Citations

Abstract Objectives Brain tumors, including pituitary adenomas (PA), cause anxiety and distress, with a high unmet need for information correlating with increased anxiety. Condition-specific education may alleviate anxiety. We explored patients’ experience around the diagnosis of a PA and piloted a patient education intervention to address peridiagnostic anxiety in adults diagnosed with PA. Methods Anxiety, patient satisfaction, patient knowledge, and need for information were measured prior to, immediately after, and 1 month following the appointment in this multimethods study. A phone interview to explore patient diagnostic and intervention experiences was analyzed using qualitative methods. Results A total of 17 patients participated in the study; 15 completed the interview. The baseline need for information was high. Disease-specific anxiety decreased, and patient knowledge and satisfaction increased significantly after the initial visit. Interview analysis identified 3 main themes: (1) the importance of communication; (2) the need for information; and (3) the impact of the diagnosis on patient experience. Conclusions For patients with newly diagnosed PA, the diagnostic experience was associated with high levels of anxiety. Patients expressed a need for information. Information delivery reduced anxiety and had a positive impact on patient satisfaction. Practice Implications The study findings suggest a need for a streamlined diagnostic process with readily accessible information.

Advances in the treatment of brain tumors have led to an increase in the number of survivors of this disease. Consequently, the long-term complications associated with past and current treatments are becoming more apparent. Of relevance to patients who receive treatment of brain tumors are the potential neuroendocrine complications that develop either acutely or several years following treatment. Presentation may differ between adults and children (e.g., short stature or adult growth hormone deficiency) but in both settings can complicate treatment and impact quality of life. The risk for the development of these complications depends on the location of the tumor (proximity to the pituitary/hypothalamus) and/or the treatment delivered (chemotherapy/surgery/radiation). Given the potential overlap in symptoms attributable to the underlying brain tumor and neuroendocrine dysfunction, a high level of suspicion, appropriate investigation, and administration of treatment may reduce morbidity and mortality for patients with brain tumors experiencing neuroendocrine dysfunction.

Abstract Introduction Tumefactive multiple sclerosis (MS) and glioblastoma (GBM) can be difficult to differentiate. Clinical presentations are nonspecific and may occur in patients with both diseases. Magnetic resonance imaging (MRI) scans are routinely used to generate an initial differential diagnosis. Although imaging features can help distinguish these entities, there are no pathognomonic signs, neither on traditional nor advanced MRI. Moreover, the ability to differentiate these entities depends on the radiologists’ experience. Ensuring the correct diagnosis has important therapeutic and prognostic implications, as the management of these diseases is very different. To date, a brain biopsy often represents the only option to obtain a diagnosis, but it may be inconclusive and could add unnecessary morbidity. We hypothesize that a machine-learning tool based on routinely performed MRI scans could guarantee a more reliable, fast, and reproducible diagnosis. Methods Only patients with a histological diagnosis were included in the study. Patients that underwent any form of radiation therapy before the surgery were excluded to avoid confounding factors due to treatment-related effects. A total of 99 MS and 257 GBMs were included, and preoperative T2-weighted images were collected. For each patient, the lesions' segmentation was performed, including both the contrast-enhancing and non-enhancing areas of the lesions. The segmentation masks were used to extract radiomic features from the T2-weighted sequences. All MRI images were standardized to the same mean and standard deviation before feature extraction and resampled to a voxel size of 1.0 × 1.0 × 1.0 mm. Features extraction was performed using Pyradiomics (v. 3.0.1) and included first order, shape, and texture features, totaling 100 features. The classifier consisted of a support vector machine (SVM) model. Patients were randomly assigned to the training and test sets with an 80-20% proportion for each class. To select the best hyperparameters for the SVM classifier, a grid-search analysis was performed on the training set using a nested cross-validation scheme (k=5). All experiments were performed using Python (v. 3.5) and the Scikit-learn library (v. 0.23.1). Results The mean accuracy and area under the ROC curve for the best set of hyperparameters on the validation set were 0.83 and 0.86, respectively. The weighted-average precision, recall, and F1 scores on the test set were 0.78, 0.76, and 0.77, respectively. Conclusions Radiomic features extracted from a routinely acquired MRI sequence showed promising results in differentiating tumefactive multiple sclerosis from glioblastoma, opening the possibility to obtain a reliable diagnosis without the need for a brain biopsy. Future experiments Future experiments will focus on using post-contrast T1-weighted images for the radiomic features extractions, testing multiple machine learning models and features selection algorithms, and including more diagnostic classes (i.e., metastasis, lymphomas). Citation Format: Gian Marco Conte, Jeanette E. Eckel-Passow, W. Oliver Tobin, Paul Decker, Daniel H. Lachance, Robert B. Jenkins, Bradley J. Erickson. Differentiation of tumefactive multiple sclerosis and glioblastoma using radiomics features extracted from magnetic resonance imaging and machine learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-023.

