Clinical Focus

  • Diagnostic Radiology
  • Genitourinary Imaging
  • Urology Imaging
  • Gynecology Imaging

Academic Appointments

  • Clinical Assistant Professor, Radiology

Administrative Appointments

  • Co-Medical Director, Point of Care Ultrasound (2021 - Present)

Honors & Awards

  • Distinguished Honors Scholar, University of Cincinnati Honors Program (2008)
  • Summa Cum Laude, Unversity of Cincinnati (2008)
  • Phi Beta Kappa Society, University of Cincinnati (2008)
  • Alpha Omega Alpha Honor Medical Society, University of Cincinnati College of Medicine (2011)

Boards, Advisory Committees, Professional Organizations

  • Editorial board member, European Journal of Radiology Open (EJRO) (2021 - Present)
  • Member, Society of Radiologists in Ultrasound (SRU) (2021 - Present)
  • Member, SAR Disease Focused Panel (DFP) Pelvic Floor Dysfunction (2021 - Present)
  • Member, SAR International Task Force Committee (2021 - Present)
  • Member, Society of Advanced Body Imaging (SABI) (2021 - 2021)
  • Member, Society of Abdominal Radiology (SAR) (2019 - Present)
  • Member, American College of Radiology (ACR) (2019 - Present)
  • Peer reviewer, European Journal of Radiology (2019 - 2020)
  • Peer reviewer, Journal of Abdominal Radiology (2018 - Present)
  • Member, European Society of Radiology (ESR) (2016 - 2020)
  • Member, Radiological Society of North America (RSNA) (2013 - Present)

Professional Education

  • Fellowship: UCLA Radiology Fellowships (2018) CA
  • Residency: UCLA Radiology Residency (2017) CA
  • Internship: University of California Irvine Dept of Internal Medicine (2013) CA
  • Medical Education: University of Cincinnati College of Medicine Registrar (2012) OH
  • Board Certification: American Board of Radiology, Diagnostic Radiology (2018)

Community and International Work

  • RAD-AID Radiology Serving the World, China


    RAD-AID China

    Populations Served

    Underserved areas in China



    Ongoing Project


    Opportunities for Student Involvement


Current Research and Scholarly Interests

GU and Gyn clinical imaging

All Publications

  • Inflammatory pseudotumor-like follicular dendritic cell tumor of the spleen: a case report and approach to differential diagnosis. Radiology case reports Nguyen, A., Negrete, L. M., Bulterys, P. L., Shen, L. 2021; 16 (11): 3213-3216


    We present a case of an inflammatory pseudotumor-like follicular dendritic cell tumor of the spleen. The patient is a 44-year-old woman, without significant underlying history, who presented with nonspecific abdominal pain for a few months. Both a contrast enhanced computed tomography and magnetic resonance imaging revealed a new 2.5 cm enhancing splenic lesion, which demonstrated hypermetabolic activity on subsequent positron emission tomography and computed tomography scan. Since the lesion was new compared to more remote imaging and hypermetabolic, a splenectomy was performed. Pathology confirmed the diagnosis and demonstrated positivity for Epstein-Barr Virus .

    View details for DOI 10.1016/j.radcr.2021.07.078

    View details for PubMedID 34484521

  • Prevalence of Malignancy and Histopathologic Association of Bosniak Classification, Version 2019 Class III and IV Cystic Renal Masses. The Journal of urology Tse, J. R., Shen, L., Shen, J., Yoon, L., Kamaya, A. 2020: 101097JU0000000000001438


