participants (359 of 615; 58.4%; 95% CI, 54.4-62.2 vs 333 of 615; 54.1%; 95% CI, 50.2-58.0, respectively; P = .13). In the adjusted model, the odds of having a positive amyloid PET scan were lower for Asian participants (odds ratio [OR], 0.47; 95% CI, 0.37-0.59; P < .001), Black participants (OR, 0.71; 95% CI, 0.60-0.84; P < .001), and Hispanic participants (OR, 0.68; 95% CI, 0.59-0.79; P < .001) compared with White participants. Conclusions and Relevance: Racial and ethnic differences found in amyloid PET positivity among individuals with MCI and dementia in this study may indicate differences in underlying etiology of cognitive impairment and guide future treatment and prevention approaches.

Association Between Surgery Preference and Receipt in Ductal Carcinoma In Situ After Breast Magnetic Resonance Imaging: An Ancillary Study of the ECOG-ACRIN Cancer Research Group (E4112).

Importance: Guiding treatment decisions for women with ductal carcinoma in situ (DCIS) requires understanding patient preferences and the influence of preoperative magnetic resonance imaging (MRI) and surgeon recommendation. Objective: To identify factors associated with surgery preference and surgery receipt among a prospective cohort of women with newly diagnosed DCIS. Design, Setting, and Participants: A prospective cohort study was conducted at 75 participating institutions, including community practices and academic centers, across the US between March 25, 2015, and April 27, 2016. Data were analyzed from August 2 to September 24, 2021. This was an ancillary study of the ECOG-ACRIN Cancer Research Group (E4112). Women with recently diagnosed unilateral DCIS who were eligible for wide local excision and had a diagnostic mammogram within 3 months of study registration were included. Participants who had documented surgery and completed the baseline patient-reported outcome questionnaires were included in this substudy. Exposures: Women received preoperative MRI and surgeon consultation and then underwent wide local excision or mastectomy. Participants will be followed up for recurrence and overall survival for 10 years from the date of surgery. Main Outcomes and Measures: Patient-reported outcome questionnaires assessed treatment goals and concerns and surgery preference before MRI and after MRI and surgeon consultation. Results: Of the 368 participants enrolled 316 (86%) were included in this substudy (median [range] age, 59.5 [34-87] years; 45 women [14%] were Black; 245 [78%] were White; and 26 [8%] were of other race). Pre-MRI, age (odds ratio [OR] per 5-year increment, 0.45; 95% CI, 0.26-0.80; P = .007) and the importance of keeping one's breast (OR, 0.48; 95% CI, 0.31-0.72; P < .001) vs removal of the breast for peace of mind (OR, 1.35; 95% CI, 1.04-1.76; P = .03) were associated with surgery preference for mastectomy. After MRI and surgeon consultation, MRI upstaging (48 of 316 [15%]) was associated with patient preference for mastectomy (OR, 8.09; 95% CI, 2.51-26.06; P < .001). The 2 variables with the highest ORs for initial receipt of mastectomy were MRI upstaging (OR, 12.08; 95% CI, 4.34-33.61; P < .001) and surgeon recommendation (OR, 4.85; 95% CI, 1.99-11.83; P < .001). Conclusions and Relevance: In this cohort study, change in patient preference for DCIS surgery and surgery received were responsive to MRI results and surgeon recommendation. These data highlight the importance of ensuring adequate information and ongoing communication about the clinical significance of MRI findings and the benefits and risks of available treatment options.

Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment.

