New Frontiers in the Role of Locoregional Therapies in Breast Cancer: Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel.

Emerging evidence regarding the effectiveness of locoregional therapies (LRTs) for breast cancer has prompted investigation of the potential role of interventional radiology (IR) in the care continuum of patients with breast cancer. The Society of Interventional Radiology Foundation invited 7 key opinion leaders to develop research priorities to delineate the role of LRTs in both primary and metastatic breast cancer. The objectives of the research consensus panel were to identify knowledge gaps and opportunities pertaining to the treatment of primary and metastatic breast cancer, establish priorities for future breast cancer LRT clinical trials, and highlight lead technologies that will improve breast cancer outcomes either alone or in combination with other therapies. Potential research focus areas were proposed by individual panel members and ranked by all participants according to each focus area's overall impact. The results of this research consensus panel present the current priorities for the IR research community related to the treatment of breast cancer to investigate the clinical impact of minimally invasive therapies in the current breast cancer treatment paradigm.

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

Gender-Based Survey Analysis of Research and Mentoring in Interventional Radiology.

PURPOSE: This study sought to define gender-related differences in attitudes, perceptions, and aspirations among trainees interested in interventional radiology (IR) and to analyze their experiences in research and mentorship. MATERIALS AND METHODS: A cross-sectional survey study was conducted among the members of the Society of Interventional Radiology Resident, Fellow, and Student section and Medical Student section in the summer of 2020. The anonymous, internally validated 27-item survey assessed demographics, research attitudes and experiences, mentorship, and career aspirations. Descriptive statistics were calculated using Fisher exact analyses and Student t-tests. The institutional review board approved the protocols for this study. RESULTS: Of 105 respondents who indicated their gender, 30% were women and 70% were men. Although both genders reported similar levels of research experience, female trainees were less likely to feel valued and encouraged by their institution to engage in research (2.79 out of 4 vs 3.16 out of 4, P = .02) and were less likely to indicate that their program required them to participate in research (2.47 vs 3.06, P = .01). Female residents and fellows reported more difficulty in finding a mentor (2.88 vs 3.28, P = .04) and received less mentorship relating to IR education (29% vs 64%, P = .002). CONCLUSIONS: This work provides a perspective on the perceived obstacles faced by female trainees in pursuing research and finding effective mentorship in the field of IR. These data may guide future interventions to boost interest and engagement in IR research and residency programs in a way that promotes gender diversity and equity in the field.

Predictors for Nonhome Patient Discharge following Lower Extremity Arterial Interventions.

PURPOSE: To identify the variables associated with patient discharge disposition to optimize postprocedural care and discharge planning following lower extremity arterial interventions for peripheral artery disease (PAD). MATERIALS AND METHODS: The 2014-2017 American College of Surgeons National Surgical Quality Improvement Program database was queried using current procedural terminology codes for endovascular infrainguinal interventions for PAD. The main outcome variable of interest was nonhome discharge. Covariates included patient sociodemographic variables, age quartile (upper quartile, ≥77 years), comorbidities (diabetes, renal disease, bleeding disorder, congestive heart failure [CHF], and chronic obstructive pulmonary disease), presence of an open wound before a procedure, type of procedure, operative time, symptom severity, American Society of Anesthesiologists class, and baseline functional status. Univariate analysis and multivariate logistic regression were performed on Stata/SE 15.1. RESULTS: A total of 3,190 patients met the inclusion criteria, of whom 664 (20.8%) had nonhome discharge. Multivariate regression revealed that age (odds ratio [OR], 1.9 for the upper age quartile [>77 years]; 95% confidence interval [CI], 1.46-2.50), operative time (OR, 1.2 per increase in quartile; 95% CI, 1.09-1.30), preoperative wound (OR, 1.5; 95% CI, 1.24-1.90), renal failure (OR, 1.7; 95% CI, 1.30-2.14), CHF (OR, 2.2; 95% CI, 1.51-3.24), symptom severity (OR, 1.7; 95% CI, 1.46-1.98), and independent functional status (OR, 0.74; 95% CI, 0.59-0.92; P = .007) were associated with nonhome discharge. All P values were ≤.001 unless otherwise stated. CONCLUSIONS: Prolonged procedural time, the presence of preprocedural wound and patient comorbidities, symptomatology, and baseline functional status may be used to identify patients who will require a nonhome discharge and early discharge planning.

Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside.

Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial improvements in delivery of patient care across all disciplines of medicine. Recent advances in imaging technology along with marked expansion of readily available advanced health information, data offer a unique opportunity for interventional radiology (IR) to reinvent itself as a data-driven specialty. Additionally, the growth of AI-based applications in diagnostic imaging is expected to have downstream effects on all image-guidance modalities. Therefore, the Society of Interventional Radiology Foundation has called upon 13 key opinion leaders in the field of IR to develop research priorities for clinical applications of AI in IR. The objectives of the assembled research consensus panel were to assess the availability and understand the applicability of AI for IR, estimate current needs and clinical use cases, and assemble a list of research priorities for the development of AI in IR. Individual panel members proposed and all participants voted upon consensus statements to rank them according to their overall impact for IR. The results identified the top priorities for the IR research community and provide organizing principles for innovative academic-industrial research collaborations that will leverage both clinical expertise and cutting-edge technology to benefit patient care in IR.

Synchronous Virtual Patient Consultations in Radiology.

We describe our experience in synchronous virtual radiologist consultations, whereby a radiologist at the PACS uses a conferencing platform to join a primary care visit between a patient at home and a referring provider, at home or at clinic, to directly explain imaging results and partner with the referrer in forming management recommendations. We explore the model's significance in the context of patient-centered care. Implementation, obstacles, and potential impact on health care disparities are also discussed.

Periprocedural factors associated with overall patient survival following percutaneous image-guided liver tumor cryoablation.

PURPOSE: To assess the impact of periprocedural factors, including adverse events, on overall patient survival following image-guided liver tumor cryoablation procedures. METHODS: In this retrospective single-institution study, 143 patients (73 male, 70 female, ages 29-88) underwent 169 image-guided liver tumor cryoablation procedures between October 1998 and August 2014. Patient, tumor and procedural variables were recorded. The primary outcome was overall survival post-procedure (Kaplan-Meier analysis). Secondary outcomes were the impact of 15 variables on patient survival, which were assessed with multivariate cox regression and log-rank tests. RESULTS: Mean tumor diameter was 2.5 ± 1.2 cm. 26 of 143 (18.2%) patients had primary hepatic malignancies; 117 of 143 (81.8%) had liver metastases. Survival analysis revealed survivor functions at 3, 5, 7, 10 and 12 years post-ablation of 0.54, 0.37, 0.30, 0.17 and 0.06, with mean survival time of 40.8 ± 4.9 months. Tumor size ≥4 cm (p = .018), pre-procedural platelet count <100 × 103/µL (p = .023), and prior local radiation therapy (p = .014) were associated with worse overall patient survival. Grade 3 or higher adverse events were not associated with reduced survival (p = .49). CONCLUSIONS: All variables associated with overall survival were patient-related and none were associated with the cryoablation procedure. Pre-procedural thrombocytopenia, larger tumor size and history of prior local radiation therapy were independent risk factors for reduced overall survival in patients undergoing hepatic cryoablation. Adverse events related to hepatic cryoablation were not associated with decreased survival.

Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer.

OBJECTIVES: Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer. METHODS: In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest-based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance. RESULTS: Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94. CONCLUSIONS: MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer. KEY POINTS: • MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.

Role of Machine Learning and Artificial Intelligence in Interventional Oncology.

PURPOSE OF REVIEW: The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology. RECENT FINDINGS: With advancements in technology, there is a significant amount of research regarding the application of artificial intelligence and machine learning in medicine. Interventional oncology is a field that can benefit greatly from this research through enhanced image analysis and intraprocedural guidance. These software developments can increase detection of cancers through routine screening and improve diagnostic accuracy in classifying tumors. They may also aid in selecting the most effective treatment for the patient by predicting outcomes based on a combination of both clinical and radiologic factors. Furthermore, machine learning and artificial intelligence can advance intraprocedural guidance for the interventional oncologist through more accurate needle tracking and image fusion technology. This minimizes damage to nearby healthy tissue and maximizes treatment of the tumor. While there are several exciting developments, this review also discusses limitations before incorporating machine learning and artificial intelligence in the field of interventional oncology. These include data capture and processing, lack of transparency among developers, validating models, integrating workflow, and ethical challenged. In summary, machine learning and artificial intelligence have the potential to positively impact interventional oncologists and how they provide cancer care treatments.

