United States

Background and Objective: The complexities of the human psyche are tough to capture by a Cartesian system. Unfortunately, the medical enterprise takes pride in simplifying the human experience and disease into neatly organized diagnoses. While psychiatry as a field is much better than others in using the phenomenological approach to illness, this art has been slowly replaced with the rigid diagnostic criteria enshrined in the Diagnostic and Statistical Manual (DSM), now in its 5th edition. The phenomenological approach to diagnosis has been criticized for being overly subjective, lacking in theoretical framework, and relying on the metaphysical (spiritual or theological) aspects of the human self. 
However, experienced psychiatrists use their personal and clinical experiences to intuitively understand their patients’ mental-emotional struggles to arrive at the diagnoses without rigidly following the DSM. The objective is to fill the knowledge gap between the theory and the practice of the phenomenological approach to psychiatric diagnosis by the practice of empathic listening (EL). Additionally, we provide empirical and EEG (electroencephalographic) evidence for the theoretical framework and metaphysical aspects of EL. 
Methods and Results: Five detailed interviews were conducted using EL. Analytical autoethnography, a qualitative research method using Grounded Theory, was used to develop narratives. Verbal and non-verbal statements, the subjective thoughts and feelings of the interviewer, and the objective events in these narratives were open-coded to generate qualitative data, which are inductively analyzed to arrive at the Mindfulness-to-Transcendence (MT) framework of the EL process. Additionally, the neurofeedback/EEG recordings from the Brain-Computer-Interface (BCI) devices worn by the psychiatrist/provider and care recipients during their interviews revealed synchronized emergence of alpha/theta/gamma predominant waves which are reported as markers of mindfulness and Transcendence.
Discussion and Conclusion: The Transcendental state of mind is a metaphysical state that transpires during EL assessment. In such a state the psychiatrist’s assessment reaches beyond subjectivity, and their empathic understanding is free from self-identification and caregiver-burnout. Such a transcendental state empowers patients’ self-empathy, a first step in healing. 
This small pilot study serves as a proof of concept that EL assessment aides the phenomenological approach to psychiatric diagnosis. Without subjectivity, the diagnostic assessments may be more accurate than the currently prevalent Cartesian system. Additionally, EL care can prevent compassion burnout among clinical providers. In conclusion, the BCI-neurofeedback-informed EL assessment can be adapted to reinstate the phenomenological approach to psychiatric diagnostic assessment and care using DSM. Medical trainees may gain skills in EL assessments with BCI-evidence-based EL training in their curricula.


2023 AMA Research Challenge

Brain-Computer-Interface Devices Informed Empathic Assessment in Psychiatry: Evidence from a Case Series Study

poster

Welcome to the **largest national, multi-specialty research** event featuring research from nearly 700 medical students, residents, fellows, and international medical graduates.

Use the buttons below or navigation menu to access the event.
 


[![](https://assets.underline.io/uploads/markdown_image/1/image/3e33a8c754cc49610b30950617d813e7.png)](https://underline.io/events/434/sessions?searchGroup=all)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/551404d429c4e274f9a54cbdd7081918.png)](https://underline.io/events/434/posters?searchGroup=all)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/677d92f566b880cebd36905fc738fc10.png)](https://underline.io/events/434/lounge)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/c7392833c7261c3874c5b43fa2f4df19.png)](https://underline.io/events/434/help-desk)
 

#### Event Details

The 2023 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
* **Attend education sessions** with physician experts on key research topics

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. 

### Grand Prize 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 February 4, 2024 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 this link [here](https://www.ama-assn.org/about/events/ama-research-challenge) to register for free.**

Registration Is Required

Join the AMA Research Challenge poster symposium and semifinals to explore top research posters, score posters in the semifinals (an AMA member exclusive opportunity) and attend education sessions with experienced physicians to learn tips to advance your research and career. 