Importance: The diagnostic evaluation for Alzheimer disease may be improved by a blood-based diagnostic test identifying presence of brain amyloid plaque pathology. Objective: To determine the clinical performance associated with a diagnostic algorithm incorporating plasma amyloid-ß (Aß) 42:40 ratio, patient age, and apoE proteotype to identify brain amyloid status. Design, Setting, and Participants: This cohort study includes analysis from 2 independent cross-sectional cohort studies: the discovery cohort of the Plasma Test for Amyloidosis Risk Screening (PARIS) study, a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, including 249 patients from 2018 to 2019, and MissionAD, a dataset of 437 biobanked patient samples obtained at screenings during 2016 to 2019. Data were analyzed from May to November 2020. Exposures: Amyloid detected in blood and by positron emission tomography (PET) imaging. Main Outcomes and Measures: The main outcome was the diagnostic performance of plasma Aß42:40 ratio, together with apoE proteotype and age, for identifying amyloid PET status, assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: All 686 participants (mean [SD] age 73.2 [6.3] years; 368 [53.6%] men; 378 participants [55.1%] with amyloid PET findings) had symptoms of mild cognitive impairment or mild dementia. The AUC of plasma Aß42:40 ratio for PARIS was 0.79 (95% CI, 0.73-0.85) and 0.86 (95% CI, 0.82-0.89) for MissionAD. Ratio cutoffs for Aß42:40 based on the Youden index were similar between cohorts (PARIS: 0.089; MissionAD: 0.092). A logistic regression model (LRM) incorporating Aß42:40 ratio, apoE proteotype, and age improved diagnostic performance within each cohort (PARIS: AUC, 0.86 [95% CI, 0.81-0.91]; MissionAD: AUC, 0.89 [95% CI, 0.86-0.92]), and overall accuracy was 78% (95% CI, 72%-83%) for PARIS and 83% (95% CI, 79%-86%) for MissionAD. The model developed on the prospectively collected samples from PARIS performed well on the MissionAD samples (AUC, 0.88 [95% CI, 0.84-0.91]; accuracy, 78% [95% CI, 74%-82%]). Training the LRM on combined cohorts yielded an AUC of 0.88 (95% CI, 0.85-0.91) and accuracy of 81% (95% CI, 78%-84%). The output of this LRM is the Amyloid Probability Score (APS). For clinical use, 2 APS cutoff values were established yielding 3 categories, with low, intermediate, and high likelihood of brain amyloid plaque pathology. Conclusions and Relevance: These findings suggest that this blood biomarker test could allow for distinguishing individuals with brain amyloid-positive PET findings from individuals with amyloid-negative PET findings and serve as an aid for Alzheimer disease diagnosis.

rPOP: Robust PET-only processing of community acquired heterogeneous amyloid-PET data.

The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers (18F-florbetapir/FBP, 18F-florbetaben/FBB or 18F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia-Evidence for Amyloid Scanning - Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer's Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of >20/>18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen's k = 0.72 [0.7-0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87-0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R2:0.95, p<0.001), with this association being significantly modulated by estimated PET resolution (ß= -0.016, p<0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPOP.

Patient-Reported Testing Burden of Breast Magnetic Resonance Imaging Among Women With Ductal Carcinoma In Situ: An Ancillary Study of the ECOG-ACRIN Cancer Research Group (E4112).

Importance: The use of magnetic resonance imaging (MRI) in pretreatment planning of ductal carcinoma in situ (DCIS) remains controversial. Understanding changes in short-term health-related quality of life associated with breast MRI would allow for a more complete comparative effectiveness assessment. Objective: To assess whether there are changes in patient-reported quality of life associated with breast MRI among women diagnosed with DCIS. Design, Setting, and Participants: This cohort study was a substudy of a nonrandomized clinical trial conducted at 75 participating US institutions from March 2015 to April 2016. Women recently diagnosed with unilateral DCIS who were eligible for wide local excision and had a diagnostic mammogram within 3 months of study registration were included. A total of 355 women met the eligibility criteria and underwent the study MRI. Data analysis was performed from June 3, 2020, to July 1, 2021. Exposures: Participants underwent bilateral breast MRI within 30 days of study registration and before surgery. Information on patient-reported testing burden for breast MRI was collected after MRI and before surgery. Main Outcomes and Measures: The primary outcome of this substudy was the patient-reported testing burden of breast MRI, measured by the Testing Morbidities Index (TMI) summated scale score. The TMI is a 7-item instrument that evaluates the temporary changes in quality of life associated with imaging before, during, and after the test (0 represents the worst possible, 100 the hypothetical ideal test experience). Results: Of the 355 women who met the eligibility criteria, 244 (69%) completed both questionnaires and were included in this analysis. The median age was 59 years (range, 34-85 years). The mean MRI TMI summated scale score was 85.9 (95% CI, 84.6-87.3). Of the 244 women, 142 (58%) experienced at least some fear and anxiety before the examination, and 120 women (49%) experienced fear and anxiety during the examination. A total of 156 women (64%) experienced pain or discomfort during the examination. In multivariable analyses, greater test-related burden was associated with higher levels of cancer worry (regression coefficient, -2.75; SE, 0.94; P = .004). Conclusions and Relevance: In this cohort study, a clinically meaningful breast MRI testing burden among women with DCIS was revealed that was significantly associated with cancer worry. Understanding the potential quality-of-life reduction associated with MRI, especially when used in combination with mammography, may allow development of targeted interventions to improve the patient experience.

Preoperative Breast MRI for Newly Diagnosed Ductal Carcinoma in Situ: Imaging Features and Performance in a Multicenter Setting (ECOG-ACRIN E4112 Trial).