Analysis of socioeconomic and demographic factors and imaging exam characteristics associated with missed appointments in pediatric radiology.

BACKGROUND: Missed appointments can have an adverse impact on health outcomes by delaying appropriate imaging, which can be critical in influencing treatment decisions. OBJECTIVE: To assess for socioeconomic and imaging exam factors associated with missed appointments among children scheduled for diagnostic imaging. MATERIALS AND METHODS: We retrospectively analyzed children (<18 years) scheduled for outpatient diagnostic imaging during a 12-month period. In doing so, we obtained socioeconomic and radiology exam characteristics (modality, intravenous contrast administration, radiation and use of sedation) data from the electronic medical record. We employed multivariate logistic regression to assess the association of socioeconomic, demographic and imaging exam characteristics with imaging missed appointments. RESULTS: In total, 7,275 children met inclusion criteria. The mean age was 8.8 years (standard deviation [SD] = 6.2 years) and the study population consisted of 52% female gender, 69% White race, 38% adolescent age group and 32% with a median household income by ZIP-code category of <$50,000. Logistic regression showed increased likelihood of missed appointments among children of Black/African-American race (odds ratio [OR] = 1.9; 95% confidence interval [CI] = 1.4-2.5); with insurance categories including Medicaid (OR=2.0; 95% CI=1.6-2.4), self-pay (OR=2.1; 95% CI=1.3-3.6) and other (OR=2.7; 95% CI=1.3-5.4); with <$50,000 median household income by ZIP-code category (OR=1.7; 95% CI=1.4-2.0); and with examination wait time of 7-21 days (OR=2.7; 95% CI=2.1-3.5) and >21 days (OR=3.7; 95% CI=2.9-4.8). The use of radiation, intravenous contrast agent or sedation was not associated with increased likelihood of missed appointments. CONCLUSION: Expanding our knowledge of how different socioeconomic and imaging-related factors influence missed appointments among children can serve as a foundational step to better understand existing and emerging disparities and inform strategies to advance health equity efforts in radiology.

Gender disparities among medical students choosing to pursue careers in medical research: a secondary cross-sectional cohort analysis.

BACKGROUND: Though the proportion of women in medical schools has increased, gender disparities among those who pursue research careers still exists. In this study, we seek to better understand the main factors contributing to the existing gender disparities among medical students choosing to pursue careers in medical research. METHODS: A secondary cross-sectional cohort analysis of previously published data was conducted using a 70-item survey that was sent to 16,418 medical students at 32 academic medical centers, and was IRB exempt from the need for ethical approval at the University of Illinois at Chicago and the University of Pennsylvania. Data was collected from September 2012 to December 2014. Survey results were analyzed using chi-square tests and Cramer's V to determine gender differences in demographic characteristics (training stage, race/ethnicity, marital status, parental status, financial support, and parental career background), career sector choice, career content choice, specialty choice, foreseeable career obstacles, and perceptions about medical research careers. RESULTS: Female respondents were more likely to be enrolled in MD-only programs, while male respondents were more likely to be enrolled in MD/PhD programs. More male students selected academia as their first-choice career sector, while more female respondents selected hospitalist as their first-choice career sector. More female respondents identified patient care and opportunities for community service as their top career selection factors, while more male respondents identified research and teaching as their top career selection factors. Student loan burden, future compensation, and work/life balance were the most reported obstacles to pursuing a career in medical research. CONCLUSIONS: There are many factors from a medical student's perspective that may contribute to the existing gender disparities in pursuing a career in medical research. While much progress has been made in attracting nearly equal numbers of men and women to the field of medicine, active efforts to bridge the gap between men and women in medical research careers are needed.

Factors associated with underrepresented minority physician scientist trainee career choices.

