Telemedicine usage surged during the COVID-19 pandemic, shaping how patients access healthcare services. Its sustained role in post-pandemic healthcare may uncover long-term trends and variations in utilization. To characterize telemedicine utilization from 2019 to 2024 and identify patient characteristics associated with telemedicine use. This retrospective cohort study analyzed outpatient visits across five hospitals within the University of Pennsylvania Health System (Penn Medicine) from January 1, 2019, to September 30, 2024. The primary outcome was whether each outpatient encounter was conducted via telemedicine (vs in-person). We used multivariable logistic regression clustering on patients to assess associations between telemedicine use and patient- and encounter-level characteristics, including demographics, insurance, patient portal use, income, clinical comorbidity, distance from care, provider specialty, encounter type, hospital index, and visit year. The study included 46,149,734 visits among 2,248,341 patients. Telemedicine surged from 1

PURPOSE:Excess weight is a key modifiable risk factor for breast cancer. Glucagon-like peptide-1 receptor agonists (GLP-1) promote weight loss and improve metabolic health, but their effect on breast cancer risk remains unclear. METHODS:We conducted a retrospective cohort study from January 1, 2022, to June 30, 2025, using electronic health records. We identified 217,624 unique women who underwent breast imaging; restricting to ages 45-80 years with a BMI ≥25 and a documented imaging outcome (n = 111,646; median age 61 years). The primary outcome was breast cancer detection. GLP-1 use was defined as a first prescription before the examination date and assessed in relation to race, ethnicity, age, and type 2 diabetes. To address potential confounding between these covariates and GLP-1 exposure, we performed one-to-one, case-control matching using propensity scores on the basis of age, race, ethnicity, highest BMI, breast density, and history of type 2 diabetes. The propensity matching to the GLP-1 use group was performed using the greedy nearest neighbor approach. RESULTS:GLP-1 exposure was associated with a lower incidence of breast cancer (odds ratio [OR], 0.649 [95% CI, 0.569 to 0.741]; P < .0001). In the matched logistic regression (30,528 observations; 600 cancer cases), GLP-1 exposure was associated with a lower breast cancer incidence (OR, 0.695 [95% CI, 0.590 to 0.819]; P < .0001). CONCLUSION:In this large observational study of women undergoing breast imaging at a major academic center and affiliated sites, GLP-1 treatment was associated with a lower incidence of breast cancer, independent of age, race, ethnicity, BMI, breast density, and diabetes. The findings support the need for prospective trials investigating GLP-1 agonists for breast cancer prevention.