Background: Pulmonary nodules pose a significant diagnostic challenge in clinical practice. These small, often asymptomatic lesions can be benign or malignant, necessitating accurate localization and sampling for appropriate management decisions. Traditional bronchoscopic procedures have limitations in reaching peripheral lung nodules, particularly in challenging locations. Shape-sensing robotic-assisted bronchoscopy, utilizing the IonTM Endoluminal System, has emerged as a potential breakthrough in the field of pulmonary nodule management. This cutting-edge technology combines advanced imaging, robotic control, and shape-sensing capabilities to enhance the precision and efficacy of bronchoscopic procedures. By providing real-time 3D mapping and navigation guidance, it allows physicians to accurately locate and biopsy pulmonary nodules, even in challenging anatomical regions.
However, despite its promising potential, there is still a knowledge gap regarding the experience and outcomes of using shape-sensing robotic-assisted bronchoscopy for pulmonary nodules. This study aims to bridge this gap by investigating the real-world experiences and outcomes of this innovative technology as a solution for accurate pulmonary nodule management.
Methods: A retrospective analysis was conducted using NVivo software at Chandler Regional Medical Center. The analysis focused on patients who underwent shape-sensing robotic-assisted bronchoscopy for pulmonary nodules. Patient demographics, nodule characteristics, procedural details, diagnostic yield, complications, and overall effectiveness were assessed using NVivo.
Results: A total of 9 patients (5 males, 4 females) with suspected lung nodules were included in this NVivo retrospective analysis. The mean age of the patients was 71.3 years (range: 60-77). The commonly involved lobes were the left upper and lower lobes, and the right upper and middle lobes. The sizes of the nodules ranged from 0.3 cm to 3.2 cm. Needle and forceps biopsies were the most common techniques used for tissue sampling, followed by bronchoalveolar lavage (BAL). Positive findings included malignancy (adenocarcinoma), hamartoma, inflammation, infection, and cellular abnormalities. Negative findings indicated the absence of carcinoma, granulomas, malignancy, fungal organisms, and acid-fast bacteria. One rare case of Mantle cell lymphoma, presenting as a cavitary lesion in the lung, was also diagnosed.
Complications were observed in 2 cases, including small pneumothorax, which were appropriately managed. The analysis also revealed gender differences in complications, with females experiencing pneumothorax and minimal bleeding, while males only suffered minimal bleeding.
Conclusion: This study unveils the transformative potential of shape-sensing robotic-assisted bronchoscopy to enhance diagnostic accuracy and reduce complications. This cutting-edge approach
revolutionizes medical practice, empowering physicians with precise tools while minimizing interventions.

Unleashing Precision: Shape-Sensing Robotic-Assisted Bronchoscopy for Pulmonary Nodules

Background: Obstructive sleep apnea (OSA) is associated with oxidative stress/inflammation, but indices of cytokine activation as measured by soluble cytokine levels, remains relatively unexplored. Moreover, how cytokine activation may be modified by continuous positive airway pressure (CPAP) and compliance to CPAP remains unknown. One critical molecular checkpoint for cytokine production is post-transcriptional control by microRNAs (miRs). The central hypothesis of this study was that cytokine activation occurs with OSA and is associated with a shift in a specific cassette of miRs which genetically map to these cytokines. Additionally, we postulated that CPAP would modify cytokine activation and miRs.

Methods: Following informed consent, plasma profiles for cytokine activation (Milliplex, multiplex array) and miRs (Qiagen, miRNA PCR array) were performed in OSA patients (N=50) at baseline and following 90 days of CPAP therapy. CPAP compliance was monitored across the therapeutic interval and graded based on Centers for Medicare and Medicaid Service (CMS) criteria. Profiles were indexed to referent normal subjects (N=18). 

Results: Cytokine activation was evident in OSA patients such as an 8-fold increase in tumor necrosis factor receptor superfamily, member 8 (sCD30) and a 2-fold increase in epidermal growth factor receptor (sEGFR) and vascular endothelial growth factor receptor 2 (sVEGFR2) (p<0.05). Following 3 months of CPAP, cytokine activation was reduced in CPAP compliant patients (N=22) as evidenced by an average 14% reduction in sCD30, sEGFR, Interleukin 1 receptor, type I (sIL1RI), interleukin 1 receptor, type II (sIL1RII), Interleukin 2 receptor, alpha (sIL2Ra), vascular endothelial growth factor receptor 1 (sVEGFR1), and sVEGFR2 whereas these values were unchanged in CPAP non-compliant patients (N=28). A total of 7 miRs were changed in OSA patients, which mapped to several cytokine pathways (p<0.05). Notably, hsa-miR233-3p, hsa-miR233-5p changed with CPAP compliance and maps to a localized inflammatory process- the inflammasome.

