Dr. Emily Tsai is a board certified radiologist with subspecialty training in thoracic imaging and image-guided procedures. Her clinical focus is on diseases affecting the lungs and airways, including cancer, interstitial lung disease, COPD, and infection. Her research focuses on quality improvement and patient outcomes. Recent projects include assessment of incidental findings and cost-effectiveness of CT screening for lung cancer, as well as application of clinical tools and machine learning to improve workflow and triage of emergent studies.
- Thoracic Imaging
- Image-Guided Biopsy
Clinical Assistant Professor, Radiology
Honors & Awards
Early Career Faculty Leadership Accelerator x Bossed Up Level Up Program, Stanford Advancement of Women in Medicine (SAWM) (2021-2022)
S. David Rockoff Research Seed Grant Award, Society of Thoracic Radiology (2021-2022)
Comparative Effectiveness Research Program, Radiological Society of North America (2019-2020)
Clinical Faculty Development Program, Association of University Radiologists (2019)
Clinician Educator Development Program, American Roentgen Ray Society (2019)
Annual Meeting Educational Exhibit Section Chair’s Pick – Subspecialty Award, American Roentgen Ray Society (2018)
Outstanding Nighthawk Resident Award, UCLA (2016)
Health Policy Research Scholar Program – Outstanding Presentation Award, American College of Radiology (ACR) – Association of University Radiologists (2015)
Annual Meeting and Leadership Summit Poster Winner, California Radiological Society (2014)
Annual Meeting and Chapter Leadership Conference – Poster Winner (Quality and Safety), American College of Radiology, Resident and Fellow Section (2013)
Annual Meeting and Leadership Summit Poster Winner, California Radiological Society (2013)
Enabling Grant, Susan G. Komen for the Cure (Breast Cancer Foundation) (2008)
Fellowship Award, Chinese American Physicians Society (2008)
Spotlight on Leadership and Community, Stanford University, Center of Excellence in Diversity in Medical Education (2008)
Medical Scholars Research Program Fellowship, Stanford University School of Medicine (2007-2010)
Grant, California HealthCare Foundation (2007)
Boards, Advisory Committees, Professional Organizations
Affiliate Faculty, Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) (2019 - Present)
Member, Society of Thoracic Radiology (2016 - Present)
Member, American College of Radiology (2012 - Present)
Member, American Roentgen Ray Society (2012 - Present)
Member, Radiological Society of North America (2008 - Present)
Board Certification: American Board of Radiology, Radiology (2017)
Fellowship: UCLA Radiology Fellowships (2017) CA
Residency: UCLA Radiology Residency (2016) CA
Internship: New York University Internal Medicine Residency (2012) NY
Medical Education: Stanford University School of Medicine (2011) CA
MD, Stanford University, Scholarly Concentration: Bioinformatics (2011)
BS, Columbia University, Biomedical Engineering (major), Computer Science (minor) (2005)
Current Research and Scholarly Interests
Lung cancer screening
Clinical applications of machine learning
Comparative effectiveness research
Image-guided biopsy and intervention
Clinical Validation of Machine Learning Triage of Chest Radiographs
Artificial intelligence and machine learning have the potential to transform the practice of radiology, but real-world application of machine learning algorithms in clinical settings has been limited. An area in which machine learning could be applied to radiology is through the prioritization of unread studies in a radiologist's worklist. This project proposes a framework for integration and clinical validation of a machine learning algorithm that can accurately distinguish between normal and abnormal chest radiographs. Machine learning triage will be compared with traditional methods of study triage in a prospective controlled clinical trial. The investigators hypothesize that machine learning classification and prioritization of studies will result in quicker interpretation of abnormal studies. This has the potential to reduce time to initiation of appropriate clinical management in patients with critical findings. This project aims to provide a thoughtful and reproducible framework for bringing machine learning into clinical practice, potentially benefiting other areas of radiology and medicine more broadly.
Artificial Intelligence (AI) for Lung Nodules: From the AJR Special Series on AI Applications.
