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AMA Research Challenge 2022

3D Printed Anatomic Models for Removal of Complex IVC Filters

Welcome to the largest national, multi-specialty research event **featuring research from 750 medical students, residents and fellows, and international medical graduates.**


#### Event Details
The 2022 American Medical Association Research Challenge includes a poster symposium hall and a semifinals hall with the top scored research. Log in at any time throughout the event to:
* **View posters and presentations** on a variety of topics and specialties
* **Score posters in the semifinals** to help decide the top five finalists (AMA member exclusive opportunity)
* **Connect with participants** and provide feedback to help advance their research

At the conclusion of this event, the AMA will announce the top five finalists who will advance to the AMA Research Challenge finals, present their research to an elite panel of judges and compete to win a $10,000 grand prize sponsored by Laurel Road.
 

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**Oct. 20, 6 — 7 p.m. Central**
 **How to plan a career in academic medicine** 
 Learn how to plan a career in academic medicine and get your questions answered. [Access Here](https://underline.io/events/363/lounge)
 
 **Speakers**
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Ricardo Correa, MD, EdD
![](https://assets.underline.io/uploads/markdown_image/1/image/5f9437ab87861538bf02b9ca7c0d926e.jpg)
Stuart M. Greenstein, MD
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Stephanie Strohbeen, MD
 
 **Oct. 22, 11 a.m. — Noon Central**
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 **Speakers**
![](https://assets.underline.io/uploads/markdown_image/1/image/8ce6235e957d31cb5e6f9097cc6f6972.jpg)
Jessica Li, MD/PhD Candidate
![](https://assets.underline.io/uploads/markdown_image/1/image/9950ef2e0237f54cb3d8280334d42491.jpg)
Charles Lopresto, DO

### Sponsor
AMA would like to thank Laurel Road for sponsoring a $10,000 grand prize for the winner of the AMA Research Challenge. Laurel Road is not involved in the judging or scoring process to select the winner. The winner will be announced on Dec. 7 during the AMA Research Challenge finals. Payment of the grand prize will be directly from Laurel Road. See the AMA Research Challenge Official Rules for terms and conditions.

![](https://assets.underline.io/uploads/markdown_image/1/image/611e331d4a0eba0920f240cfc04b4d77.png)

**There is no registration fee for this event! **

Register to explore research in a variety of topics. AMA members get exclusive access to score posters in the semifinals.

**Please use the link below to register for free if you have not already done so:**

https://www.ama-assn.org/about/events/ama-research-challenge

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Welcome to the largest national, multi-specialty research event featuring research from 750 medical students, residents and fellows, and international medical graduates.


poster


Background
The use of Millimeter Wave (MMW) to Terahertz (THz) sensors in the diagnosis of thermal burn injury, radiation dermatitis, and other complications associated with tissue injury secondary to thermal, chemical, or radiation burns has not been previously seen. This ex-vivo investigation of pigskin utilizes MMW sensors to view the underlying skin damage caused by high-temperature internal and external thermal burns.

Methods
Fresh porcine skin is sectioned into 5 cm by 5 cm squares, and the subdermal adipose is removed revealing the dermis. A triangle brand is heated to 300ºC and placed on the underside of the porcine skin for 10 seconds inducing a thermal burn. Another sample group is burned on the epidermis at both 100ºC and 300ºC. The samples are then placed under the 94 GHz and 30 GHz MM-wave sensors, that generate 2D images of the epidermis. Punch biopsies are taken from the samples and sent to dermatopathology for analysis of thermal burn injury depth.

Results
The 94 GHz radiation penetrates the epidermis and upper dermis showing sensitivity to various changes in hydration and surface roughness. The triangle-shaped burn on the underside of the porcine skin was not visible from the epidermal side; however, the MMW-generated image revealed the presence of a triangular structure with lower reflectivity readings in deeper skin layers. The triangular 300ºC and 100ºC burns on the epidermis were clearly shown on the MMW images as areas of low and high reflectance, respectively. Punch biopsies are currently being analyzed to correlate differences in reflectance with the depth of burn to differentiate between second and third-degree burns. 30-GHz MMW sensor imaging studies show an increase in penetration depth of the waves at the expense of image resolution, indicating its use for deeper or thicker skin.

Conclusion
The use of MMW sensors may be used to view changes in skin hydration that are not directly visible to the clinician. Integration of this device with current imaging methods may yield valuable information about skin injury. This tool may aid in prompt clinical diagnosis between second and third-degree burns and subcutaneous thermal burn injuries caused by interventional procedures.