Conclusion: A specific set of soluble cytokine receptors and regulatory miRs, including: sCD30, sEGFR, sIL1RII, sVEGFR2, hsa-miR233-3p, hsa-miR233-5p, may serve as a marker of inflammation severity in patients with OSA, and as an approach to determine therapeutic responses to treatment strategies such as CPAP.

Obstructive sleep apnea causes cytokine activation and alters microRNA levels; Effects of continuous positive airway pressure

Background: Mental illnesses following an ICU stay are prevalent among patients and can be triggered by physical and emotional stress associated with critical illness, compounded by invasive procedures and sedatives administered in the ICU setting. Despite their frequency, these conditions are often not recognized or addressed in the ICU setting. However, in recent years, the mental health outcomes of patients who have survived an ICU stay have been a growing concern, and evolving methodologies are on the ascent particularly in European countries. The goal of this study is to assess the efficacy of existing methods for prevention of post-ICU mental health disorders and to analyze advancing techniques implemented in other countries.
Methods: This study employed a systematic literature review utilizing PubMed, EMBASE, and Wiley online databases to investigate the mental health outcomes linked to ICU experiences and the efficacy of current methods of prevention.
Results: Mental health conditions are frequently observed in patients following an ICU stay, with anxiety occurring in 12% to 43%, depression in 10% to 30%, and PTSD in 64% of patients. Current preliminary approaches to prevention include ICU journals, psychological counseling, and follow-up care in outpatient settings. The aforementioned strategies have shown varying statistical efficacy in terms of reducing the incidence of new onset mental health disorders. A systematic review on the impact of ICU diaries reported that four of five randomized trials showed a significantly reduced rate of new onset PTSD after 3 months with the use of ICU diaries. In addition, a meta-analysis of studies on psychological interventions, such as counseling, found that these interventions can significantly reduce symptoms of anxiety and depression. Furthermore, one study found that follow-up consultations may reduce the symptoms of PTSD after ICU admission, although they did not significantly improve other factors such as quality of life, anxiety, or depression.
Conclusion: Patients that survive an ICU stay are at a significant risk for developing a mental illness following their discharge. While the aforementioned studies provide evidence for the efficacy of these approaches, further research is needed to determine the most effective strategies for preventing new-onset mental health diagnoses in patients after ICU stay, with the ultimate objective of establishing proper guidelines to better navigate this issue. By prioritizing mental health in the ICU setting and providing appropriate resources to address these conditions, we can improve outcomes for patients and support their overall well-being beyond the acute care setting.

Preventing New Onset Mental Health Diagnoses After ICU Stay: Current and Emerging Approaches

Background
Hypertonic saline is a mainstay treatment of cerebral edema in neurological emergencies. The risk of acute kidney injury (AKI) with use of balanced versus unbalanced hypertonic saline solutions is equivocal across studies. Hypertonic sodium acetate (NaOAc) instead of hypertonic sodium chloride (NaCl) has been used as an alternative to prevent renal injury, with little evidence supporting this. We evaluated patients receiving either NaCl or NAOAc to evaluate the risk of development of AKI.

Methods
We performed a retrospective analysis of patients admitted to our neuroscience ICU from June 2013 to February 2023 who have received continuous intravenous infusions of either NaCl or NaOAc at either 2% or 3% concentration for therapeutic hypernatremia. We excluded patients receiving renal replacement therapy, patients who expired within 24 hours of administration of hypertonic saline, and those receiving treatment for hyponatremia. Renal injury was based of KDIGO criteria (elevation in serum creatinine greater than 0.3 mg/dL within 48 hours of starting intravenous infusion).

Results
Out of 1133 patients who received hypertonic saline, 880 met study inclusion criteria, with 812 patients receiving NaCl and 68 patients receiving NaOAc. Of these patients, 30.4% had acute ischemic strokes, 29.7% had intracerebral hemorrhages, 15.0% had subarachnoid hemorrhages, and 24.9% had other intracranial pathologies. 399 (38.5%) of the patients were female. 79 patients (9.7%) receiving sodium chloride developed AKI, while 11 patients (16.2%) receiving sodium acetate developed an AKI (X2 = 2.8408, p = 0.092) within 48 hours of initiation of intravenous therapy. 