AJR. American journal of roentgenology
Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening (LCS) CT. AI has been heavily investigated for detecting and characterizing lung nodules, as well as for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiograph or vessel suppression on CT) and for non-interpretive aspects of reporting and workflow, including management of nodule follow-up. Despite the growing interest and rapid development of AI tools, as well as the FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns of generalizability, various regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of the AI tools in clinical settings as well as demonstration of benefit in terms of patient-oriented outcomes also remain needed. This review provides an overview of potential applications of AI tools for the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in such tools' clinical implementation.
View details for DOI 10.2214/AJR.22.27487
View details for PubMedID 35544377
The RSNA International COVID-19 Open Annotated Radiology Database (RICORD).
The coronavirus disease 2019 (COVID-19) pandemic is a global healthcare emergency. Although reverse transcriptase polymerase chain reaction (RT-PCR) is the reference standard method to identify patients with COVID-19 infection, chest radiographs and CT chest play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology (STR) collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations were performed by thoracic radiology subspecialists for all COVID positive thoracic CTs. The labeling schema was coordinated with other international consensus panels and COVID data annotation efforts, European Society of Medical Imaging Informatics (EUSOMII), the American College of Radiology (ACR) and the American Association of Physicists in Medicine (AAPM). Study level COVID classification labels for chest radiographs were annotated by three radiologists with majority vote adjudication by board certified radiologists. RICORD consists of 240 thoracic CT scans and 1,000 chest radiographs contributed from four international sites. We anticipate that the RICORD database will ideally lead to prediction models that can demonstrate sustained performance across populations and healthcare systems. See also the editorial by Bai and Thomasian.
View details for DOI 10.1148/radiol.2021203957
View details for PubMedID 33399506
Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings.
JNCI cancer spectrum
2019; 3 (3): pkz035
Numerous health policy organizations recommend lung cancer screening, but no consensus exists on the optimal policy. Moreover, the impact of the Lung CT screening reporting and data system guidelines to manage small pulmonary nodules of unknown significance (a.k.a. indeterminate nodules) on the cost-effectiveness of lung cancer screening is not well established.We assess the cost-effectiveness of 199 screening strategies that vary in terms of age and smoking eligibility criteria, using a microsimulation model. We simulate lung cancer-related events throughout the lifetime of US-representative current and former smokers. We conduct sensitivity analyses to test key model inputs and assumptions.The cost-effectiveness efficiency frontier consists of both annual and biennial screening strategies. Current guidelines are not on the frontier. Assuming 4% disutility associated with indeterminate findings, biennial screening for smokers aged 50-70 years with at least 40 pack-years and less than 10 years since smoking cessation is the cost-effective strategy using $100 000 willingness-to-pay threshold yielding the highest health benefit. Among all health utilities, the cost-effectiveness of screening is most sensitive to changes in the disutility of indeterminate findings. As the disutility of indeterminate findings decreases, screening eligibility criteria become less stringent and eventually annual screening for smokers aged 50-70 years with at least 30 pack-years and less than 10 years since smoking cessation is the cost-effective strategy yielding the highest health benefit.The disutility associated with indeterminate findings impacts the cost-effectiveness of lung cancer screening. Efforts to quantify and better understand the impact of indeterminate findings on the effectiveness and cost-effectiveness of lung cancer screening are warranted.
View details for DOI 10.1093/jncics/pkz035
View details for PubMedID 31942534
View details for PubMedCentralID PMC6947892
Incidental Findings on Lung Cancer Screening: Significance and Management
SEMINARS IN ULTRASOUND CT AND MRI
2018; 39 (3): 273–81
Incidental findings are commonly detected by computed tomography, but distinguishing which findings have little or no clinical consequence and which are significant enough to require further evaluation is not always clear. This distinction is important for patient care and to ensure appropriate use of health care resources. This article aims to highlight some of the incidental findings detected by low-dose CT (LDCT) performed for lung cancer screening and to present an overview of currently accepted management recommendations.
