Citations

Despite the high incidence and burden of stroke, biological biomarkers are not used routinely in clinical practice to diagnose, determine progression, or prognosticate outcomes of acute ischemic stroke (AIS). Because of its direct interface with neural tissue, cerebrospinal fluid (CSF) is a potentially valuable source for biomarker development. This systematic review was conducted using three databases. All trials investigating clinical and preclinical models for CSF biomarkers for AIS diagnosis, prognostication, and severity grading were included, yielding 22 human trials and five animal studies for analysis. In total, 21 biomarkers and other multiomic proteomic markers were identified. S100B, inflammatory markers (including tumor necrosis factor-alpha and interleukin 6), and free fatty acids were the most frequently studied biomarkers. The review showed that CSF is an effective medium for biomarker acquisition for AIS. Although CSF is not routinely clinically obtained, a potential benefit of CSF studies is identifying valuable biomarkers from the pathophysiologic microenvironment that ultimately inform optimization of targeted low-abundance assays from peripheral biofluid samples (e.g., plasma). Several important catabolic and anabolic markers can serve as effective measures of diagnosis, etiology identification, prognostication, and severity grading. Trials with large cohorts studying the efficacy of biomarkers in altering clinical management are still needed.

Objective: The purpose of this study was to describe management and outcomes from a contemporary cohort of children with Wilms tumor complicated by inferior vena caval thrombus. Background: The largest series of these patients was published almost 2 decades ago. Since then, neoadjuvant chemotherapy has been commonly used to manage these patients, and outcomes have not been reported. Methods: Retrospective review of 19 North American centers between 2009 and 2019. Patient and disease characteristics, management, and outcomes were investigated and analyzed. Results: Of 124 patients, 81% had favorable histology (FH), and 52% were stage IV. IVC thrombus level was infrahepatic in 53 (43%), intrahepatic in 32 (26%), suprahepatic in 14 (11%), and cardiac in 24 (19%). Neoadjuvant chemotherapy using a 3-drug regimen was administered in 82% and postresection radiation in 90%. Thrombus level regression was 45% overall, with suprahepatic level showing the best response (62%). Cardiopulmonary bypass (CPB) was potentially avoided in 67%. The perioperative complication rate was significantly lower after neoadjuvant chemotherapy [(25%) vs upfront surgery (55%); P=0.005]. CPB was not associated with higher complications [CPB (50%) vs no CPB (27%); P=0.08]. Two-year event-free survival was 93% and overall survival was 96%, higher in FH cases (FH 98% vs unfavorable histology/anaplastic 82%; P=0.73). Neither incomplete resection nor viable thrombus cells affected event-free survival or overall survival. Conclusions: Multimodal therapy resulted in excellent outcomes, even with advanced-stage disease and cardiac extension. Neoadjuvant chemotherapy decreased the need for CPB to facilitate resection. Complete thrombectomy may not always be necessary.

Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is to identify the underlying factors that affect the adoption of artificial intelligence in healthcare (AIH) by healthcare providers and to understand “What are the factors that influence healthcare providers’ behavioral intentions to adopt AIH in their routine practice?” An integrated survey was conducted among healthcare providers, including consultants, residents/students, and nurses. The survey included items related to performance expectancy, effort expectancy, initial trust, personal innovativeness, task complexity, and technology characteristics. The collected data were analyzed using structural equation modeling. A total of 392 healthcare professionals participated in the survey, with 72.4% being male and 50.7% being 30 years old or younger. The results showed that performance expectancy, effort expectancy, and initial trust have a positive influence on the behavioral intentions of healthcare providers to use AIH. Personal innovativeness was found to have a positive influence on effort expectancy, while task complexity and technology characteristics have a positive influence on effort expectancy for AIH. The study’s empirically validated model sheds light on healthcare providers’ intention to adopt AIH, while the study’s findings can be used to develop strategies to encourage this adoption. However, further investigation is necessary to understand the individual factors affecting the adoption of AIH by healthcare providers.