Conclusion
Contrary to some reported observations, we found no statistical difference in the development of AKI as defined by KDIGO criteria in patients treated with NaCl versus NaOAc. Our study is limited by a small subset of patients receiving NaOAc. A larger cohort is needed to establish the protective effect of balanced hypertonic saline on AKI.

Effects of Balanced Versus Unbalanced Hypertonic Saline on Renal Function in Neurocritical Care

Background: Point-of-Care-Ultrasound (POCUS) has become an increasingly useful tool in modern medical practice. These non-invasive diagnostic instruments can be used to quickly assess many pathologies, including pleural effusion. In particular, there are several equations described in the literature that aim to estimate pleural effusion volumes using lung ultrasound (US) imaging. Among these described equations, we seek to determine the accuracy of these formulas with consideration of their practicality in clinical use. 
Methods: To assess this, a search was conducted using PubMed in which fourteen articles were found. We included articles that attempted to answer the question regarding volume estimation using POCUS within the last 20 years. Within these articles, ten different formulas were identified which were defined and described in relation to the patient’s position, the probe orientation, and the distance between the thoracic wall and lung or lung to the diaphragm. Our exclusion criteria were based on the age of the article, availability of free publications, review of the articles’ abstracts, determining their relevance to fluid volume estimation, and their use of lung ultrasound for pleural effusions. 
Results: Upon review, we determined that the pleural effusion estimated volume (EV) using the Goeke and Schwerck 2 formula (EV= (H + D) x 70, where H is the craniocaudal extent of the effusion at the dorsolateral chest wall and D is the distance from lung base to the apex of diaphragm cupola) in erect patients yielded the most accurate results. The most accurate simplified formula, requiring only one variable measurement in erect patients, proved to be the Hassan formula (EV = H x 100). With consideration of practicality in the Point-of-Care setting and patient’s performance status, we determined that the Balik formula (EV = C x 20, where C is the maximum perpendicular distance between the pulmonary surface and the chest wall) and Usta formula (EV=D×16, where D=distance from lung base to the apex of the diaphragm cupola) provide a sensitive and practical approach for measuring pleural effusion volume in the supine position when compared with other equations, albeit sacrificing accuracy.
Conclusions: In patients with reduced performance status and inability to be placed in the erect position, the Balik and Usta formulas provide a sensitive and practical approach for measuring pleural effusion volume when compared with the gold standard left lateral decubitus chest X-ray. In addition, our investigations noted that the most accurate equation used as an estimation tool for pleural effusion volume across multiple studies proved to be the Goecke and Schwerck 2 formula in erect patients. The most accurate simplified formula proved to be the Hassan formula, sacrificing some accuracy for practicality. With this in mind, providers should note the patient’s positioning (supine vs. erect) when performing a POCUS examination to decide which formula is best to use. POCUS offers a practical way to measure pleural effusion volume that facilitates timely care, supports and considers the patient’s performance status, and reduces both patient and hospital costs.


Pleural Effusion Volume Estimation Using Handheld Lung Ultrasound

Purpose: MRI may be useful to identify women with cervix cancer at high risk of disease progression to test strategies of treatment intensification. The purpose of this study was to determine the value of a machine-learning model built on pre-treatment MRI for prediction of risk of progression after radiation therapy. 

Methods/Materials: Magnetic Resonance Imaging (MRI) Data for 54 women with cervix cancer was collected from The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma Collection (TCGA-CESC) on the Cancer Imaging Archive (TCIA), which reported clinical, treatment, and imaging data from a single institution. 27 patients that had received radiation for cervix cancer were selected for input into a custom 3-D Residual Neural Network (ResNet) model with added custom layers specific to DICOM data in tensorflow python package. One T2 MRI per patient was used to predict progression free survival after radiation treatment, where patients were predicted to be “high risk” or “low risk” of progression as the output of the model. All slices of the T2 MRI were used. Model was validated using 5-fold cross validation; where 80% of the data was used to train each fold and 20% was used for validation. Final model statistical significance was confirmed through shuffle test at the p < 0.01 level. The clinical outcomes of patients predicted to be low risk or high risk by the machine learning model are reported.