View details for PubMedID 29807637
Feasibility and Safety of Intrathoracic Biopsy and Repeat Biopsy for Evaluation of Programmed Cell Death Ligand-1 Expression for Immunotherapy in Non-Small Cell Lung Cancer
2018; 287 (1): 326–32
Purpose To determine feasibility and safety of biopsy and repeat biopsy for assessment of programmed cell death ligand-1 (PD-L1) status. Materials and Methods This retrospective analysis reviewed 101 patients who underwent transthoracic core needle biopsy for the KEYNOTE-001 (MK-3475) clinical trial of pembrolizumab, an antiprogrammed cell death-1 therapy for non-small cell lung cancer, from May 2012 to September 2014. Sixty-one male patients (mean age, 66.1 years; range 36-83 years) and 40 female patients (mean age, 66.8 years; age range, 36-90 years) were included. Data collected included population characteristics, treatment history, target location, size, and depth from pleura. Adequacy of the tissue sample for diagnostic testing and rates of biopsy-related complications were assessed. Statistical analysis was performed by using univariate and multivariate generalized linear models to determine significant risk factors for biopsy complications. Results A total of 110 intrathoracic biopsies were performed, and 101 (91.8%) were performed as repeat biopsies subsequent to a previous percutaneous or bronchoscopic biopsy or previous surgical biopsy or resection. More than 84.5% (93 of 110) of biopsies were performed in patients who had undergone previous local or systemic therapy. Specimens were adequate for evaluation of PD-L1 expression in 96.4% of biopsies. Procedure-related complications occurred in 28 biopsies (25.4%); pneumothorax was most common (22.7%). Overall mean number of core needle biopsy samples obtained was 7.9 samples. Conclusion Image-guided transthoracic core needle biopsy is an effective method for obtaining tissue for PD-L1 expression analysis. © RSNA, 2017.
View details for DOI 10.1148/radiol.2017170347
View details for Web of Science ID 000427992600040
View details for PubMedID 29232184
- Traumatic Lung Injury in Sports Stems to Sternum: Sports Imaging Inside and Out ARRS. 2022: 98-104
Pragmatic Application of Computed Tomography Lung Texture Analysis in Immune Checkpoint Inhibitor Pneumonitis: An Exploratory Study
AMER THORACIC SOC. 2021
View details for Web of Science ID 000685468901148
- Dual energy chest x-ray for improved COVID-19 detection using a dual-layer flat-panel detector: Simulation and phantom studies SPIE-INT SOC OPTICAL ENGINEERING. 2021
Bronchopulmonary Dysplasia: From Neonate to Adult
Contemporary Diagnostic Radiology
2020; 43 (17)
View details for DOI 10.1097/01.CDR.0000694612.92296.19
Treatment of a benign esophagopericardial fistula with an esophageal stent—a case report
Annals of Esophagus
View details for DOI 10.21037/aoe.2020.03.05
- Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings JNCI CANCER SPECTRUM 2019; 3 (3)
Unusual Tumors of the Lung
Contemporary Diagnostic Radiology
2019; 42 (3)
View details for DOI 10.1097/01.CDR.0000552870.91176.72
- PVH/Pulmonary Edema (Cardiogenic) CT & MR in Cardiology Elsevier. 2019; 1
- Pulmonary Aneurysm CT & MR in Cardiology Elsevier. 2019; 1
- Proximal Interruption of the Pulmonary Artery CT & MR in Cardiology Elsevier. 2019; 1
- Approach to Pulmonary Vasculature CT & MR in Cardiology Elsevier. 2019; 1
- Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports arXiv 2019; 11
- Long-Term Experience With a Mandatory Clinical Decision Rule and Mandatory D-Dimer in the Evaluation of Suspected Pulmonary Embolism JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY 2018; 15 (12): 1673-1680
Long-Term Experience With a Mandatory Clinical Decision Rule and Mandatory d-Dimer in the Evaluation of Suspected Pulmonary Embolism.