IMPORTANCE Telemedicine usage surged during the COVID-19 pandemic, shaping how patients access healthcare services. Its sustained role in post-pandemic healthcare may uncover long-term trends and variations in utilization. OBJECTIVE To characterize telemedicine utilization patterns from 2019 to 2024 and identify patient characteristics associated with telemedicine use. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study analyzed outpatient visits across five hospitals within the University of Pennsylvania Health System (Penn Medicine) from January 1, 2019, to September 30, 2024. MAIN OUTCOMES AND MEASURES The primary outcome was the proportion of visits conducted through telemedicine. Multivariable logistic regression models were employed to assess association between telemedicine use and patient characteristics including demographics, insurance type, patient portal use, and socioeconomic status. RESULTS The study included 46,149,734 visits among 2,248,341 patients. Following the declaration of the COVID-19 pandemic in March 2020, telemedicine surged from 1% to 17% of outpatient encounters by April 2020, stabilizing at 8-13% for the rest of the year. Usage declined in 2021 but remained at 4-6% from 2022 to 2024. In multivariable models, older adults were less likely to use telemedicine compared to those under 40 years (40-64 years: aOR, 0.67 [95% CI, 0.67-0.67]; ≥65 years: aOR, 0.46 [95% CI, 0.46-0.46]). Higher telemedicine use was observed among women (male: aOR, 0.91 [95% CI, 0.91-0.92]), unmarried individuals (aOR, 1.10, 95% CI, 1.10-1.11), patient portal users (aOR, 1.44 [95% CI, 1.43-1.45]), patients with fewer comorbidities (Charlson Comorbidity Index scores ≥3: aOR, 0.87 [95% CI, 0.87-0.88]), those living farther from the place of service (5-15 miles: aOR, 1.04 [95% CI, 1.04-1.04]; ≥15 miles: aOR, 1.44 [95% CI, 1.43-1.44]; reference: <5 miles), lower-income individuals (<$50,000: aOR, 1.06 [95% CI, 1.06-1.07]; ≥$100,000: aOR, 0.91 [95% CI, 0.91-0.92]; reference: $50,000-$100,000), and primary care compared to specialty care (aOR, 1.19 [95% CI, 1.18-1.20]). Return patients used telemedicine more than new patients (new: aOR, 0.47 [95% CI, 0.47-0.47]). Telemdicine use varied by race/ethnicity, with lower use among Non-Hispanic Black (aOR, 0.89 [95% CI, 0.88-0.89]), Hispanic (aOR, 0.95 [95% CI, 0.95-0.96]), and Asian (aOR, 0.83 [95% CI, 0.82-0.83]) patients compared to Non-Hispanic White patients. Patterns varied across visit types (e.g., diabetes, mental disorders, sleep disorders, heart failure, COPD, CAD, and GI disorders), though younger, female, and geographically distant patients consistently used telemedicine more. Non-Hispanic White patients with mental disorders exhibited disproportionately higher telemedicine use, underscoring racial/ethnic differences that persisted during and after the pandemic, likely influenced by differences in access and coverage. CONCLUSIONS AND RELEVANCE Telemedicine use is higher among tech-friendly populations, including, younger individuals, female, return patients, and those living farther from healthcare facilities. However, difference by age, socioeconomic status, and race/ethnicity persist, suggesting barriers in access, digital literacy, and coverage. Targeted policies are needed to ensure equitable telemedicine adoption and accessibility for all patients. ### Competing Interest Statement Dr. David A. Asch is a partner and part owner of VAL Health and serves on the advisory boards of Thrive Global and Morpheus. Dr. Yong Chen reported receiving personal fees from Merck & Co., Inc. outside the submitted work. All other co-authors have no conflicts of interest to report. ### Funding Statement We acknowledge the start-up funding from University of Pennsylvania Health System ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of University of Pennsylvania gave ethnical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon request

Background: The primary aim of the Tomosynthesis Mammographic Imaging Screening Trial (TMIST) is to determine whether women randomly assigned to be screened through 3-5 rounds with tomosynthesis (TM) have fewer advanced cancers than the population screened with digital mammography (DM) over 3-8 years after entry. In addition, there are 15 secondary aims with data being collected in the areas of imaging assessment, medical physics, breast biology and pathology, long-term follow-up, and health care utilization. Women ages 45 to 74 are eligible to participate. The study will enroll 108,508 women. Participants may also volunteer to contribute blood and/or buccal smears to the TMIST biorepository. Approximately 70% of TMIST participants have agreed to do so. Because of the size of the TMIST study and vast amount of data to be collected, there is an opportunity for investigators to utilize TMIST data to support various research questions not covered in TMIST. Methods:The TMIST study team developed a process where investigators who would like access to the TMIST data can submit a concept while the trial is ongoing for access to data in a protected manner. The process starts with the project investigator reaching out to the TMIST study chair. If the study chair, lead statistician, and ECOG-ACRIN (EA) co-Principal Investigator think the project has promise; a timeline for when the project could take place (either (1) during the TMIST clinical trial or (2) after the end of the trial and publication of the primary paper) is developed. The next steps involve reviews by the TMIST Data Safety and Monitoring Board and the EA Executive Review Committee. All ancillary projects proposed will require an external funding plan and budget before the project moves out of concept review inside of EA. Once the project concept clears all required EA approvals it then goes to the National Cancer Institute (NCI) Division of Cancer Prevention (DCP) for their approval. NCI Central Institutional Review Board (CIRB) approval is also required for the project to start while the trial is still active but is not sought until funding has been received. Two projects have secured external funding, have completed the EA committees’ review processes, and have received NCI approval. One is a case control study assessing short-term breast cancer risk through image-based analysis of screening mammograms (Project PI: Jon Steingrimsson, PhD, Brown University). The second is a case control study to assess the impact of breast compression pressure versus force in screening mammography on the likelihood of developing interval breast cancers (Project PIs: Etta Pisano, MD and Aili Maki, PhD, University of Toronto). Both projects involve analysis of images where software is being applied to TMIST images on computer systems controlled by EA IT personnel. Both projects are expected to be completed in the next year. Two additional projects have been approved for grant submission through the process described above. The PreSCRiB study (PI: Elizabeth Burnside, MD MPH, U of Wisconsin) will utilize Machine Learning applied to TMIST and All of Us data, including genetics, mammograms, social determinates of health and other data to develop individualized screening strategies for women. The second project (PI; Marc Ryser, PhD, Duke University) will utilize TMIST data to validate an algorithm the investigators have developed to assess overdiagnosis. Another project that is in development and will likely be submitted for approval and funding in the next 6-9 months is a collaboration between TMIST and UK-based clinical trial PROSPECTS study teams to compare rates of all cancers and advanced cancers for annual, biennial, and 3-year screening, with analysis by age, race, ethnicity, breast density and other factors. The ongoing TMIST study, as of June 24, 2024, has enrolled 101,394 women. Total enrollment is expected by late 2024 or early 2025. Follow-up on enrolled participants is expected to end in early 2028. Citation Format: Etta Pisano, Constantine Gatsonis, Mitchell Schnall, Melissa Troester, Elodia Cole , Jean Cormack, Jon Steingrimsson, Ilana Gareen, Martin Yaffe, Laura Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock. Conducting Ancillary Studies during an Active NCTN/NCORP Screening Trial – The TMIST (ECOG-ACRIN EA1151) Experience [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-01-05.

