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.

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.

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.

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.

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.

Comparing the clinical impact of pancreatic cyst surveillance programs: A trial of the ECOG-ACRIN cancer research group (EA2185).

BACKGROUND: The optimal surveillance strategy for pancreatic cysts, which occur in up to 20% of the adult population, is ill defined. The risk of malignant degeneration of these cysts is low, however the morbidity and mortality associated with pancreatic cancer are high. Two clinical surveillance guidelines are in regular use. Both the Fukuoka and American Gastroenterological Association (AGA) guidelines rely on radiographic and endoscopic imaging. They differ primarily in their recommended frequencies of interval surveillance imaging. While evidence driven clinical guidelines should promote higher quality care, competing guidelines on the same topic may provide discordant recommendations and potential reduction in the quality and/or value of care. OBJECTIVES: The primary objective is to compare the clinical effectiveness of the two surveillance guidelines to identify patients most likely to benefit from pancreatic resection. Secondary objectives include comparison of resource utilization, patient reported outcomes, incidental findings are other clinical outcomes. METHODS: 4606 asymptomatic patients with newly identified pancreatic cysts ≥1 cm in diameter will be randomized 1:1 to high intensity (Fukuoka) or low intensity (AGA) surveillance. All participants will be followed prospectively for 5 years. CONCLUSION: Differing guidelines confuse providers, patients and policymakers. This large, prospective, randomized trial will compare the clinical effectiveness and resource allocation requirements of two guidelines addressing a common clinical entity. CLINICALTRIALS. GOV IDENTIFIER: NCT04239573.

Clinical Application of Computational Methods in Precision Oncology: A Review.

Importance: There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective: To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review: The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings: Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance: Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening.

Importance: Improved screening methods for women with dense breasts are needed because of their increased risk of breast cancer and of failed early diagnosis by screening mammography. Objective: To compare the screening performance of abbreviated breast magnetic resonance imaging (MRI) and digital breast tomosynthesis (DBT) in women with dense breasts. Design, Setting, and Participants: Cross-sectional study with longitudinal follow-up at 48 academic, community hospital, and private practice sites in the United States and Germany, conducted between December 2016 and November 2017 among average-risk women aged 40 to 75 years with heterogeneously dense or extremely dense breasts undergoing routine screening. Follow-up ascertainment of cancer diagnoses was complete through September 12, 2019. Exposures: All women underwent screening by both DBT and abbreviated breast MRI, performed in randomized order and read independently to avoid interpretation bias. Main Outcomes and Measures: The primary end point was the invasive cancer detection rate. Secondary outcomes included sensitivity, specificity, additional imaging recommendation rate, and positive predictive value (PPV) of biopsy, using invasive cancer and ductal carcinoma in situ (DCIS) to define a positive reference standard. All outcomes are reported at the participant level. Pathology of core or surgical biopsy was the reference standard for cancer detection rate and PPV; interval cancers reported until the next annual screen were included in the reference standard for sensitivity and specificity. Results: Among 1516 enrolled women, 1444 (median age, 54 [range, 40-75] years) completed both examinations and were included in the analysis. The reference standard was positive for invasive cancer with or without DCIS in 17 women and for DCIS alone in another 6. No interval cancers were observed during follow-up. Abbreviated breast MRI detected all 17 women with invasive cancer and 5 of 6 women with DCIS. Digital breast tomosynthesis detected 7 of 17 women with invasive cancer and 2 of 6 women with DCIS. The invasive cancer detection rate was 11.8 (95% CI, 7.4-18.8) per 1000 women for abbreviated breast MRI vs 4.8 (95% CI, 2.4-10.0) per 1000 women for DBT, a difference of 7 (95% CI, 2.2-11.6) per 1000 women (exact McNemar P = .002). For detection of invasive cancer and DCIS, sensitivity was 95.7% (95% CI, 79.0%-99.2%) with abbreviated breast MRI vs 39.1% (95% CI, 22.2%-59.2%) with DBT (P = .001) and specificity was 86.7% (95% CI, 84.8%-88.4%) vs 97.4% (95% CI, 96.5%-98.1%), respectively (P < .001). The additional imaging recommendation rate was 7.5% (95% CI, 6.2%-9.0%) with abbreviated breast MRI vs 10.1% (95% CI, 8.7%-11.8%) with DBT (P = .02) and the PPV was 19.6% (95% CI, 13.2%-28.2%) vs 31.0% (95% CI, 17.0%-49.7%), respectively (P = .15). Conclusions and Relevance: Among women with dense breasts undergoing screening, abbreviated breast MRI, compared with DBT, was associated with a significantly higher rate of invasive breast cancer detection. Further research is needed to better understand the relationship between screening methods and clinical outcome. Trial Registration: ClinicalTrials.gov Identifier: NCT02933489.