A Novel Method for Non-Invasive Medical Imaging of Thermal Burn Injuries Utilizing Millimeter Wave and Terahertz Technology

Purpose 
Growing evidence suggests sarcopenia, body mass index (BMI), and the inflammatory marker neutrophil-lymphocyte ratio (NLR) correlate with outcomes among patients undergoing radiotherapy (RT). Maintaining nutritional status and BMI among head and neck cancer patients can be difficult— especially among those undergoing RT. We evaluated whether pre-RT sarcopenia, BMI, and NLR were associated with treatment outcomes among patients with oropharyngeal cancer. 



Materials and Methods 
We retrospectively enrolled 172 patients with oropharyngeal cancer who underwent definitive RT+/-chemo at an academic center from 2013 to 2019. We measured bilateral temporalis muscle thickness (TMT) on CT imaging used for radiation plan design. Sarcopenic patients were defined by a TMT ≤ 25th percentile. We measured pre-RT NLR, with high NLR defined as ≥ 75th percentile. Patients were subdivided into four BMI categories including: underweight (BMI: <18.5), normal weight (BMI: 18.5-24.9), overweight (BMI: 25-29.9), and obese (BMI: ≥ 30). We recorded rates of overall survival, recurrence, and whether a PEG or NG tube was placed during treatment. 



Results 
In our patient cohort, 31%, 43%, 14%, and 10% had stage T1, T2, T3, and T4 primary site tumors by AJCC 8th edition staging, respectively. Furthermore, 6%, 61%, 22%, and 9% had stage N0, N1, N2, and N3 nodal involvement. 86% were HPV+, while 6% were HPV- and 6% were unknown. Sarcopenic patients (n = 43) were found to be older than non-sarcopenic patients (n = 129); (64 vs. 58, p < 0.001). Sarcopenia was associated with lower BMI. Among sarcopenic patients, 2.3%, 30%, 42% and 19% were underweight, normal weight, and obese, respectively. Among non-sarcopenic patients, 1.6%, 16%, 43% and 39% were underweight, normal weight, and obese, respectively (p = .063). No significant differences were found between patients with vs. without sarcopenia with respect to smoking history, p16 status, primary tumor site, chemotherapy exposure, tumor stage, or NLR. At 24-month follow-up, sarcopenia did not significantly impact overall survival (OS) or recurrence-free survival (RFS) (OS 92.6% vs 91.1%, p = 0.790; RFS 69.7% vs 75.1%, p=0.465). PEG/NG tube placement was not significantly different (60% vs 55% with and without sarcopenia, respectively, p=0.378). Age was an independent predictor of poorer OS (hazard ratio [HR], 1.1; 95% CI, 1-1.1; p = 0.0037), whereas greater BMI (HR, 0.57; 95% CI, 0.36-0.9; p = 0.015) predicted improved overall survival. 



Conclusions 
Pre-RT sarcopenia and NLR were not associated with survival and recurrence outcomes. However, pre-RT BMI was found to be of significant prognostic importance. This highlights the value of comprehensive nutritional support for patients undergoing head and neck radiotherapy. Special attention should be paid toward elderly patients as age predicted poorer overall survival.

Association between sarcopenia and overall survival among oropharyngeal cancer patients undergoing radiation therapy

PURPOSE 
This review was undertaken to compare the clinical outcomes of prostate artery embolization (PAE) in patients with >80ml vs <80ml prostatic volume (PV) at 24 month follow up to determine if prostate volume predicted the durability or efficacy of PAE. 
MATERIALS AND METHODS
The PubMed/MEDLINE database was searched for articles published between 2010 and 2022 using the search term “(prostat* artery embolization) AND (long term OR follow-up OR 24-month)”. Articles were included if they discussed PAE for BPH and reported a minimum follow up of 24 months. Articles with fewer than 10 patients were excluded. A subgroup analysis was performed to evaluate for any difference in clinical outcomes at 24 month post-PAE follow-up between studies with mean PV >80ml and those with mean PV <80ml.
RESULTS
17 studies with 2,762 patients were included, all of which demonstrated significant reduction of symptoms at 24 month follow up post-PAE. Four studies were included as part of the >80ml PV subgroup (n=467) and thirteen studies were included as part of the <80ml PV subgroup (n=2,299). There were statistically significant differences between the mean pre-procedural prostatic volume (122.4ml vs 64.0ml, p=0.0052) and mean pre-procedural PSA (7.2 vs 4.0, p=0.027). At 24 month follow up, the only clinical parameter that differed significantly between groups was the PSA (4.9 vs 2.4, p=0.003). The collective incidence of major adverse events reported was <1%.
CONCLUSION
PAE is both safe and durable for patients suffering from BPH, and can be utilized in a wide range of baseline prostate volumes.