    PURPOSE: Bosniak Classification, version 2019 (v2019) describes two types of class III and IV masses each: 1) thick, wall/septa ≥4 mm (III-WS), 2) obtuse protrusion ≤3 mm (III-OP), 3) obtuse protrusion ≥4 mm (IV-OP), and 4) acute protrusion of any size (IV-AP). The purposes were to determine the prevalence of malignancy and histopathologic features of class III and IV masses and subclasses.MATERIALS AND METHODS: In this IRB-approved and HIPAA-compliant study, three fellowship-trained abdominal radiologists (R1-3) reviewed cystic renal masses that had tissue pathology and pre-operative renal mass protocol CT or MRI. Classes based on v2019 and prior classification systems were retrospectively re-assigned and associated with malignancy, aggressive histologic features (necrosis or high Fuhrman grade), and radiologic progression following resection.RESULTS: The final sample included 79 masses (59 malignant, 20 benign) from 74 patients. Based on v2019, prevalence of malignancy ranged from 56-61% (mean 60%) for class III and 83-83% (mean 83%) for class IV (p=0.036, 0.013, 0.036 for R1-3). Prevalence of malignancy within subclasses were: III-WS (47-53%); III-OP (71-85%); IV-OP (75-87%); IV-AP (87-95%; p=0.029, 0.001, 0.005). All readers were more likely to classify malignancies with aggressive histologic features as class IV (88-100%) rather than class III (0-12%; p=0.012, <0.001, 0.002), corresponding to a negative predictive value of 96-100%. Following treatment (mean follow-up length 1210 days), one patient developed metastases.CONCLUSIONS: Bosniak Classification, version 2019 can help risk stratification of class III-IV masses by identifying those likely to be malignant and have aggressive histologic features.

    View details for DOI 10.1097/JU.0000000000001438

    View details for PubMedID 33085925

  • Bosniak Classification of Cystic Renal Masses Version 2019: Comparison of Categorization using CT and MRI. AJR. American journal of roentgenology Tse, J. R., Shen, J. n., Shen, L. n., Yoon, L. n., Kamaya, A. n. 2020


    Please see the Author Video associated with this article. Background: Bosniak Classification, version 2019 recently proposed refinements for cystic renal mass characterization and now formally incorporates MRI, which may improve concordance with CT. Purpose: To compare concordance of CT and MRI in evaluation of cystic renal masses using Bosniak Classification, version 2019. Materials and Methods: In this IRB-approved and HIPAA compliant study, three abdominal radiologists (R1-R3) retrospectively reviewed 68 consecutive cystic renal masses from 45 patients assessed with both CT and MR renal mass protocols within a year between 2005-2019. CT and MRI were reviewed independently and in separate sessions, using both the original and version 2019 Bosniak Classification systems. Results: Using Bosniak Classification, version 2019, cystic renal masses were classified into 12 category I, 19 category II, 13 category IIF, 4 category III, and 20 category IV by CT and 8 category I, 15 category II, 23 category IIF, 9 category III, and 13 category IV by MRI. Among individual features, MRI depicted more septa (p<0.001, p=0.046, p=0.005 for R1-R3; McNemar's test) for all radiologists, though both CT and MRI showed a similar number of protrusions (p=0.823, 1.0, 0.302) and maximal septa/wall thickness (p=1.0, 1.0, 0.145). Of discordant cases with version 2019, MRI led to the higher category in 12 masses. Reason for upgrade was most commonly due to protrusions identified only on MRI (n=4), increased number of septa (n=3), and a new category of heterogeneously T1-hyperintense (n=3). Neither modality was more likely to lead to a category change for both version 2019 (p=0.502; McNemar's test) and the original Bosniak classification system (p=0.823). Overall inter-rater agreement was substantial for both CT (κ=0.745) and MRI (κ=0.655) using version 2019 and was slightly higher than that of the original system (CT κ=0.707; MRI κ=0.623). Conclusion: CT and MRI were concordant in the majority of cases using Bosniak Classification, version 2019 and category changes by modality were not statistically significant. Inter-rater agreements were substantial for both CT and MRI. Clinical Impact: Bosniak Classification, version 2019 applied to cystic renal masses has substantial inter-rater agreement and does not lead to systematic category upgrades with either CT or MRI.

    View details for DOI 10.2214/AJR.20.23656

    View details for PubMedID 32755181

  • Human-machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ digital medicine Patel, B. N., Rosenberg, L. n., Willcox, G. n., Baltaxe, D. n., Lyons, M. n., Irvin, J. n., Rajpurkar, P. n., Amrhein, T. n., Gupta, R. n., Halabi, S. n., Langlotz, C. n., Lo, E. n., Mammarappallil, J. n., Mariano, A. J., Riley, G. n., Seekins, J. n., Shen, L. n., Zucker, E. n., Lungren, M. n. 2019; 2: 111


    Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.

