Resume-aware faculty matching

Find professors who actually fit you

Upload your resume. Four AI agents analyze your background, rank the faculty who fit, inspect their recent research, and help you draft outreach — grounded in their actual work, not templates.

Free to startNo credit cardCancel anytime
Top matches Balanced preset
Dr. Sarah Chen
Stanford · Interpretability · NLP
91
Dr. Marcus Holloway
MIT · Robotics · RL
84
Dr. Aisha Okonkwo
CMU · Fairness · HCI
82
Nova · Professor Researcher · re-ranking top 20…
Hanna M Zafar

Hanna M Zafar

Verified

University of Pennsylvania · Rehabilitation Medicine

Active 1997–2026

h-index22
Citations1.3k
Papers12320 last 5y
Funding
See your match with Hanna M Zafar — sign in to PhdFit.Sign in

About

Hanna M Zafar, MD MHS, is an Associate Professor of Radiology at the Hospital of the University of Pennsylvania. She is actively involved in medical staff at Penn Presbyterian Medical Center and serves as an attending radiologist in the Department of Radiology at the Hospital of the University of Pennsylvania. Dr. Zafar is also the Co-director of the Automated Radiology Recommendation Tracking Engine at the Hospital of the University of Pennsylvania, Pennsylvania Hospital, and Presbyterian Hospital. She holds the position of Vice Chair for Radiology Quality at the University of Pennsylvania. Her educational background includes a BA in Biological Basis of Behavior from the University of Pennsylvania (1995), an MHS in Health Policy from Johns Hopkins University Bloomberg School of Public Health (1998), and an MD from Jefferson Medical College (2002). Her professional focus encompasses radiology research, with contributions to developing algorithm-based recommendations, diagnostic certainty communication frameworks, and imaging risk assessment systems. Dr. Zafar's work emphasizes improving radiology practices through innovative strategies and quality improvement initiatives.

Research topics

  • Medicine
  • Radiology
  • Computer science
  • Medical physics
  • Internal medicine

Selected publications

  • Clinician Nudge to Gynecologic Oncology Referral at Suspected Ovarian Cancer Diagnosis: A Pilot Study

    Cancer Control · 2026-02-19

    articleOpen access

    IntroductionOnly two-thirds of patients with ovarian cancer ever see a gynecologic oncologist. Our objective was to examine the feasibility of an electronic health record-based nudge to clinicians for referral to gynecologic oncology at suspected ovarian cancer by imaging.MethodsWe developed a nudge, a short behavioral economics informed best practice advisory with a pended referral order for gynecologic oncology, for primary care, emergency medicine, and obstetrician/gynecology clinicians for when a patient had a O-RADS 4 or 5 lesion on imaging and had not already seen gynecologic oncology. In 2024, clinicians were sent the nudge within 2 business days of a patient's abnormal imaging through the electronic health record. Our primary outcome was referral rate to gynecologic oncology compared to a historic cohort of patients with O-RADS 4 or 5 lesions from 2020-2023.ResultsIn this prospective cohort study, we sent 20 clinician nudges for gynecologic oncology referral; six clinicians (30%) responded that the nudge changed their referral behavior. The 90-day referral rate was 75% compared to historic baseline of 61%. In the pilot, 92% patients undergoing surgery for complex adnexal mases had surgery with gynecologic oncology compared to historic baseline of 82%. One in four patients in the pilot were diagnosed with cancer, all early-stage disease.ConclusionsA clinician nudge for gynecologic oncology referral at suspected ovarian cancer diagnosis was acceptable and associated with 75% referral rate. A clinician nudge standardizes gynecologic oncology referral and may improve early detection of ovarian cancer. A randomized controlled trial of the clinician nudge is warranted.

  • Benign Lesions Scored as O-RADS US v2022 4 and 5 Categories: Prevalence and Morphologic Analysis

    Radiology · 2026-05-01

    article

    = .22). Conclusion Approximately half of the benign lesions scored as O-RADS US 4 or 5 were cystadenomas or cystadenofibromas, followed by nonneoplastic lesions (including physiologic cysts and endometriomas) and benign neoplasms (dermoids). A solid component was the most frequent imaging observation of benign lesions scored as O-RADS US 4 or 5. © RSNA, 2026.