Comparison of Long-Term Outcomes of Prostate Artery Embolization (PAE) in >80mL vs <80mL Prostates: A Systematic Review

Diagnosing Brain and Bone Cancers Using Artificial Intelligence and Imaging

Background: Advances in the field of radiotherapy planning have led to faster and more accurate planning for patients receiving radiation. One source of this advancement is the use of AI programs to assist with marking target structures and organs at risk (OAR) in a process known as contouring, on patient imaging. This is especially important regarding the HN, given the number and complexity of the organs in this region. 

Methods: Two AI programs dubbed V1 and V2 were utilized to generate contours for four OAR in Eclipse (v15.5, Varian Medical Systems, Inc.). Contours were generated from computed tomography (CT) images of three separate patients receiving radiotherapy for HN cancer at a rural tertiary care center. Dice Similarity Coefficients (DSC), a measure of the spatial overlap between image sets, were calculated between contours created in a medical student-physician team (denoted the G contours) and the AI program contours. Each CT slice for the G, V1, and V2 contours was also scored by a separate medical physicist and radiation oncologist team; these scores were termed subjective scores (SS). A score of 100% corresponded to all slices being deemed acceptable for radiotherapy planning for a given OAR.

Results: The average DSC for the V1 and V2 packages compared to the G contours were 64.6% and 61.6%, respectively (via matched pairs two-tailed t-test: mean difference = -2.98, p-value = 0.03). All SS were above 89%, and the overall average OAR SS were 94.3%, 95.4%, and 92.5% (for the G, V1, and V2 contours, respectively). Two-tailed paired Student’s t-tests between each AI program and the G contours yielded a p-value > 0.05 for the V2 program only.

Conclusion: The V1 and V2 auto contouring packages created OAR contours with similar DSC, though the V1 package demonstrated slightly better overlap with the G contours. SS for the V2 program only were not statistically different from the G contours. This implies the V2 package can create clinically useful contours for HN OAR, at the cost of a slightly decreased DSC compared to V1, translating to faster radiotherapy planning, helping both radiotherapy planning teams and patients alike.

Evaluating artificial intelligence (AI) programs for head and neck (HN) radiotherapy planning

Harnessing Artificial Intelligence For Improved Detection of Brain Metastases

Purpose: This QI study compared the completeness of HRCT radiology reports before and after the implementation of a disease specific structured reporting template for suspected cases of interstitial lung disease (ILD).

Materials and Methods: A pre-post study of radiology reports for HRCT of the thorax at a multicenter health system was performed. Data was collected in 6-month period intervals before (June 2019-November 2019) and after (January 2021-June 2021) the implementation of a disease specific template. The use of the template was voluntary. The primary outcome measure was the completeness of HRCT reports graded on the basis of the documentation of ten quality measures. The secondary outcome measure assessed the differences in reporting frequencies of each quality measure pre- and post-intervention.

Results: 521 HRCT reports before and 557 HRCT reports after the intervention were reviewed. Of the 557 reports, 118 reports (21%) used the implemented structured reporting template. The mean completeness score of pre-intervention group was 9.20 (SD = 1.08) and post-intervention group was 9.36 (SD = 1.03) with a difference of -0.155, 95% CI [-0.2822, -0.0285, p < 0.0001]. After the intervention, two of the ten quality measures improved significantly: presence of honeycombing from 78.3% to 85.1% (p <0.0039) and technique from 90% to 96.6% (p <0.0001). The rest of the quality measures did not show significant improvement.

Discussion: There are benefits to shifting to disease specific structured reporting for HRCT exams of suspected ILD. Further research on how to improve the voluntary uptake of a disease-specific template is needed to help increase the acceptance of structured reporting among radiologists.

Impact of Structured Reporting Template on the Quality of HRCT Radiology Reports for Interstitial Lung Disease

Purpose: We investigated whether deep learning techniques can be used in enhancing ultra-low-dose 18 F-PI-2620 tau PET/MR images, acquired in a healthy aging population as well as those with neurodegenerative diseases, to produce diagnostic quality images. Methods: 44 total participants were recruited for this study and 18 F-PI-2620 tau PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra- low-dose tau images, which were reconstructed from a random sampling of 1/20 (approximately
5% of original count level) of the original full-dose data; MR images were also utilized as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared to their full-dose counterparts. Results: The enhanced tau images showed significant noise reduction compared to the ultra-low-dose images. The regional standard uptake value ratios showed that while there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy but amyloid positive population, this bias was present in the unenhanced ultra- low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately
compared to their full-dose counterparts. Conclusion: The clinical readings of deep learning-enhanced ultra-low-dose tau PET images were consistent with those performed with full-dose imaging, suggesting the possibility to reduce dose and enable more frequent examinations in patients with dementia.