    View details for DOI 10.1038/s41746-019-0189-7

    View details for PubMedID 31754637

    View details for PubMedCentralID PMC6861262

  • Author Correction: Human-machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ digital medicine Patel, B. N., Rosenberg, L. n., Willcox, G. n., Baltaxe, D. n., Lyons, M. n., Irvin, J. n., Rajpurkar, P. n., Amrhein, T. n., Gupta, R. n., Halabi, S. n., Langlotz, C. n., Lo, E. n., Mammarappallil, J. n., Mariano, A. J., Riley, G. n., Seekins, J. n., Shen, L. n., Zucker, E. n., Lungren, M. P. 2019; 2 (1): 129


    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

    View details for DOI 10.1038/s41746-019-0198-6

    View details for PubMedID 33293693

  • Erratum: Author Correction: Human-machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ digital medicine Patel, B. N., Rosenberg, L. n., Willcox, G. n., Baltaxe, D. n., Lyons, M. n., Irvin, J. n., Rajpurkar, P. n., Amrhein, T. n., Gupta, R. n., Halabi, S. n., Langlotz, C. n., Lo, E. n., Mammarappallil, J. n., Mariano, A. J., Riley, G. n., Seekins, J. n., Shen, L. n., Zucker, E. n., Lungren, M. P. 2019; 2: 129


    [This corrects the article DOI: 10.1038/s41746-019-0189-7.].

    View details for DOI 10.1038/s41746-019-0198-6

    View details for PubMedID 31840097

    View details for PubMedCentralID PMC6904441

  • Pathological and 3 Tesla Volumetric Magnetic Resonance Imaging Predictors of Biochemical Recurrence after Robotic Assisted Radical Prostatectomy: Correlation with Whole Mount Histopathology. The Journal of urology Tan, N., Shen, L., Khoshnoodi, P., Alcalá, H. E., Yu, W., Hsu, W., Reiter, R. E., Lu, D. Y., Raman, S. S. 2018; 199 (5): 1218-1223


    We sought to identify the clinical and magnetic resonance imaging variables predictive of biochemical recurrence after robotic assisted radical prostatectomy in patients who underwent multiparametric 3 Tesla prostate magnetic resonance imaging.We performed an institutional review board approved, HIPAA (Health Insurance Portability and Accountability Act) compliant, single arm observational study of 3 Tesla multiparametric magnetic resonance imaging prior to robotic assisted radical prostatectomy from December 2009 to March 2016. Clinical, magnetic resonance imaging and pathological information, and clinical outcomes were compiled. Biochemical recurrence was defined as prostate specific antigen 0.2 ng/cc or greater. Univariate and multivariate regression analysis was performed.Biochemical recurrence had developed in 62 of the 255 men (24.3%) included in the study at a median followup of 23.5 months. Compared to the subcohort without biochemical recurrence the subcohort with biochemical recurrence had a greater proportion of patients with a high grade biopsy Gleason score, higher preoperative prostate specific antigen (7.4 vs 5.6 ng/ml), intermediate and high D'Amico classifications, larger tumor volume on magnetic resonance imaging (0.66 vs 0.30 ml), higher PI-RADS® (Prostate Imaging-Reporting and Data System) version 2 category lesions, a greater proportion of intermediate and high grade radical prostatectomy Gleason score lesions, higher pathological T3 stage (all p <0.01) and a higher positive surgical margin rate (19.3% vs 7.8%, p = 0.016). On multivariable analysis only tumor volume on magnetic resonance imaging (adjusted OR 1.57, p = 0.016), pathological T stage (adjusted OR 2.26, p = 0.02), positive surgical margin (adjusted OR 5.0, p = 0.004) and radical prostatectomy Gleason score (adjusted OR 2.29, p = 0.004) predicted biochemical recurrence.In this cohort tumor volume on magnetic resonance imaging and pathological variables, including Gleason score, staging and positive surgical margins, significantly predicted biochemical recurrence. This suggests an important new imaging biomarker.

    View details for DOI 10.1016/j.juro.2017.10.042

    View details for PubMedID 29128577

    View details for PubMedCentralID PMC6946378

  • Translabial US: Preoperative Detection of Midurethral Sling Erosion in Stress Urinary Incontinence. Radiology Viragh, K. A., Cohen, S. A., Shen, L., Kurzbard-Roach, N., Raz, S., Raman, S. S. 2018; 289 (3): 721-727