BACKGROUND: Recently, there have been concerted efforts to improve racial and ethnic diversity in the physician-scientist workforce. Identifying factors associated with career choices among those underrepresented in medicine and science is a necessary first step to advance this objective. The aim of the present study was to assess the attitudes and factors associated with academic and research career interests among underrepresented predoctoral physician-scientists. METHODS: A cross-sectional 70-question survey was distributed to all predoctoral single degree (MD or DO) and dual degree (MD/PhD or DO/PhD) trainees at 32 medical schools in the United States from 2012 to 2014. Main outcomes included factors important to advancement in academic medicine, intended medical specialty, and future career plans. To test the post-hoc hypothesis of whether trainees from underrepresented groups have differing perceptions of career trajectories and obstacles than their counterparts, we evaluated responses according to self-identified race/ethnic status using Chi-square and Fisher's exact tests. All tests were two-sided and significance level of < 0.05 was used. RESULTS: There were a total of 4433 responses representing all predoctoral training stages. The response rate was 27%. Most respondents were single degree trainees (MD/DO 79% vs MD/DO-PhD 21%). Most respondents self-identified as White (67%), followed by Multi-racial or Other (14.3%), Asian or Pacific Islander (10.4%), Hispanic (6%), and Black or African American (4.1%). Desired career sector, career intention, and clinical specialty interest differed across race/ethnic groups. With respect to career selection factors, anticipated non-work related responsibilities during residency were also significantly different between these groups. By multivariable regression analysis, Black or African American trainees were significantly less likely than White trainees to indicate a career in academia (OR 0.496, 95% CI 0.322-0.764) and basic research (OR 0.314, 95% CI 0.115-0.857), while Multi-racial or Other trainees were also less likely than White trainees to indicate a career in academia (OR 0.763, 95% CI 0.594-0.980). CONCLUSIONS: These data represent the first in-depth survey of career aspirations, perceptions, and interests between demographically underrepresented and non-underrepresented predoctoral physician-scientist trainees. Our results identify key differences between these cohorts, which may guide efforts to improve diversity within the physician-scientist workforce.

Local Control and Survival after Image-Guided Percutaneous Ablation of Adrenal Metastases.

PURPOSE: To evaluate local control and survival after image-guided ablation of adrenal gland metastases. MATERIALS AND METHODS: Image-guided ablations of adrenal metastases measuring < 5 cm performed at 2 academic medical centers between July 2002 and June 2016 were analyzed. There were 51 procedures performed on 46 tumors (mean diameter 2.8 cm ± 1.1; range, 0.7-4.9 cm) in 38 patients (mean age 66 y; range, 41-80 y) with renal cell carcinoma (n = 17 patients; 45%), non-small cell lung cancer (n = 10 patients; 26%), and other primary malignancies (n = 11 patients; 29%). Treatment modalities included cryoablation (n = 30 procedures; 59%), radiofrequency ablation (n = 12 procedures; 24%) and microwave ablation (n = 9 procedures; 18%). Technical success, primary and secondary efficacy, local progression rate, local progression-free survival, and overall survival were assessed. Mean follow-up was 37 months (range, 2-128 months). Statistical analysis was performed with univariate Cox hazards regression and Kaplan-Meier analyses. RESULTS: Technical success, primary efficacy, and secondary efficacy were 96%, 72%, and 76%. Local progression rate during all follow-up was 25%. Local tumor progression-free survival at 1, 3, and 5 years was 82%, 69%, and 55%. Overall survival at 1, 3, and 5 years was 82%, 44%, and 34%. In 16 patients with isolated adrenal metastasis, median disease-free survival was 8 months; 4 patients had no evidence of disease during follow-up. Lung cancer was associated with decreased survival (hazard ratio 4.41, P = .002). CONCLUSIONS: Image-guided ablation can achieve local control for adrenal metastases < 5 cm.

Social Media Tools for Department and Practice Communication and Branding in the Digital Age.

With nearly 70% of adults in the United States using at least one social media platform, a social media presence is increasingly important for departments and practices. Patients, prospective faculty and trainees, and referring physicians look to social media to find information about our organizations. The authors present a stepwise process for planning, executing, and evaluating an organizational social media strategy. This process begins with alignment with a strategic plan to set goals, identification of the target audience(s), selection of appropriate social media channels, tracking effectiveness, and resource allocation. The article concludes with a discussion of advantages and disadvantages of social media through a review of current literature. ©RSNA, 2018.