BACKGROUND. Abbreviated breast MRI (AB-MRI) achieves a higher cancer detection rate (CDR) than digital breast tomosynthesis when applied for baseline (i.e., first- round) supplemental screening of individuals with dense breasts. Limited literature has evaluated subsequent (i.e., sequential) AB-MRI screening rounds. OBJECTIVE. This study aimed to compare outcomes between baseline and subsequent rounds of screening AB-MRI in individuals with dense breasts who otherwise had an average risk for breast cancer. METHODS. This retrospective study included patients with dense breasts who otherwise had an average risk for breast cancer and underwent AB-MRI for supplemental screening between December 20, 2016, and May 10, 2023. The clinical interpretations and results of recommended biopsies for AB-MRI examinations were extracted from the EMR. Baseline and subsequent-round AB-MRI examinations were compared. RESULTS. The final sample included 2585 AB-MRI examinations (2007 baseline and 578 subsequent-round examinations) performed for supplemental screening of 2007 women (mean age, 57.1 years old) with dense breasts. Of 2007 baseline examinations, 1658 (82.6%) were assessed as BI-RADS category 1 or 2, 171 (8.5%) as BI-RADS category 3, and 178 (8.9%) as BI-RADS category 4 or 5. Of 578 subsequent-round examinations, 533 (92.2%) were assessed as BI-RADS category 1 or 2, 20 (3.5%) as BI-RADS category 3, and 25 (4.3%) as BI-RADS category 4 or 5 (p < .001). The abnormal interpretation rate (AIR) was 17.4% (349/2007) for baseline examinations versus 7.8% (45/578) for subsequent-round examinations (p < .001). For baseline examinations, PPV2 was 21.3% (38/178), PPV3 was 26.6% (38/143), and the CDR was 18.9 cancers per 1000 examinations (38/2007). For subsequent-round examinations, PPV2 was 28.0% (7/25) (p = .45), PPV3 was 29.2% (7/24) (p = .81), and the CDR was 12.1 cancers per 1000 examinations (7/578) (p = .37). All 45 cancers diagnosed by baseline or subsequent-round AB-MRI were stage 0 or 1. Seven cancers diagnosed by subsequent-round AB-MRI had a mean interval of 872 +/- 373 (SD) days since prior AB-MRI and node-negative status at surgical axillary evaluation; six had an invasive component, all measuring 1.2 cm or less. CONCLUSION. Subsequent rounds of AB-MRI screening of individuals with dense breasts had lower AIR than baseline examinations while maintaining a high CDR. All cancers detected by subsequent-round examinations were early-stage node-negative cancers. CLINICAL IMPACT. The findings support sequential AB-MRI for supplemental screening in individuals with dense breasts. Further investigations are warranted to optimize the screening interval.