Journal of the American College of Radiology : JACR
This study evaluated the long-term effectiveness of mandatory assignment of both a clinical decision rule (CDR) and highly sensitive d-dimer in the evaluation of patients with suspected pulmonary embolism (PE).Institutional guidelines with a CDR and highly sensitive d-dimer were embedded in an order entry menu with mandatory assignment of key components before ordering a CT pulmonary angiogram (CTPA). Data were retrospectively extracted from the electronic health record.This was a retrospective review of 1,003 CTPA studies (905 patients, 845 male and 60 female patients, age 63.7 ± 13.5 years). CTPAs were positive for PE in 170 studies (17%), representing an average yield of 15% (year [average]; 2007 [15%], 2008 [18%], 2009 [15%], 2010 [15%], 2011 [17%], 2012 [15%], 2013 [23%]). The increased yield represented efforts of mandatory order entry assignments, educational sessions, and clinical champions. Different d-dimer thresholds with or without age adjustments in combination with the CDR identified about 10% of patients who may have been managed without CTPA.Mandatory assignment of a CDR and highly sensitive d-dimer clinical decision pathway can be successfully incorporated into an order entry menu and produce a sustained increase in CTPA yield of patients with suspected PE.
View details for PubMedID 29907418
- Hodgkin Disease Müller’s Imaging of the Chest Elsevier. 2018; 2
Lung Adenocarcinoma: Correlation of Quantitative CT Findings with Pathologic Findings
2016; 280 (3): 931-939
Purpose To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 41) with thin-section CT data were identified. Two readers independently placed over-inclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results Mean percentage solid volume of INV was 35.4% (95% confidence interval [CI]: 26.2%, 44.5%)-higher than the 14.5% (95% CI: 10.3%, 18.7%) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2% (95% CI: 2.7%, 13.7%) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2%; accuracy of the model based on total mass and percentage solid mass was 75.6%. Conclusion Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma. (©) RSNA, 2016.
View details for DOI 10.1148/radiol.2016142975
View details for Web of Science ID 000391311200033
View details for PubMedID 27097236
Reducing Unnecessary Portable Pelvic Radiographs in Trauma Patients: A Resident-Driven Quality Improvement Initiative
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
2015; 12 (9): 954-959
Quality improvement is increasingly important in the changing health care climate. We aim to establish a methodology and identify critical factors leading to successful implementation of a resident-led radiology quality improvement intervention at the institutional level. Under guidance of faculty mentors, the first-year radiology residents developed a quality improvement initiative to decrease unnecessary STAT pelvic radiographs (PXRs) in hemodynamically stable trauma patients who would additionally receive STAT pelvic CT scans. Development and implementation of this initiative required multiple steps, including: establishing resident and faculty leadership, gathering evidence from published literature, cultivating multidisciplinary support, and developing and implementing an institution-wide ordering algorithm. A visual aid and brief questionnaire were distributed to clinicians for use during treatment of trauma cases to ensure sustainability of the initiative. At multiple time points, pre- and post-intervention, residents performed a retrospective chart review to evaluate changes in imaging-ordering trends for trauma patients. Chart review showed a decline in the number of PXRs for hemodynamically stable trauma patients, as recommended in the ordering algorithm: 78% of trauma patients received both a PXR and a pelvic CT scan in the first 24 hours of the initiative, compared with 26% at 1 month; 24% at 6 months; and 18% at 10 to 12 months postintervention. The resident-led radiology quality improvement initiative created a shift in ordering culture at an institutional level. Development and implementation of this algorithm exemplified the impact of a multidisciplinary collaborative effort involving multiple departments and multiple levels of the medical hierarchy.