Implementation of Digital Awareness Strategies to Engage Patients and Providers in a Lung Cancer Screening Program: Retrospective Study.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths in the United States. Despite mandated insurance coverage for eligible patients, lung cancer screening rates remain low. Digital platforms, including social media, provide a potentially valuable tool to enhance health promotion and patient engagement related to lung cancer screening (LCS). OBJECTIVE: The aim was to assess the effectiveness of LCS digital awareness campaigns on utilization of low-dose computed tomography (LDCT) and visits to institutional online educational content. METHODS: A pay-per-click campaign utilizing Google and Facebook targeted adults aged 55 years and older and caregivers aged 18 years and older (eg, spouses, adult children) with LCS content during a 20-week intervention period from May to September 2016. A concurrent pay-per-click campaign using LinkedIn and Twitter targeted health care providers with LCS content. Geographic target radius was within 60 miles of an academic medical center. Social media data included aggregate demographics and click-through rates (CTRs). Primary outcome measures were visits to institutional Web pages and scheduled LDCT exams. Study period was 20 weeks before, during, and after the digital awareness campaigns. RESULTS: Weekly visits to the institutional LCS Web pages were significantly higher during the digital awareness campaigns compared to the 20-week period prior to implementation (mean 823.9, SD 905.8 vs mean 51, SD 22.3, P=.001). The patient digital awareness campaign surpassed industry standard CTRs on Google (5.85%, 1108/18,955 vs 1.8%) and Facebook (2.59%, 47,750/1,846,070 vs 0.8%). The provider digital awareness campaign surpassed industry standard CTR on LinkedIn (1.1%, 630/57,079 vs 0.3%) but not Twitter (0.19%, 1139/587,133 vs 0.25%). Mean scheduled LDCT exam volumes per week before, during, and after the digital awareness campaigns were 17.4 (SD 7.5), 20.4 (SD 5.4), and 26.2 (SD 6.4), respectively, with the difference between the mean number of scheduled exams after the digital awareness campaigns and the number of exams scheduled before and after the digital awareness campaigns being statistically significant (P<.001). CONCLUSIONS: Implementation of the LCS digital awareness campaigns was associated with increased visits to institutional educational Web pages and scheduled LDCT exams. Digital platforms are an important tool to enhance health promotion activities and engagement with patients and providers.

Exploring intentions of physician-scientist trainees: factors influencing MD and MD/PhD interest in research careers.

BACKGROUND: Prior studies have described the career paths of physician-scientist candidates after graduation, but the factors that influence career choices at the candidate stage remain unclear. Additionally, previous work has focused on MD/PhDs, despite many physician-scientists being MDs. This study sought to identify career sector intentions, important factors in career selection, and experienced and predicted obstacles to career success that influence the career choices of MD candidates, MD candidates with research-intense career intentions (MD-RI), and MD/PhD candidates. METHODS: A 70-question survey was administered to students at 5 academic medical centers with Medical Scientist Training Programs (MSTPs) and Clinical and Translational Science Awards (CTSA) from the NIH. Data were analyzed using bivariate or multivariate analyses. RESULTS: More MD/PhD and MD-RI candidates anticipated or had experienced obstacles related to balancing academic and family responsibilities and to balancing clinical, research, and education responsibilities, whereas more MD candidates indicated experienced and predicted obstacles related to loan repayment. MD/PhD candidates expressed higher interest in basic and translational research compared to MD-RI candidates, who indicated more interest in clinical research. Overall, MD-RI candidates displayed a profile distinct from both MD/PhD and MD candidates. CONCLUSIONS: MD/PhD and MD-RI candidates experience obstacles that influence their intentions to pursue academic medical careers from the earliest training stage, obstacles which differ from those of their MD peers. The differences between the aspirations of and challenges facing MD, MD-RI and MD/PhD candidates present opportunities for training programs to target curricula and support services to ensure the career development of successful physician-scientists.

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

INTRODUCTION: Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). METHODS: Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. RESULTS: All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). CONCLUSIONS: Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Metabolic reprogramming in cancer: unraveling the role of glutamine in tumorigenesis.

Increased glutaminolysis is now recognized as a key feature of the metabolic profile of cancer cells, along with increased aerobic glycolysis (the Warburg effect). In this review, we discuss the roles of glutamine in contributing to the core metabolism of proliferating cells by supporting energy production and biosynthesis. We address how oncogenes and tumor suppressors regulate glutamine metabolism and how cells coordinate glucose and glutamine as nutrient sources. Finally, we highlight the novel therapeutic and imaging applications that are emerging as a result of our improved understanding of the role of glutamine metabolism in cancer.