  • Reply

    Journal of the American College of Radiology · 2025-11-20

    article
  • Potential Frameworks for Communicating Diagnostic Certainty in Radiology Reports: From the ACR Commission on Quality and Safety

    Journal of the American College of Radiology · 2025-08-05 · 5 citations

    article
  • Accuracy of O-RADS 4 and 5 Classification System in Diverse Patients in an Academic Health System: A Cohort Study [ID 1659]

    Obstetrics and Gynecology · 2025-05-15

    article

    INTRODUCTION: The Ovarian-Adnexal Reporting and Data System (O-RADS) 4 and 5 lesions correspond with a 10–50% and greater than 50% chance of malignancy, respectively. We examined the accuracy of these classifications across imaging type and patient demographic factors in predicting malignancy. METHODS: The university IRB reviewed and exempted this retrospective cohort study of patients with O-RADS 4 or 5 lesions on ultrasound (US) or magnetic resonance imaging (MRI) from July 1, 2020, to December 31, 2023 (n=425). We examined the positive predictive value (PPV) of O-RADS classifications in predicting malignancy in patients with diagnostic resolution, ie, who received surgery (n=266) or whose lesions resolved on follow-up imaging (n=44). RESULTS: Across all subgroups, the classifications corresponded with the percent chance of malignancy consistent with previously published studies: O-RADS 4 ranged from 0.13 to 1.0; O-RADS 5 ranged from 0.50 to 1.0. Both classifications were more accurate in MRI (0.91 and 0.28) than US (0.57 and 0.18). The PPVs of O-RADS 4 and 5 on US were similar across postmenopausal status: 17% and 56% PPV for premenopausal, 20% and 52% for postmenopausal for O-RADS 4 and 5, respectively. Stratifying by race, PPVs were within published estimates by imaging modality and O-RADS score. Rates of incomplete follow-up were higher for Black than White patients. CONCLUSIONS/IMPLICATIONS: We found that O-RADS scores remained highly predictive of malignancy in a clinical population diverse in age and race. There were modifiable disparities in follow-up by race. Validating diagnostic classification systems in diverse populations is essential for diagnostic and treatment equity.

  • Clinician nudge to gynecologic oncology referral at suspected ovarian cancer diagnosis: A pilot study

    Gynecologic Oncology · 2025-09-01

    articleOpen access
  • Gynecologic oncology referral rates of adnexal masses suspicious for ovarian cancer in an academic health system: A cohort study.

    Journal of Clinical Oncology · 2025-05-28

    article

    5544 Background: Ovarian-Adnexal Reporting Data System (O-RADS) is an international lexicon and risk stratification tool. O-RADS 4 or 5 lesions are complex adnexal masses that a 10-90% risk of malignancy, and national guidelines recommend gynecologic oncology referral. Our objective was to examine patient, clinician, and imaging factors associated with referral to gynecologic oncology for complex adnexal masses. Methods: This retrospective cohort study was exempt from IRB review. We identified all patients with O-RADS 4 or 5 lesions on ultrasound (US) or MRI from July 1, 2020 to December 31, 2023. Our primary outcome was referral to gynecologic oncology. We gathered patient demographic data and ordering clinician characteristics from electronic health records. We performed descriptive statistics and multivariate logistic regression of patient demographics and ordering clinician characteristics associated with gynecologic oncology referral. Results: Our cohort included 373 patients with O-RADS 4 or 5 lesions and no prior gynecologic oncology care. The referral rate to gynecologic oncology was 68%, and referral within 30 days of abnormal imaging was 43%. Time from abnormal imaging to referral ranged from 0 to 407 days (mean 15.3, median 4 days). In multivariate analyses, the likelihood of referral to gynecologic oncology was higher among patients with repeat abnormal imaging compared to those with single instance of abnormal imaging (aOR 20.61, 95%CI 2.63-161.6), O-RADS 5 lesions compared to O-RADS 4 lesions (aOR 9.15, 95%CI 3.47-24.85) and detection on MRI compared to US (aOR 7.79, 95%CI 1.57-38.65). The likelihood of referral to gynecologic oncology was lower among non-white patients (aOR 0.24, 95%CI 0.08-0.76). There were no differences by Hispanic ethnicity, rurality, insurance, or language. Referral was higher among patients whose imaging was ordered by an internal medicine clinician (aOR 3.89, 95%CI 1.48-10.20) compared to ob/gyn. Conclusions: One-third of patients with complex adnexal masses were not referred to gynecologic oncology. Disparities in referral to gynecologic oncology for complex adnexal masses rates based on patient race and ordering clinician specialty highlight the need for system-based approaches including clinician education or automated referrals. Gynecologic oncology referral after O-RADS 4/5. Multivariate OR (95%CI) Postmenopausal (≥55 years) 1.89 (0.95-3.74) Race - White Reference - Black 0.57 (0.27-1.21) - Asian 1.03 (0.30-3.58) - Some other race 0.24 (0.08-0.76) Ordering specialty - Obstetrics/Gynecology Reference - Emergency Medicine 0.93 (0.33-2.62) - Internal Medicine 3.89 (1.48-10.20) - Family Medicine 1.62 (0.66-3.98) - Other specialty 0.87 (0.27-2.76) Has PCP 1.66 (0.81-3.42) O-RADS - 4 Reference - 5 9.15 (3.27-24.85) Imaging - MRI 7.79 (1.57-38.65) - US Reference Repeat abnormal imaging 20.61 (2.63-161.79)