Abstract In this multi-institutional retrospective study, we examined the characteristics and outcomes of 160 patients with high-grade B-cell lymphoma, not otherwise specified (HGBL-NOS)—a rare category defined by high-grade morphologic features and lack of MYC rearrangements with BCL2 and/or BCL6 rearrangements ("double hit"). Our results show that HGBL-NOS tumors are heterogeneous: 83% of patients had a germinal center B-cell immunophenotype, 37% a dual-expressor immunophenotype (MYC and BCL2 expression), 28% MYC rearrangement, 13% BCL2 rearrangement, and 11% BCL6 rearrangement. Most patients presented with stage IV disease, a high serum lactate dehydrogenase, and other high-risk clinical factors. Most frequent first-line regimens included dose-adjusted cyclophosphamide, doxorubicin, vincristine, and etoposide, with rituximab and prednisone (DA-EPOCH-R; 43%); rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP; 33%); or other intensive chemotherapy programs. We found no significant differences in the rates of complete response (CR), progression-free survival (PFS), or overall survival (OS) between these chemotherapy regimens. CR was attained by 69% of patients. PFS at 2 years was 55.2% and OS was 68.1%. In a multivariable model, the main prognostic factors for PFS and OS were poor performance status, lactate dehydrogenase >3 × upper limit of normal, and a dual-expressor immunophenotype. Age >60 years or presence of MYC rearrangement were not prognostic, but patients with TP53 alterations had a dismal PFS. Presence of MYC rearrangement was not predictive of better PFS in patients treated with DA-EPOCH-R vs R-CHOP. Improvements in the diagnostic criteria and therapeutic approaches beyond dose-intense chemotherapy are needed to overcome the unfavorable prognosis of patients with HGBL-NOS.

TPS657 Background: Poorly differentiated neuroendocrine carcinoma (NEC) is an aggressive malignancy comprising both pulmonary and extrapulmonary primary sites. NEC includes both small cell lung cancer (SCLC) and large cell neuroendocrine carcinoma (LCNEC), as well as other neuroendocrine carcinomas arising from any primary organ. A substantial portion (~40%) of NEC arises from gastrointestinal primary sites. The optimal systemic therapy beyond first line platinum and etoposide is not established. There is a critical need to improve upon the median survival in the second line, as most patients do not survive more than 6 months. The efficacy of single agent immune checkpoint inhibitors (ICIs) in NEC has been disappointing. One possible explanation for this is that the tumor microenvironment in NEC is non-inflamed. VSV-IFNβ-NIS is a vesicular stomatitis virus (VSV)-based oncolytic virus being tested in multiple early phase clinical trials. Preliminary studies of immune responses in patients receiving VSV-IFNβ-NIS therapy suggest some patients develop T cell responses to viral antigens and known tumor antigens. We hypothesize that VSV-IFNβ-NIS therapy may convert a non-inflamed or immune-excluded phenotype in NEC to a highly inflamed phenotype that sensitizes the tumor to ICIs. Methods: This is a phase 1-2 safety run-in study designed to determine the safety of VSV-IFNβ-NIS in combination with a single agent ICI, pembrolizumab, followed by dose expansion in patients with refractory non-small cell lung cancer (NSCLC) or NEC. The safety run-in portion of this study has been completed, and we are presently testing the recommended phase 2 dose (RP2D) of VSV-IFNβ-NIS in an expansion cohort of patients with SCLC or NEC of any primary site. Patients must have previously progressed on at least one line of systemic therapy. Prior treatment with checkpoint inhibitors is permitted. Patients are treated one time with the RP2D of 1.0x10^11 TCID50 VSV-IFNβ-NIS on day 1, followed by pembrolizumab on day 8 and then pembrolizumab every 21 days until progression, up to 2 years. The primary objective is to estimate the response rate by RECIST 1.1. Secondary objectives include estimation of disease-control rate, duration or response, progression-free survival, overall survival, and safety signals. The NEC expansion cohort will seek to enroll 10 patients. If at least one objective response is observed, and safety is confirmed, the regimen will be considered for future study. Clinical trial information: NCT03647163 .

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.