Using Deep Learning Networks to Improve Ultra-low Dose Tau PET/MR Image Quality

Background
Lung nodules identified by Low-Dose Computed Tomography (LDCT) are classified radiologically in the Lung Reporting and Data System (LungRADS). Herein, we elucidate clinical and radiomic factors associated with follow-up LungRADS 4 categorization and lung cancer diagnosis in patients with initial LungRADS 2-3 nodules.

Methods
We reviewed 2847 patients’ charts with LDCT screening between 2015 and 2019, identifying 2174 (76.7%) with LungRADS 2-3 baseline nodules, of which 908 had follow-up screening available (41.8%). We abstracted gender, race, smoking history, body mass index (BMI), history of chronic obstructive pulmonary disease (COPD), emphysema, pneumonia, cancer, and family history of lung cancer. Association with subsequent LungRADS 4 nodules and lung cancer was assessed via Cox proportional hazards regression. Among a subset of 132 patients, we performed radiomic analysis by annotating the largest nodule of each baseline scan and training machine learning models using 86 textural features. Clinical, radiomic and combined models were developed via Lasso regression and random forest analyses using five-fold cross-validation to predict a subsequent LungRADS 4 result or lung cancer diagnosis.

Results
Among patients with baseline LungRADS 2-3 nodules, 23 (1.1%) developed lung cancer. Age(p=0.04), pack-years (p<0.005), family history of lung cancer (p<0.005), COPD (p=0.02) and emphysema (p<0.005) were associated with lung cancer. 39 patients with follow-up screening (4.3%) progressed to LungRADS 4. Emphysema (p<0.005) was associated with this finding. Machine learning models trained with clinical variables yielded an average area-under-receiver operating characteristic curve (AUC) of 0.543 (random forest) and 0.799 (Lasso regression) for subsequent findings of LungRADS 4 and lung cancer. Radiomic analysis of the largest baseline nodule found an AUC of 0.574 (random forest) and 0.728 (Lasso regression) for findings of LungRADS 4 and lung cancer. Fusion of clinical and radiomic features yielded AUCs of 0.591 (random forest) and 0.718 (Lasso regression) for findings of LungRADS 4 and lung cancer. 


Conclusion
Clinical and radiomic factors showed association with lung cancer diagnosis and LungRADS 4 categorization on follow-up screening in patients with baseline LungRADS 2-3 nodules. Assessment of these features may aid to predict which patients with likely benign screening findings may progress to a higher LungRADS category or develop lung cancer.

Analysis of Clinical and Radiologic factors associated with follow up Lung RADS score and lung cancer diagnosis in a low-intermediate risk lung cancer screening population

Background: The development of tricuspid valve regurgitation (TVR) is a common complication of mitral valve disease. Although severe TVR is usually operated on at the same time as left-sided surgery, there is no clear consensus concerning treatment of mild-moderate TVR.

Methods: We searched electronic databases such as PubMed, Cochrane Library, Scopus, and Web of science to identify studies comparing concomitant tricuspid valve repair at the time of mitral valve surgery to mitral valve surgery alone in patients with mild to moderate TVR. Heterogeneity was measured via the I2 statistic. The Mantel-Haenszel method was used to pool study estimates and the restricted maximum-likelihood estimator was used to estimate between- study heterogeneity. Effect sizes are provided in the form of odds ratios (OR) with 95% confidence intervals (CI).

Results: Our analysis included a total of 25 studies (N = 8,980). The concomitant tricuspid repair group had significantly lower all-cause mortality (OR: 0.57; 95% CI: 0.44 to 0.74) and cardiovascular mortality (OR: 0.49; 95% CI: 0.29 to 0.84). However, the concomitant repair group also had higher odds of permanent pacemaker implantation (OR: 2.09; 95% CI: 1.45 to 3.00). Additionally, while there was no significant difference between the two groups in terms of tricuspid valve reinterventions, stroke, acute kidney injury, concomitant tricuspid valve repair showed statistically significant lower odds for hospitalization due to heart failure (OR: 0.55; 95% CI: 0.22 to 0.58).

Conclusion: Concomitant repair of the tricuspid valve in mild to moderate TVR is a promising intervention to reduce both all-cause and cardiovascular mortality in patients undergoing mitral valve surgeries; however, this risk needs to be weighed against the significantly increased risk of pacemaker implantation.

Concomitant tricuspid valve repair for mild or moderate tricuspid regurgitation for patients undergoing mitral valve surgery? A systematic review and meta-analysis for efficacy and safety

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A Novel Method for Non-Invasive Medical Imaging of Thermal Burn Injuries Utilizing Millimeter Wave and Terahertz Technology

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