MRI Evaluation of the Contralateral Breast in Women with Recently Diagnosed Breast Cancer: 2-Year Follow-up.

OBJECTIVE: The American College of Radiology Imaging Network Trial 6667 showed that MRI can detect cancer in the contralateral breast that is missed by mammography and clinical examination at the time of the initial breast cancer diagnosis, based on 1-year follow-up. This study is a continuation of the trial that evaluates the diagnostic accuracy of MRI for contralateral breast cancer after 2 years of follow-up. METHODS: In total, 969 women with a diagnosis of unilateral breast cancer and no clinical or imaging abnormalities in the contralateral breast underwent breast MRI. The cancer status of all participants was monitored for 2 years after the initial MRI. Follow-up included documentation of any clinical, imaging, or interventional procedures performed. A study participant was considered positive for cancer if she had a tissue diagnosis of in situ or invasive breast cancer in the contralateral breast within 730 days of her initial MRI. RESULTS: Three additional cancers were diagnosed in the study population in the second year of the trial. The diagnostic yield for MRI for the 2-year period was 3% (31/969). After 2 years of follow-up, breast MRI has a sensitivity of 86% and specificity of 88% for detection of contralateral breast cancer. Its negative predictive value was 99%, and its positive predictive value was 22%. These values did not change significantly from the 1-year data. CONCLUSION: A negative contralateral breast MRI has a very high and reliable negative predictive value over 2 years, and, therefore, is helpful in managing and counseling patients during the period of initial diagnosis and early treatment.

Micronodules Detected on Computed Tomography During the National Lung Screening Trial: Prevalence and Relation to Positive Studies and Lung Cancer.

INTRODUCTION: In the National Lung Screening Trial (NLST) all cases with a 4-mm nodule (micronodule) and no other findings were classified as a negative study. The prevalence and malignant potential of micronodules in the NLST is evaluated to understand if this classification was appropriate. METHODS AND MATERIALS: In the NLST a total of 53,452 participants were enrolled with 26,722 undergoing low-dose computed tomography (CT) screening. To determine whether a micronodule developed into a lung cancer, a list from the NLST database of those participants who developed lung cancer and had a micronodule recorded was selected. The CT images of this subset were reviewed by experienced, fellowship-trained thoracic radiologists (R.F.M., C.C., P.M.B., and D.R.A.), all of whom participated as readers in the NLST. RESULTS: There were 26,722 participants who underwent CT in the NLST, of which 11,326 (42%) participants had at least one CT with a micronodule. Five thousand five hundred sixty (49%) of these participants had at least one positive CT examination, of which 409 (3.6%) subsequently were diagnosed with lung cancer. Of the 409 lung cancer cases with a micronodule recorded, there were 13 cases in which a micronodule developed into lung cancer. Considering the 13 cases, they represent 1.2% (13 of 1089) of the lung cancers diagnosed in the CT arm of the NLST and 0.11% (13 of 11,326) of the total micronodule cases. Additionally they represent 0.23% (13 of 5560) of the micronodule and at least one positive CT examination cases and 3.2% (13 of 409) of the micronodule cases diagnosed with lung cancer. The average size of the nodule at baseline (recorded as maximum diameter by perpendicular diameter) was 3.0 × 2.5 mm (ranges 2 x 4 mm and 2 x 4 mm) and at the positive CT the nodule was 11.1 × 8.6 mm (ranges, 6 x 20 mm and 5 x 14 mm); a difference of average change in size of 8.1 × 6.1 mm. The average number of days from first CT with a micronodule recorded to positive CT was 459 days (range, 338 - 723 days), the mean time from first CT with micronodule to lung cancer diagnosis was 617 days (range, 380 - 1140 days) and the mean time from positive CT to lung cancer diagnosis was 160 days (range, 18 - 417 days). Histologically, there was one small cell carcinoma and 12 non-small cell with stages of IA in 8 (62%), stage IB in 2 (15%), and 1 each stage IIIA, IIIB, and IV. The overall survival of NSCLC cases with a micronodule was not significantly different than the survival of the CT subset diagnosed with NSCL (p = 0.36). CONCLUSIONS: Micronodules are common among lung cancer-screened participants and are capable of developing into lung cancer; however, following micronodules by annual CT screening surveillance is appropriate and does not impact overall survival or outcome.