View details for DOI 10.1016/j.jacr.2015.02.015
View details for Web of Science ID 000360874300022
View details for PubMedID 25868670
- Atypical Carcinoid Specialty Imaging: Thoracic Neoplasms Elsevier. 2015; 1
- Typical Carcinoid Specialty Imaging: Thoracic Neoplasms Elsevier. 2015; 1
SUVmax correlates with degree of invasiveness in early lung adenocarcinoma categorized using the new IASLC/ATS/ERS multidisciplinary classification system
SOC NUCLEAR MEDICINE INC. 2012
View details for Web of Science ID 000443680200228
Incidental Extracardiac Findings at Coronary CT: Clinical and Economic Impact
AMERICAN JOURNAL OF ROENTGENOLOGY
2010; 194 (6): 1531-1538
The purpose of this study was to evaluate the prevalence of incidental extracardiac findings on coronary CT, to determine the associated downstream resource utilization, and to estimate additional costs per patient related to the associated diagnostic workup.This retrospective study examined incidental extracardiac findings in 151 consecutive adults (69.5% men and 30.5% women; mean age, 54 years) undergoing coronary CT during a 7-year period. Incidental findings were recorded, and medical records were reviewed for downstream diagnostic examinations for a follow-up period of 1 year (minimum) to 7 years (maximum). Costs of further workup were estimated using 2009 Medicare average reimbursement figures.There were 102 incidental extracardiac findings in 43% (65/151) of patients. Fifty-two percent (53/102) of findings were potentially clinically significant, and 81% (43/53) of these findings were newly discovered. The radiology reports made specific follow-up recommendations for 36% (19/53) of new significant findings. Only 4% (6/151) of patients actually underwent follow-up imaging or intervention for incidental findings. One patient was found to have a malignancy that was subsequently treated. The average direct costs of additional diagnostic workup were $17.42 per patient screened (95% CI, $2.84-$32.00) and $438.39 per patient with imaging follow-up (95% CI, $301.47-$575.31).Coronary CT frequently reveals potentially significant incidental extracardiac abnormalities, yet radiologists recommend further evaluation in only one-third of cases. An even smaller fraction of cases receive further workup. The failure to follow-up abnormal incidental findings may result in missed opportunities to detect early disease, but also limits the short-term attributable costs.
View details for DOI 10.2214/AJR.09.3587
View details for PubMedID 20489093
The rise and fall of insulin secretion in type 1 diabetes mellitus
2006; 49 (2): 261-270
An understanding of the natural history of beta cell responses is an essential prerequisite for interventional studies designed to prevent or treat type 1 diabetes. Here we review published data on changes in insulin responses in humans with type 1 diabetes. We also describe a new analysis of C-peptide responses in subjects who are at risk of type 1 diabetes and enrolled in the Diabetes Prevention Trial-1 (DPT-1). C-peptide responses to a mixed meal increase during childhood and through adolescence, but show no significant change during adult life; responses are lower in adults who progress to diabetes than in those who do not. The age-related increase in C-peptide responses may account for the higher levels of C-peptide observed in adults with newly diagnosed type 1 diabetes compared with those in children and adolescents. Based on these findings, we propose a revised model of the natural history of the disease, in which an age-related increase in functional beta cell responses before the onset of autoimmune beta cell damage is an important determinant of the clinical features of the disease.
View details for DOI 10.1007/s00125-005-0100-8
View details for Web of Science ID 000235130200003
View details for PubMedID 16404554
Natural history of beta-cell function in type 1 diabetes
6th Servier-IGIS Symposium
AMER DIABETES ASSOC. 2005: S32–S39
Despite extensive and ongoing investigations of the immune mechanisms of autoimmune diabetes in humans and animal models, there is much less information about the natural history of insulin secretion before and after the clinical presentation of type 1 diabetes and the factors that may affect its course. Studies of insulin production previously published and from the Diabetes Prevention Trial (DPT)-1 suggest that there is progressive impairment in insulin secretory responses but the reserve in response to physiological stimuli may be significant at the time of diagnosis, although maximal responses are more significantly impaired. Other factors, including insulin resistance, may play a role in the timing of clinical presentation along this continuum. The factors that predict the occurrence and rapidity of decline in beta-cell function are still largely unknown, but most studies have identified islet cell autoantibodies as predictors of future decline and age as a determinant of residual insulin production at diagnosis. Historical as well as recent clinical experience has emphasized the importance of residual insulin production for glycemic control and prevention of end-organ complications. Understanding the modifiers and predictors of beta-cell function would allow targeting immunological approaches to those individuals most likely to benefit from therapy.
View details for Web of Science ID 000233727300006
View details for PubMedID 16306337