Background There are limited data from clinical trials describing preoperative MRI features and performance in the evaluation of mammographically detected ductal carcinoma in situ (DCIS). Purpose To report qualitative MRI features of DCIS, MRI performance in the identification of additional disease, and associations of imaging features with pathologic, genomic, and surgical outcomes from the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network (ECOG-ACRIN) E4112 trial. Materials and Methods Secondary analyses of a multicenter prospective clinical trial from the ECOG-ACRIN Cancer Research Group included women with DCIS diagnosed with conventional imaging techniques (mammography and US), confirmed via core-needle biopsy (CNB), and enrolled between March 2015 and April 2016 who were candidates for wide local excision (WLE) based on conventional imaging and clinical examination results. DCIS MRI features and pathologic features from CNB and excision were recorded. Each woman without invasive upgrade of the index DCIS at WLE received a 12-gene DCIS score. MRI performance metrics were calculated. Associations of imaging features with invasive upgrade, dichotomized DCIS score (<39 vs ≥39), and single WLE success were estimated in uni- and multivariable analyses. Results Among 339 women (median age, 60 years; interquartile range, 51-66 years), most DCIS cases showed nonmass enhancement (NME) (195 of 339 [58%]) on MRI scans with larger median size than on mammograms (19 mm vs 12 mm; P < .001). Positive predictive value of MRI-prompted CNBs was 32% (21 of 66) (95% CI: 22, 44), yielding an additional cancer detection rate of 6.2% (21 of 339) (95% CI: 4.1, 9.3). MRI false-positive rate was 14.2% (45 of 318) (95% CI: 10.7, 18.4). No imaging features were associated with invasive upgrade or DCIS score (P = .05 to P = .95). Smaller size and focal NME distribution at MRI were linked to single WLE success (P < .001). Conclusion Preoperative MRI depicted ductal carcinoma in situ (DCIS) diagnosed with conventional imaging most commonly as nonmass enhancement, with larger median span than mammography, and additional cancer detection rate of 6.2%. MRI features of this subset of DCIS did not enable prediction of pathologic or genomic outcomes. Clinical trial registration no. NCT02352883 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Kuhl in this issue. An earlier incorrect version of this article appeared online. This article was corrected on August 4, 2021.

Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis.

Calibration of a microsimulation model (MSM) is a challenging but crucial step for the development of a valid model. Numerous calibration methods for MSMs have been suggested in the literature, most of which are usually adjusted to the specific needs of the model and based on subjective criteria for the selection of optimal parameter values. This article compares 2 general approaches for calibrating MSMs used in medical decision making, a Bayesian and an empirical approach. We use as a tool the MIcrosimulation Lung Cancer (MILC) model, a streamlined, continuous-time, dynamic MSM that describes the natural history of lung cancer and predicts individual trajectories accounting for age, sex, and smoking habits. We apply both methods to calibrate MILC to observed lung cancer incidence rates from the Surveillance, Epidemiology and End Results (SEER) database. We compare the results from the 2 methods in terms of the resulting parameter distributions, model predictions, and efficiency. Although the empirical method proves more practical, producing similar results with smaller computational effort, the Bayesian method resulted in a calibrated model that produced more accurate outputs for rare events and is based on a well-defined theoretical framework for the evaluation and interpretation of the calibration outcomes. A combination of the 2 approaches is an alternative worth considering for calibrating complex predictive models, such as microsimulation models.

The Longitudinal Early-onset Alzheimer's Disease Study (LEADS): Framework and methodology.

Patients with early-onset Alzheimer's disease (EOAD) are commonly excluded from large-scale observational and therapeutic studies due to their young age, atypical presentation, or absence of pathogenic mutations. The goals of the Longitudinal EOAD Study (LEADS) are to (1) define the clinical, imaging, and fluid biomarker characteristics of EOAD; (2) develop sensitive cognitive and biomarker measures for future clinical and research use; and (3) establish a trial-ready network. LEADS will follow 400 amyloid beta (Aß)-positive EOAD, 200 Aß-negative EOnonAD that meet National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) or AD dementia, and 100 age-matched controls. Participants will undergo clinical and cognitive assessments, magnetic resonance imaging (MRI), [18 F]Florbetaben and [18 F]Flortaucipir positron emission tomography (PET), lumbar puncture, and blood draw for DNA, RNA, plasma, serum and peripheral blood mononuclear cells, and post-mortem assessment. To develop more effective AD treatments, scientists need to understand the genetic, biological, and clinical processes involved in EOAD. LEADS will develop a public resource that will enable future planning and implementation of EOAD clinical trials.