  • Follow-up care of patients with complex adnexal masses in an academic health system: A cohort study

    Gynecologic Oncology · 2025-09-01

    article
  • Developing Algorithm-Based Recommendations in the ACR: Defining a New Process

    Journal of the American College of Radiology · 2025-11-10

    article
  • Follow-Up Care of Patients With Complex Adnexal Masses in an Academic Health System: A Cohort Study [ID 1098]

    Obstetrics and Gynecology · 2025-05-15

    article

    INTRODUCTION: International guidelines recommend follow-up imaging and gynecologic oncology referral for patients with Ovarian-Adnexal Reporting and Data System (O-RADS) 4/5 lesions. We examined factors associated with guideline-concordant follow-up of these lesions in our health system. METHODS: The university IRB exempted this retrospective cohort study of patients with O-RADS 4/5 lesions on ultrasound (US) or magnetic resonance imaging (MRI) from July 1, 2020, to December 31, 2023. Patients with adnexal surgery within 120 days of detection were excluded (n=253). We performed descriptive statistics of our final cohort (n=172). RESULTS: 59.9% were not referred to gynecologic oncology (103/172, 59.9%). 65.7% had a documented pelvic examination after initial detection (113/172, 65.7%), 48.8% had generalist follow-up (84/172, 48.8%), and 15.7% had both generalist and gynecologic oncology follow-ups (27/172, 15.7%). 13.4% had ongoing lesion surveillance at data collection (23/172). 55.0% of the remaining (82/149, 55.0%) had benign follow-up imaging. 9.4% received surgery for persistent abnormalities (14/149, 9.4%) with three cancers diagnosed (3/149, 2.0%). Black compared to White patients had higher rates of incomplete or no follow-up imaging (14/40, 35.0% versus 18/91, 19.8%), follow-up pelvic examinations (30/40, 75.0% versus 62/91, 68.1%), and generalist visits (23/40, 57.5% versus 47/91, 51.6%), with lower rates of gynecologic oncology follow-up (13/40, 32.5% versus 43/91, 47.3%). Insured patients had higher rates of completed follow-up imaging (80/156, 51.3% versus 2/6, 33.3%). CONCLUSIONS/IMPLICATIONS: 22.7% (39/172) of patients with O-RADS 4/5 lesions had no or incomplete follow-up imaging. Higher rates of guideline discordant care among Black and uninsured patients suggest opportunities for care optimization.

Frequent coauthors

Labs

  • Hanna M Zafar LabPI

  • Resume-aware match score
  • Save to shortlist
  • AI-drafted outreach

See your match with Hanna M Zafar

PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.

  • Free to start
  • No credit card
  • 30-second signup