431 Background: Patients (pts) with localized PC do not routinely undergo comprehensive genomic profiling (CGP) unless they develop recurrent or metastatic disease. KRAS is the most frequently mutated gene in PC, however, the impact of different KRAS mutations in localized PC has not been well characterized. We interrogated our genomic database to analyze the KRAS status in PC pts who presented with localized disease at diagnosis (Dx). Methods: We identified PC pts at our institution who underwent CGP utilizing the Foundation One CDx assay and had localized disease at initial Dx; these pts were categorized into resectable/borderline resectable PC (LPC) and locally advanced PC (LAPC). All pts with LPC and LAPC underwent neoadjuvant chemotherapy and chemoradiation prior to possible surgery (all intended therapy - AIT). Tissue from metastatic sites was used for CGP in pts who developed recurrent/metastatic disease before or after completion of AIT. The primary tumor was used for CGP in pts who completed AIT without subsequent relapse or in the absence of adequate metastatic tissue. Effect of each gene on response and survival outcomes was estimated using proportional odds and Cox regression analysis, respectively, adjusting for stage. Results: 75 pts were identified, median age at Dx was 65 years, 59% were male, 65% had a primary tumor in the pancreatic head. 38 (86%) pts with LPC completed AIT compared to 21 (68%) pts with LAPC (p<0.001). KRAS mutation was detected in 95% (71/75) of pts– 94% (67/71) in codon 12 and 6% (4/71) in codon 61. The various KRAS mutations and their association with completion of AIT is summarized in the table. The likelihood of completing AIT did not differ based on KRAS wildtype (WT) vs mutated status (p =1.00), the mutated codon (codon 12 vs. codon 61; p =1.00) or the individual KRAS point mutations (p = 0.7); however, all patients with G12A (N= 1), G12C (N=1), G12L (N=1) and G12R (N=11) mutations completed AIT. KRAS status (mutated vs. WT) and the individual KRAS mutations were not associated with overall survival (OS) after adjusting for stage (p= 0.13 and p = 0.26 respectively). Median OS for patients with LPC and LAPC, was 39 months (mos) and 29 mos respectively. Conclusions: KRAS status and individual KRAS mutations did not have an impact on completing AIT or mOS; however, these findings need to be interpreted with caution due to the inherent biases involved in such analyses. The clinical significance and functional relevance of KRAS G12A, G12C, G12L and G12R mutations, though relatively rare, needs further characterization as well as mechanistic elucidation. [Table: see text]

Objectives Concussions and mild traumatic brain injuries are important medical issues, particularly among youth as the long-term health consequences of these injuries can become increasingly problematic. The purpose of this study was to examine recent trends in diagnosed concussions among pediatric patients in a large health care system. Methods This was a retrospective, population-based epidemiology study design that queried all patient files (pediatrics included) using electronic medical health records and further stratified patients based on type of concussion, age, sex, and year from 2013 to 2018. Results Electronic health records from a cohort of 8 832 419 (n males = 4 246 492; n females = 4 585 931) patient visits were assessed for concussion diagnosis and filtered for those whose concussive event led to a loss of consciousness (LOC) or not (nLOC). Of these patients, 12 068 were diagnosed with a concussion (LOC = 3 699; nLOC = 8 369) with an overall incidence rate of 1.37 concussions per 1000 patients. Overall, the number of patients diagnosed with a concussion increased by 5063 (LOC = 1351; nLOC = 3712) from 2013 to 2018. Males and females presented with similar rates of concussions 5919 (49.05%) and 6149 concussions (50.95%), respectively. Of total diagnosed concussions, 4972 (LOC = 815; nLOC = 4157) were under the age of 18 and represented 41.2% of all diagnosed concussions with an incidence rate of 6.79 per 1000 patients. Conclusion The number of concussions diagnosed appear to be on the rise with the largest number of concussions being diagnosed in those under the age of 18. Future studies should seek to determine primary causality and the long-term health implications of concussions with or without LOC.