    Purpose To evaluate the performance of translabial (TL) US in preoperative detection of sling erosion into pelvic organs with cystourethroscopic and surgical correlation. Materials and Methods The study cohort included women who underwent surgery at a subspecialty center (from 2008 to 2016) for suspected mesh complications in the setting of previous midurethral sling placement for stress urinary incontinence (from 1999 to 2012) with available preoperative TL US imaging. Clinical information, the finding of sling erosion identified intraoperatively and at cystourethroscopy, and blinded dual-reader radiologic analysis of the TL US studies for mesh location (intraluminal, mural, or extramural) relative to pelvic organs (bladder, urethra, vagina, or rectum) were evaluated. The diagnostic performance of TL US was correlated with the reference standard of surgical findings. The consensus of two radiologists was recorded, and interobserver agreement was evaluated with the κ statistic. Results Of the 124 women who were suspected of having sling erosion (mean age, 57.5 years ± 11.1 [standard deviation]), 15 women (12.1%) had sling erosion into the urethra or bladder at surgery. Sensitivity and specificity for erosion at TL US were 53% (95% confidence interval: 45%, 62%) and 100% (95% confidence interval: 97%, 100%), respectively, when erosion was defined as only intraluminal mesh products. Sensitivity and specificity for erosion at TL US were 93% (95% confidence interval: 89%, 98%) and 72% (95% confidence interval: 65%, 80%), respectively, when erosion was defined as visualizing either intraluminal or intramural mesh products. Interobserver agreement (κ value) was 0.95. Cystourethroscopy had 67% sensitivity and 100% specificity for sling erosion. Conclusion Preoperative translabial US can be used to detect sling erosion into the lower urinary tract, with sensitivity up to 93% and specificity up to 100%. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Benson and Phillips in this issue.

    View details for DOI 10.1148/radiol.2018180786

    View details for PubMedID 30106346

  • Automatic Classification of Ultrasound Screening Examinations of the Abdominal Aorta. Journal of digital imaging Morioka, C., Meng, F., Taira, R., Sayre, J., Zimmerman, P., Ishimitsu, D., Huang, J., Shen, L., El-Saden, S. 2016; 29 (6): 742-748


    Our work facilitates the identification of veterans who may be at risk for abdominal aortic aneurysms (AAA) based on the 2007 mandate to screen all veteran patients that meet the screening criteria. The main research objective is to automatically index three clinical conditions: pertinent negative AAA, pertinent positive AAA, and visually unacceptable image exams. We developed and evaluated a ConText-based algorithm with the GATE (General Architecture for Text Engineering) development system to automatically classify 1402 ultrasound radiology reports for AAA screening. Using the results from JAPE (Java Annotation Pattern Engine) transducer rules, we developed a feature vector to classify the radiology reports with a decision table classifier. We found that ConText performed optimally on precision and recall for pertinent negative (0.99 (0.98-0.99), 0.99 (0.99-1.00)) and pertinent positive AAA detection (0.98 (0.95-1.00), 0.97 (0.92-1.00)), and respectably for determination of non-diagnostic image studies (0.85 (0.77-0.91), 0.96 (0.91-0.99)). In addition, our algorithm can determine the AAA size measurements for further characterization of abnormality. We developed and evaluated a regular expression based algorithm using GATE for determining the three contextual conditions: pertinent negative, pertinent positive, and non-diagnostic from radiology reports obtained for evaluating the presence or absence of abdominal aortic aneurysm. ConText performed very well at identifying the contextual features. Our study also discovered contextual trigger terms to detect sub-standard ultrasound image quality. Limitations of performance included unknown dictionary terms, complex sentences, and vague findings that were difficult to classify and properly code.

    View details for DOI 10.1007/s10278-016-9889-6

    View details for PubMedID 27400914

    View details for PubMedCentralID PMC5114229

  • MR anatomy and pathology of the ulnar nerve involving the cubital tunnel and Guyon's canal. Clinical imaging Shen, L., Masih, S., Patel, D. B., Matcuk, G. R. 2015; 40 (2): 263-74


    Ulnar neuropathy is a common and frequent reason for referral to hand surgeons. Ulnar neuropathy mostly occurs in the cubital tunnel of the elbow or Guyon's canal of the wrist, and it is important for radiologists to understand the imaging anatomy at these common sites of impingement. We will review the imaging and anatomy of the ulnar nerve at the elbow and wrist, and we will present magnetic resonance imaging examples of different causes of ulnar neuropathy, including trauma, overuse, arthritis, masses and mass-like lesions, and systemic diseases. Treatment options will also be briefly discussed.

    View details for DOI 10.1016/j.clinimag.2015.11.008

    View details for PubMedID 26995584