BACKGROUND AND PURPOSE:The slow adoption of new advanced imaging techniques into clinical practice has been a long-standing challenge. Principles of implementation science and the reach, effectiveness, adoption, implementation, maintenance (RE-AIM) framework were used to build a clinical vessel wall imaging program at an academic medical center. MATERIALS AND METHODS:Six phases for implementing a clinical vessel wall MR imaging program were contextualized to the RE-AIM framework. Surveys were designed and distributed to MR imaging technologists and clinicians. Effectiveness was measured by surveying the perceived diagnostic value of vessel wall imaging among MR imaging technologists and clinicians, trends in case volumes in the clinical vessel wall imaging examination, and the number of coauthored vessel wall imaging-focused publications and abstracts. Adoption and implementation were measured by surveying stakeholders about workflow. Maintenance was measured by surveying MR imaging technologists on the value of teaching materials and online tip sheets. The Integration dimension was measured by the number of submitted research grants incorporating vessel wall imaging protocols. Feedback during the implementation phases and solicited through the survey is qualitatively summarized. Quantitative results are reported using descriptive statistics. RESULTS:Six phases of the RE-AIM framework focused on the following: 1) determining patient and disease representation, 2) matching resource availability and patient access, 3) establishing vessel MR wall imaging (VWI) expertise, 4) forming interdisciplinary teams, 5) iteratively refining workflow, and 6) integrating for maintenance and scale. Survey response rates were 48.3% (MR imaging technologists) and 71.4% (clinicians). Survey results showed that 90% of the MR imaging technologists agreed that they understood how vessel wall MR imaging adds diagnostic value to patient care. Most clinicians (91.3%) reported that vessel wall MR imaging results changed their diagnostic confidence or patient management. Case volumes of clinical vessel wall MR imaging performed from 2019 to 2022 rose from 22 to 205 examinations. Workflow challenges reported by MR imaging technologists included protocoling examinations and scan length. Feedback from ordering clinicians included the need for education about VWI indications, limitations, and availability. During the 3-year implementation period of the program, the interdisciplinary teams coauthored 27 publications and abstracts and submitted 13 research grants. CONCLUSIONS:Implementation of a clinical imaging program can be successful using the principles of the RE-AIM framework. Through iterative processes and the support of interdisciplinary teams, a vessel wall MR imaging program can be integrated through a dedicated clinical pipeline, add diagnostic value, support educational and research missions at an academic medical center, and become a center for excellence.

The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m 2 and hepatic steatosis with threshold SHAD ≥ − 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN . The cohort contained 8914 patients—lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.

Abstract Background The United States Preventative Services Task Force in their 2023 recommendations identified areas where more research data is needed to inform future breast cancer screening recommendations. Research areas identified are: improve clinicians and patients understanding and evaluation of dense breast tissue on a screening mammogram, benefits and harms of supplemental screening using ultrasound or MRI for women with dense breasts, health outcomes such as rates of breast cancer diagnosis requiring treatment, rates of advanced breast cancers diagnosed across consecutive screening rounds, and breast cancer-associated morbidity and mortality, causes of increased risk of breast cancer mortality in black women across spectrum of stages and biomarker patterns, understand why black women are more likely to be diagnosed with breast cancers that have biomarker patterns that are indicative of poor health outcomes, assess benefits/harms differences between annual and biennial screening for breast cancer in women overall and if there are differences between black and white women, approaches to reduce the risk of overdiagnosis leading to overtreatment of breast lesions found at screening that may not cause morbidity and mortality, natural history of DCIS, and identify prognostic indicators of breast tumors that are unlikely to affect quality or length of life. Methods The ongoing TMIST study, currently with 88,801 asymptomatic women presenting for screening mammography ages 45-74 enrolled out of 128,905, could contribute to scientific evidence to support the above research areas through existing study aims and planned ancillary studies. Supplemental Screening with US and MRI: TMIST PreSCRIB will utilize Machine Learning applied to TMIST and All of Us data, including genetics, mammograms, social determinates of health and other data to recommend individualized screening strategies for women. DxMRI is a study where women will get AbMRI at time of Dx work-up. There are plans to use these examinations plus supplemental screening MRIs performed on TMIST subjects in an enriched reader study to evaluate the role of supplemental screening MRI in moderate risk women. Rates of breast cancer treatment, consecutive screening, morbidity, and mortality: TMIST’s primary outcome is the proportion of women experiencing an advanced breast cancer and needing treatment. TMIST is also collecting information on health care utilization following a cancer diagnosis, including types of treatment given, and costs data from the screening and diagnostic work-up visits, and mortality data for study participants. Increased risk of breast cancer mortality in black women: TMIST is performing PAM50 plus p53 status, immune profile, DNA repair phenotype, and 21-gene recurrence assay on all breast cancers and a subset of benign tissue. Blood and buccal smears might also help address this issue. Ongoing work, funded by the Susan B. Komen Foundation, focuses on improving Black participation in TMIST Biorepository (currently about 45% participation of the 21% of TMIST US black subjects). Surveys are planned on perceived racism and social determinates of health as part of DxMRI Study. Screening Frequency: We are developing a collaboration with the UK-based clinical trial PROSPECTS to compare rates of all cancers and advanced cancers for annual, biennial, and 3-year screening. Overdiagnosis, natural history of DCIS, prognostic indicators of breast tumors not impacting quality of life: PRoGram- will use radiomics, genomics and pathomics to develop a greater understanding of the variability of the non-advanced cancers diagnosed in the TMIST population, including DCIS. It is hoped that this model will provide greater understanding of the risk of poor outcomes for women diagnosed with lower risk cancers, including DCIS. Citation Format: Etta Pisano, Constantine Gatsonis, Mitchell Schnall, Melissa Troester, Elodia Cole, Jean Cormack, Ilana Gareen, Martin Yaffe, Laura Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock. Addressing USPSTF 2023 Identified Key Gaps in Knowledge in Breast Cancer Screening through TMIST (ECOG-ACRIN EA1151) or its Ancillary Studies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-19-03.