Results: Median follow-up of patients was 29 months (range 3-64). Mean age was 51 years (range 29-79). 10 women were treated with radiation alone and 17 with chemo-radiation. 5 women received surgery in addition to radiation or chemoradiation. 21 patients received brachytherapy. Staging was IB in 9 women, IIA in 2, IIB in 9, IIIA in 2, IIIB in 2, IV in 3. 20 patients had squamous cell carcinoma and 7 patients had adenocarcinoma. 7 patients were predicted to be high risk by the model, and of these 7 developed progression during follow up. None of the 20 patients predicted to be low risk developed progression. Among all patients in the study, the 2 year progression free survival (PFS) was 82.0%. Among patients predicted to be low risk by model, the 2 year PFS was 100%, and for patients predicted to be high risk by model, the 2 year PFS was 43%. Of patients with progression, 3 developed local recurrence and 4 developed distant progression. ResNet model achieved cross-validated accuracy of 92% for prediction of progression free survival. Model was significant at p < 0.01 by shuffle test.

Conclusion: A 3-D ResNet machine-learning model built upon pre-treatment MRI data for cervical cancer was able to accurately predict clinical outcomes following radiation therapy in a single institution dataset. Future work to confirm generalizability should focus on validation on a larger clinical dataset.

Machine-Learning Model of Pre-Treatment MRI Accurately Predicts Progression Following Cervix Cancer Radiotherapy

Background:
Breast cancer is the most commonly diagnosed cancer type, accounting for about 2.3 million new cases and 685,000 deaths worldwide in 2020. In the US, breast cancer screenings are routinely performed in women starting from the age of 40. One major challenge with breast screening is the high rate of false positives, with over 50% of women experiencing at least one false positive result over 10 years of annual screening on plain film mammography, the most common breast cancer screening modality. Deep learning methods have been shown to be effective in reducing false positive rates that are associated with unnecessary costs, medical procedures, and emotional burden on patients, as well as improving the accuracy of cancer detection in screening mammograms — hence, this study investigates the use of deep learning methods for breast cancer detection. Our aim was to investigate and evaluate the performance of transfer learning and deep learning models in breast cancer detection.

Methods:
We trained and evaluated our deep learning model using multi-center breast screening data provided by Kaggle in the RSNA Screening Mammography Breast Cancer Detection competition. Our model uses mammogram images as input to fine-tune a pre-trained ResNet-50 deep-learning model via transfer learning and outputs the probability of cancer in each image. The model performance is evaluated using a held-out test-set (train:validate:test 70:10:20) to compute the sensitivity, specificity, and probabilistic F1-score achieved.

Results:
Overall we achieved a classification sensitivity of 1.00 and a specificity of 1.00 (ROC-AUC=1.00) on our held-out test set, with a probabilistic F1 score of 0.795 on our held-out test set. 

Conclusion:
Our model detected breast cancer with 100% sensitivity and specificity in the held-out test set. Future work may focus on validating algorithm performance in a larger out-of-distribution patient cohort. Application of deep learning models to breast cancer detection in mammograms may lead to improved clinical efficiency, diagnostic accuracy, and reduction in costs associated with the high false positive burden in breast cancer screening.


Deep Transfer Learning for Breast Cancer Detection Using 2D Screening Mammography

**Scoring is only possible in the semifinals hall**

**Abstract:**



T2 White Matter Hyperintensity Volumes Are Correlated With Poststroke Neuropsychological Deficits

Background
Renal cell carcinoma (RCC) is a highly heterogenous cancer that typically relies on biopsy to identify the specific tumor subtype and identify high-grade histology. However, accuracy and reliability of biopsy is sometimes limited by tumor heterogeneity. The use of 3D printing to improve the accuracy of this process has been previously described for prostate and renal cancers. Preoperative imaging is used to 3D print “negative” molds matching the contour of the surgically removed tumor and provide guiding lines along which to cut precise slices that correspond with imaging slices. Our objective was to design a novel technique combining negative molds with additional 3D-printed overlays, or “positive” molds that would fit onto the pathology slices and have cutouts to denote regions of interest previously identified on imaging, further facilitating the sectioning process and correlating pathology findings to imaging in RCC.