(1) Background: Limited information exists on the prevalence of low energy availability (LEA) in collegiate team sports. The purpose of this study was to examine the prevalence of LEA in collegiate women soccer players. (2) Methods: Collegiate women soccer athletes (n = 18, height: 1.67 ± 0.05 m; body mass: 65.3 ± 7.9 kg; body fat %: 24.9 ± 5.6%) had their body composition and sport nutrition knowledge assessed in the pre-season. Energy availability was assessed mid-season using a 4-day dietary log and activity energy expenditure values from a team-based monitoring system. A validated screening tool was used to screen for LEA. (3) Results: The screening tool classified 56.3% of athletes as at risk of LEA (<30 kcal/kg of FFM); however, the actual dietary intake identified 67% as LEA. Athletes identified as non-LEA consumed significantly more absolute (p = 0.040) and relative (p = 0.004) energy than LEA athletes. (4) Conclusions: There was a high prevalence of LEA among collegiate women soccer athletes. Although previously validated in women endurance athletes, the LEA screening tool was not effective in identifying those at risk of LEA in this sample of athletes.

Abstract Objective Determine if information regarding hypertrophic cardiomyopathy (HCM) can be accurately retrieved from cardiac magnetic resonance (CMR) reports using natural language processing (NLP). Background CMR imaging is used for diagnosis and risk stratification of HCM. Manual annotation of information from CMR is time-consuming. NLP is an artificial intelligence method for automating extraction of information from narrative text. Methods We identified 200 HCM patients who had CMR reports from 1998 to 2018. These patients were randomly allocated into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports). An NLP system with 2 tiers was developed; the first extracted information regarding HCM diagnosis while second extracted categorical or numeric concepts for HCM classification. NLP performance was compared with gold-standard manual annotation. Results NLP algorithms achieved very high performance across all concepts with mean positive predictive value (PPV) = 0.96. An outlier was the performance for abstracting the presence of an apical pouch from CMR reports, which had noticeably lower PPV= 0.78 which be attributed to the low number of cases with this finding. Conclusions The algorithms developed can be translated to clinical decision support systems to increase efficiency and contribute to improved quality of care. Funding Acknowledgement Type of funding source: Other. Main funding source(s): Study supported by the National Heart, Lung and Blood Institute of National Institutes of Health (K01HL124045), the Mayo Clinic Center for Clinical and Translational Science (CCaTS), and the Mayo Clinic K2R award. Content is solely the responsibility of authors and does not necessarily represent official views of the National Institutes of Health.

Background: The objective of this study was to report the incidence of in-hospital serum ionized calcium derangement and its impact on mortality. Methods: We included 12,599 non-dialytic adult patients hospitalized at a tertiary medical center from January 2009 to December 2013 with normal serum ionized calcium at admission and at least 2 in-hospital serum ionized calcium values. Using serum ionized calcium of 4.60–5.40 mg/dL as the normal reference range, in-hospital serum ionized calcium levels were categorized based on the presence of hypocalcemia and hypercalcemia in hospital. We performed logistic regression to assess the relationship of in-hospital serum ionized calcium derangement with mortality. Results: Fifty-four percent of patients developed new serum ionized calcium derangements: 42% had in-hospital hypocalcemia only, 4% had in-hospital hypercalcemia only, and 8% had both in-hospital hypocalcemia and hypercalcemia. In-hospital hypocalcemia only (OR 1.28; 95% CI 1.01–1.64), in-hospital hypercalcemia only (OR 1.64; 95% CI 1.02–2.68), and both in-hospital hypocalcemia and hypercalcemia (OR 1.73; 95% CI 1.14–2.62) were all significantly associated with increased in-hospital mortality, compared with persistently normal serum ionized calcium levels. Conclusions: In-hospital serum ionized calcium derangements affect more than half of hospitalized patients and are associated with increased in-hospital mortality.