Electronic consultations (e-consults) mediated through an electronic health record system or web-based platform allow synchronous or asynchronous physician-to-physician communication. E-consults have been explored in various clinical specialties, but relatively few instances in the literature describe e-consults to connect health care providers directly with radiologists.The authors outline how a radiology department can implement an e-consult service and review the development of such a service in a large academic health system. They describe the logistics, workflow, turnaround time expectations, stakeholder management, and pilot implementation and highlight challenges and lessons learned.

10502 Background: The sensitivity of mammography, including digital breast tomosynthesis (DBT), is limited by breast density. Abbreviated breast MRI (AB-MR) significantly increases breast cancer detection in women with dense breasts. In the prevalence (baseline) screening round of the EA1141 trial, AB-MR offered a 2.45-fold higher cancer detection rate (CDR) than DBT. Here we report the CDR and diagnostic accuracies of AB-MR and DBT during the EA1141 incidence screening round and the overall interval cancer rate. Methods: Informed consent was obtained from all participants. Asymptomatic average risk women aged 40-75 years with mammographically dense breasts scheduled for routine screening with DBT were enrolled. All women underwent DBT and AB-MR at baseline (prevalence) and year 1 (incidence) screens which were interpreted independently by 2 different radiologists blinded to the other modality. Women were contacted 11-13 months after the year 1 screen to assess for a subsequent breast cancer diagnosis. Pathology of biopsy was the reference standard for CDR and positive predictive value of biopsy (PPV3). Interval cancers reported during 11-13 months of follow-up until the next annual screening were included in the reference standard for sensitivity and specificity. A post hoc adjustment was made for multiple testing using the Bonferroni method; p-values are reported for comparisons which met the adjusted significance threshold (0.05/5 = 0.01). Reported 95% confidence intervals were not adjusted for multiplicity. Results: Of 1516 enrolled subjects, 1444 completed the baseline and 1291 also the incidence screen. During the latter, 9 women were diagnosed with breast cancer. DBT detected cancer in 5 women (3 DCIS, 2 invasive) for a CDR of 3.9/1000 (5/1291, CI 1.7, 9.0). AB-MR detected cancer in 6 women (0 DCIS, 6 invasive), 4 of which were not detected on DBT, for a CDR of 4.6/1000 (6/1291, CI 2.1, 10.1). The additional imaging (subject-level) rate for DBT was 7.9% (102/1291, CI 6.6%, 9.5%) vs 3.7% (48/1291, CI 2.8%, 4.9%) for AB-MR (p < 0.001). The PPV3 (lesion-level) for DBT was 29.4% (5/17, CI 13.5%, 52.6%) and 15.0% (6/40, CI 6.9%, 29.6%) for AB-MR. During the 2 years of study screening (baseline and year 1), there was 1 interval cancer (palpable lump 348 days after year 1 screen), for an overall interval cancer rate of 0.37/1000 person-years (1/2677 person-years, CI 0.05, 2.65). Sensitivity and specificity were 50.0% (5/10, CI 23.7%, 76.3%) and 98.5% (1261/1280, CI 97.7%, 99.0%) for DBT vs 60% (6/10, CI 31.3%, 83.2%) and 93.3% (1193/1278, CI 91.8%, 94.6%, p < 0.001) for AB-MR. Conclusions: On the incidence screening round, AB-MR detected additional invasive breast cancers not seen on DBT. In addition, a low overall interval cancer rate was observed with combined screening. Although the need for additional imaging was lower for AB-MR, specificity was higher for DBT. Clinical trial information: NCT02933489 .