Association of Magnetic Resonance Imaging and a 12-Gene Expression Assay With Breast Ductal Carcinoma In Situ Treatment.

IMPORTANCE: Advanced diagnostics, such as magnetic resonance imaging (MRI) and gene expression profiles, are potentially useful to guide targeted treatment in patients with ductal carcinoma in situ (DCIS). OBJECTIVES: To examine the proportion of patients who converted to mastectomy after MRI and the reasons for those conversions and to measure patient adherence to radiotherapy guided by the 12-gene DCIS score. DESIGN, SETTING, AND PARTICIPANTS: Analysis of a prospective, cohort, nonrandomized clinical trial that enrolled women with DCIS on core biopsy who were candidates for wide local excision (WLE) from 75 institutions from March 25, 2015, to April 27, 2016, through the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network trial E4112. INTERVENTIONS: Participants underwent breast MRI before surgery, and subsequent management incorporated MRI findings for choice of surgery. The DCIS score was used to guide radiotherapy recommendations among women with DCIS who had WLE as the final procedure and had tumor-free excision margins of 2 mm or greater. MAIN OUTCOMES AND MEASURES: The primary end point was to estimate the conversion rate to mastectomy and the reason for conversion. RESULTS: Of 339 evaluable women (mean [SD] age, 59.1 [10.1] years; 262 [77.3%] of European descent) eligible for WLE before MRI, 65 (19.2%; 95% CI, 15.3%-23.7%) converted to mastectomy. Of these 65 patients, conversion was based on MRI findings in 25 (38.5%), patient preference in 25 (38.5%), positive margins after attempted WLE in 10 (15.4%), positive genetic test results in 3 (4.6%), and contraindication to radiotherapy in 2 (3.1%). Among the 285 who had WLE performed after MRI as the first surgical procedure, 274 (96.1%) achieved successful breast conservation. Of 171 women eligible for radiotherapy guided by DCIS score (clear margins, absence of invasive disease, and score obtained), the score was low (<39) in 82 (48.0%; 95% CI, 40.6%-55.4%) and intermediate-high (≥39) in 89 (52.0%; 95% CI, 44.6%-59.4%). Of these 171 patients, 159 (93.0%) were adherent with recommendations. CONCLUSIONS AND RELEVANCE: Among women with DCIS who were WLE candidates based on conventional imaging, multiple factors were associated with conversion to mastectomy. This study may provide useful preliminary information required for designing a planned randomized clinical trial to determine the effect of MRI and DCIS score on surgical management, radiotherapy, overall resource use, and clinical outcomes, with the ultimate goal of achieving greater therapeutic precision. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02352883.