Methods
This was a single-center, IRB-approved, prospective study in patients with RCC who planned to undergo partial or radical nephrectomy. Preoperative images were transferred to a viewing software (3D Slicer), and their data was used to generate “positive” and “negative” 3D -printed molds. Following nephrectomy, kidneys were sectioned by a study pathologist and study radiologist using the 3D-printed molds. Pathology slides were then directly compared to CT by a radiologist using 3D Slicer and Docker to determine degree of pathology-to-radiology correlation. 

Results
11 total 3D-printed molds were constructed. Mold failure was defined by cases where 3D-printed molds could not be used throughout the entire sectioning process, or where lesions on pathology did not align with preoperative imaging. Mold success was defined by cases with complete mold-assisted sectioning and correlation of pathology to radiology images. The first 5 molds produced failed, either due to tumor growth between imaging and sectioning (n=3) or perirenal fat (n=2). Improvements in mold design using a bivalving technique were achieved following discussion between the study urologist, pathologist, and radiologist, and the subsequent 6 molds produced were successful.

Conclusion
In this study, we aimed to develop a novel technique for designing patient-specific renal molds to improve the diagnostic accuracy of tumor grading in renal cell carcinoma (RCC), which we achieved following multidisciplinary discussion and process revision. This is the first study to describe such an approach using negative and positive 3D-printed molds, and further research is warranted to validate the efficacy and efficiency of our proposed methodology in order to better standardize tumor sectioning and advance the diagnostic process for RCC.


3D Printed Molds for Pathology Sectioning of Renal Masses: Enabling Radiology-Pathology Correlation

Background:
Magnetic Resonance Imaging (MRI) has been a large component of the evaluation of breast lesions in high-risk women for the last decade. Many of these imaging tools have been developed to assist physicians in risk stratifying patients. Specifically, Time Activity Curves (TAC) have been utilized to incorporate contrast uptake patterns into decision making. In the years since its inception, new tools have been developed to more efficiently and accurately classify different TACs, such as DynaCAD. Previous studies on the use of TAC curves have indicated their accuracy is not sufficient to be utilized without morphological and historical data being incorporated, but it is unclear how the improvements noted in these tools affects these results. Therefore, we re-evaluate TACs within a broad patient population and treatment landscape.
Methods:
We performed a retrospective chart review of 48 patients who received both an MRI with DynaCAD TAC analysis followed by a diagnostic biopsy. Of the 48 patients evaluated, 18 were malignant and 30 were benign. We performed statistical analysis for sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) on this set of patient results, stratifying by the individual kinetic classification as well as the generally accepted associations of each type with malignancy (Type 1 classically benign, Type 2 and 3, concerning for malignancy). We performed a literature search for previously reported TAC statistics.
Results:
From the 48 patients evaluated, washout curves utilized alone as a marker of malignancy status resulted in a sensitivity of 64.3%, specificity of 73.5% and PPV of 50% and NPV of 83%. When stratified by individual TAC curve type, persistent was calculated to have 50% and 81.1% sensitivity and specificity, respectively. Plateau was calculated to have a 72% and 94% sensitivity and specificity, respectively. Washout was calculated to have a 78.3% and 76.9% sensitivity and specificity, respectively.
Conclusion:
For years, TAC curves have been utilized to enhance radiologists’ ability to classify breast lesions as likely benign or malignant in high-risk patients. In our study we evaluated the accuracy of TACs with the new improvements provided by more advanced commercial tools. We have found TAC curves have improved in their accuracy with the rapid improvement of analysis tools. However, their primary benefit lies in being an ancillary tool for determining benignlesions along with morphological data and they still struggle in accurately predicting malignancy on biopsy. The study is limited due to its lower total patient population.

Premium content

Brain-Computer-Interface Devices Informed Empathic Assessment in Psychiatry: Evidence from a Case Series Study

Downloads

Next from 2023 AMA Research Challenge

Unleashing Precision: Shape-Sensing Robotic-Assisted Bronchoscopy for Pulmonary Nodules