Number of subjects with DOSI scans performed at each time-point.

The integration of digital data from imaging, pathology, genomics, and other fields creates an opportunity to produce information-rich diagnoses, especially for complex patients. Technology exists today to create a "diagnostic cockpit" that can accept inputs from multiple sources, analyze them using artificial intelligence and other quantitative tools, and produce a precision diagnosis. Although barriers to creation and dissemination of such a cockpit exist, the metaphor provides a glimpse into the diagnostic processes of the future.

List of Chemotherapy regimens used in this study.

Supplementary data includes appendix of extracted Radiomic features. Figure S1 shows a summary of patient characteristics. Figure S2 shows the radiomic analysis workflow. Figure S3 shows representative cases from heterogeneity phenotypes. Figure S4 shows independent validation of intrinsic imaging phenotypes of tumor heterogeneity.

TPS10614 Background: The ECOG-ACRIN Tomosynthesis Mammographic Imaging Screening Trial (TMIST), which opened in 2017, is a randomized trial designed to assess whether Tomosynthesis Mammography (TM) should replace Digital Mammography (DM) for breast cancer screening. It is hypothesized that women assigned to TM for 3-5 screening rounds will have fewer advanced breast cancers than the women assigned to DM. Advanced cancers are those that have distant metastases or positive nodes, are invasive tumors greater than or equal to 2.0 cm in size, or are invasive tumors greater than 1.0 cm in size that are triple negative or HER 2+. The initially planned enrollment of 164,946 women was due to be completed by the end of 2020, with follow-up concluded by 2025. There were substantial challenges in meeting this timeline, including the organizational and funding structure of the NCI National Clinical Trials Network which is dependent upon sites using their existing staffing resources (not always readily available at the time of study activation). This led to longer than anticipated start of enrollment for most interested sites and lower than anticipated annual enrollment per participating site based ultimately on the staffing support that could be allocated to manage TMIST. In addition, research staffing shortages and periodic research operations closures due to COVID-19 have also impacted enrolling TMIST sites, though unevenly, since the start of the pandemic. Enrollment plateaued at approximately 2,100 subjects per month by the end of 2020. With that accrual rate expected, the trial design was modified to reduce the sample size so that the study could be completed by 2027. Methods: With the approval of the NCI CIRB, we changed how the primary endpoint measure for TMIST is assessed from the number of advanced cancers that occur by 4.5 years after randomization to the time from randomization to occurrence of advanced cancers. All advanced cancers occurring within 7 years of randomization are now included and all participants followed for at least three years. In addition, the power of the study of the study was modified from 0.9 to 0.85, while the originally assumed effect size at 4.5 years was retained These changes allowed a reduction of sample size to 128,905, with subject recruitment projected to end in 2024. As of February 14, 2022, there are 125 sites open, 114 in the U.S. and 11 in other countries, with an additional 31 sites planning to open. As of February 14, 2022, a total of 63,845 women have been enrolled in the trial worldwide at 115 sites, with 20% of US participants self-identifying as belonging to minority racial and ethnic groups and 70% consenting to optional blood and/or buccal cell collection. Clinical trial information: NCT03233191.

