United States

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.

AMA Research Challenge 2023

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

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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

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**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.

Revisiting Time Activity Curves in Breast MRI: Has it kept up with the times?

Background: Prostate cancer (PCa) is the second most commonly diagnosed malignancy and the second leading cause of cancer death in men in the United States. Traditional screening for PCa has included monitoring of prostate specific antigen (PSA) levels and systematic transrectal ultrasound-guided (TRUS) prostate biopsy. Recommendations for PCa screening are evolving with recent advances in multi-parametric MRI (mp-MRI) of the prostate. However, negative findings on mp-MRI of the prostate in patients with suspicion of PCa can lead to uncertainties regarding clinical management. This study therefore aims to evaluate the detection rate of PCa by TRUS biopsy in patients with negative mp-MRI.

Methods: The records of 253 patients without evidence of PCa on mp-MRI performed at the Virginia Commonwealth University Health System between 2018 and 2023 who also underwent TRUS prostate biopsies were retrospectively reviewed. Clinical information including age at mp- MRI, PSA levels, and prostate volume, as well as surgical pathology results, namely presence of malignancy, Gleason score (GS), and percentage of positive cores on biopsy, were recorded for these patients. Ultimately, the incidence of clinically significant PCa (GS ≥ 7) and clinically insignificant PCa (GS 6) detected on TRUS-guided biopsy was determined.

Results: Of 253 patients with negative mp-MRI findings, 126 (49.8%) had PCa at TRUS biopsy. Among these 126 patients, 37 had clinically significant PCa with GS 7-9 (14.6% of patients with negative mp-MRI, 29.4% of those with biopsy-proven PCa), and 89 had clinically insignificant PCa with GS 6 (35.2% of patients with negative mp-MRI, 70.6% of those with biopsy-proven PCa). Of the patients with clinically significant PCa, 89.2% (33 patients) had PCa up to GS 7, 5.41% (2 patients) had PCa up to GS 8, and 5.41% (2 patients) had PCa up to GS 9 on TRUS-
guided biopsy. The remaining 127 patients (50.2% of total patients with negative mp-MRI) had no evidence of PCa on TRUS biopsy. There was no statistically significant difference in age, PSA level, or prostate volume between patients with PCa and without PCa at biopsy.

Conclusion: This retrospective study confirms that a percentage of patients without evidence of PCa at mp-MRI of the prostate may still have clinically significant PCa and may benefit from further screening, including TRUS biopsy. Further research may be of benefit to determine optimal screening for PCa.

Detection Rate of Prostate Cancer at Systematic Prostate Biopsy in Patients with Negative mp-MRI of the Prostate: A Retrospective Study

Comparison of Phased-Array 3.0T and Endorectal 1.5 T Magnetic Resonance Imaging in Detection of Seminal Vesicle Involvement and Extracapsular Extension in Prostate Cancer; Using Pathological Findings at Prostatectomy as a Gold Standard

Abbreviated Breast MRI Protocol: A Feasible Alternative for Breast Screening

OBJECTIVE
Despite rising popularity and performance, studies evaluating the use of LLMs for clinical decision support are lacking. Here, we evaluate ChatGPT 3.5 and 4’s capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain.
METHODS
We compared ChatGPT's responses to the American College of Radiology (ACR) Appropriateness Criteria for breast pain and breast cancer screening. Our prompt formats included an open-ended (OE) and a select all that apply (SATA) format. Scoring criteria evaluated whether proposed imaging modalities were in accordance with ACR guidelines. Three replicate entries were conducted for each prompt, and the average of these was used to determine final scores.
RESULTS
Both ChatGPT 3.5 and 4 achieved an average OE score of 1.830 (out of 2) for breast cancer screening prompts. ChatGPT 3.5 achieved a SATA average percentage correct of 88.9%, compared to ChatGPT 4’s average percentage correct of 98.4% for breast cancer screening prompts. For breast pain, ChatGPT 3.5 achieved an average OE score of 1.125 (out of 2) and a SATA average percentage correct of 58.3%, as compared to an average OE score of 1.666 (out of 2) and a SATA average percentage correct of 77.7%. 
DISCUSSION
Our results demonstrate the eventual feasibility of using LLMs like ChatGPT for radiologic decision making, with the potential to improve clinical workflow and responsible use of radiology services. More use cases and greater accuracy are necessary to evaluate and implement such tools.


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

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Pleural Effusion Volume Estimation Using Handheld Lung Ultrasound

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