PURPOSE In the United States, the National Cancer Institute National Cancer Clinical Trials Network (NCTN) groups have conducted publicly funded oncology research for 50 years. The combined impact of all adult network group trials has never been systematically examined. METHODS We identified randomized, phase III trials from the adult NCTN groups, reported from 1980 onward, with statistically significant findings for ≥ 1 clinical, time-dependent outcomes. In the subset of trials in which the experimental arm improved overall survival, gains in population life-years were estimated by deriving trial-specific hazard functions and hazard ratios to estimate the experimental treatment benefit and then mapping this trial-level benefit onto the US cancer population using registry and life-table data. Scientific impact was based on citation data from Google Scholar. Federal investment costs per life-year gained were estimated. The results were derived through December 31, 2020. RESULTS One hundred sixty-two trials comprised of 108,334 patients were analyzed, representing 29.8% (162/544) of trials conducted. The most common cancers included breast (34), gynecologic (28), and lung (14). The trials were cited 165,336 times (mean, 62.2 citations/trial/year); 87.7% of trials were cited in cancer care guidelines in favor of the recommended treatment. These studies were estimated to have generated 14.2 million (95% CI, 11.5 to 16.5 million) additional life-years to patients with cancer, with projected gains of 24.1 million (95% CI, 19.7 to 28.2 million) life-years by 2030. The federal investment cost per life-year gained through 2020 was $326 in US dollars. CONCLUSION NCTN randomized trials have been widely cited and are routinely included in clinical guidelines. Moreover, their conduct has predicted substantial improvements in overall survival in the United States for patients with oncologic disease, suggesting they have contributed meaningfully to this nation's health. These findings demonstrate the critical role of government-sponsored research in extending the lives of patients with cancer.

Rationale: Lung-RADS classification was developed to standardize reporting and management of lung cancer screening using low-dose computed tomographic (LDCT) imaging. Although variation in Lung-RADS distribution between healthcare systems has been reported, it is unclear if this is explained by patient characteristics, radiologist experience with lung cancer screening, or other factors. Objectives: Our objective was to determine if patient or radiologist factors are associated with Lung-RADS score. Methods: In the Population-based Research to Optimize the Screening Process (PROSPR) Lung consortium, we conducted a study of patients who received their first screening LDCT imaging at one of the five healthcare systems in the PROSPR Lung Research Center from May 1, 2014, through December 31, 2017. Data on LDCT scans, patient factors, and radiologist characteristics were obtained via electronic health records. LDCT scan findings were categorized using Lung-RADS (negative [1], benign [2], probably benign [3], or suspicious [4]). We used generalized estimating equations with a multinomial distribution to compare the odds of Lung-RADS 3, and separately Lung-RADS 4, versus Lung-RADS 1 or 2 and estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between Lung-RADS assignment and patient and radiologist characteristics. Results: Analyses included 8,556 patients; 24% were assigned Lung-RADS 1, 60% Lung-RADS 2, 10% Lung-RADS 3, and 5% Lung-RADS 4. Age was positively associated with Lung-RADS 3 (OR, 1.02; 95% CI, 1.01-1.03) and 4 (OR, 1.03; 95% CI, 1.01-1.05); chronic obstructive pulmonary disease (COPD) was positively associated with Lung-RADS 4 (OR, 1.78; 95% CI, 1.45-2.20); obesity was inversely associated with Lung-RADS 3 (OR, 0.70; 95% CI, 0.58-0.84) and 4 (OR, 0.58; 95% CI, 0.45-0.75). There was no association between sex, race, ethnicity, education, or smoking status and Lung-RADS assignment. Radiologist volume of interpreting screening LDCT scans, years in practice, and thoracic specialty were also not associated with Lung-RADS assignment. Conclusions: Healthcare systems that are comprised of patients with an older age distribution or higher levels of COPD will have a greater proportion of screening LDCT scans with Lung-RADS 3 or 4 findings and should plan for additional resources to support appropriate and timely management of noted positive findings.

Evaluation of image characteristics at ultra-low radiation dose levels of a first-generation dual-source photon-counting computed tomography (PCCT) compared to a dual-source dual-energy CT (DECT) scanner. A multi-energy CT phantom was imaged with and without an extension ring on both scanners over a range of radiation dose levels (CTDIvol 0.4–15.0 mGy). Scans were performed in different modes of acquisition for PCCT with 120 kVp and DECT with 70/Sn150 kVp and 100/Sn150 kVp. Various tissue inserts were used to characterize the precision and repeatability of Hounsfield units (HUs) on virtual mono-energetic images between 40 and 190 keV. Image noise was additionally investigated at an ultra-low radiation dose to illustrate PCCT’s ability to remove electronic background noise. Our results demonstrate the high precision of HU measurements for a wide range of inserts and radiation exposure levels with PCCT. We report high performance for both scanners across a wide range of radiation exposure levels, with PCCT outperforming at low exposures compared to DECT. PCCT scans at the lowest radiation exposures illustrate significant reduction in electronic background noise, with a mean percent reduction of 74