Citations

AbstractPurposeThis paper reports the efficacy of the poly (ADP‐ribose) polymerase inhibitor olaparib alone and in combination with the antiangiogenesis agent cediranib compared with cediranib alone in patients with advanced endometrial cancer.MethodsThis was open‐label, randomized, phase 2 trial (NCT03660826). Eligible patients had recurrent endometrial cancer, received at least one (<3) prior lines of chemotherapy, and were Eastern Cooperative Oncology Group performance status 0 to 2. Patients were randomly assigned (1:1:1), stratified by histology (serous vs. other) to receive cediranib alone (reference arm), olaparib, or olaparib and cediranib for 28‐day cycles until progression or unacceptable toxicity. The primary end point was progression‐free survival in the intention‐to‐treat population. Homologous repair deficiency was explored using the BROCA‐GO sequencing panel.ResultsA total of 120 patients were enrolled and all were included in the intention‐to‐treat analysis. Median age was 66 (range, 41–86) years and 47 (39.2%) had serous histology. Median progression‐free survival for cediranib was 3.8 months compared with 2.0 months for olaparib (hazard ratio, 1.45 [95% CI, 0.91‐2.3] p = .935) and 5.5 months for olaparib/cediranib (hazard ratio, 0.7 [95% CI, 0.43–1.14] p = .064). Four patients receiving the combination had a durable response lasting more than 20 months. The most common grade 3/4 toxicities were hypertension in the cediranib (36%) and olaparib/cediranib (33%) arms, fatigue (20.5% olaparib/cediranib), and diarrhea (17.9% cediranib). The BROCA‐GO panel results were not associated with response.ConclusionThe combination of cediranib and olaparib demonstrated modest clinical efficacy; however, the primary end point of the study was not met. The combination was safe without unexpected toxicity.

Importance Transoral robot-assisted surgery (TORS) continues to have a major role in the treatment of oropharyngeal cancer. As new iterations of robotic technology are increasingly utilized, it is important to share learning experiences and clinical outcomes data, to optimize technical efficiency and clinical care. Observations This was a retrospective review of a large academic institution’s initial clinical use of the da Vinci Single Port (SP) compared with the da Vinci Si (Si) system. A total of 205 TORS cases were reviewed: 109 in the SP group (November 22, 2018, through September 30, 2020), and 96 in the Si group (January 1, 2016, through November 12, 2018). Both groups had comparable operative times, rates of postoperative pharyngeal hemorrhage, length of hospital stay, and duration of nasogastric feeding tube use. There was no difference in pathological characteristics, rates of positive margins, or indications for or time to initiation of adjuvant therapy between the groups. The collective experience of 6 faculty members—who have trained 139 TORS surgeons for the SP system rollout—was compiled to provide a summary of learning experiences and technical notes on safe and efficient operation of the SP system. Conclusions and Relevance This Review found that the functional and oncologic outcomes were comparable between TORS cases performed with the Si and SP systems, and they had similar complication rates. Recognized advantages of the SP over the Si system include the availability of bipolar-energized instruments, a usable third surgical arm, and improved camera image quality.

Background: Enlargement or dilation of the left ventricle (LV) is associated with increased cardiovascular morbidity and mortality, and is an established precursor of ventricular dysfunction and heart failure in almost all major cardiovascular conditions. Although there are existing artificial intelligence frameworks that make use of deep learning to detect LV enlargement, many entail LV segmentation, which can be error-prone especially for point-of-care ultrasound (POCUS) images. Goal: To develop a fully end-to-end deep learning framework that takes as input either transthoracic echocardiographic (TTE) or POCUS 2D image clips, and classifies directly (i.e., without first segmenting the LV) whether a patient has an enlarged LV. Methods: Using a sample of retrospective TTE data enriched for LV enlargement (out of 13,868 patients total, 3,351 had an enlarged LV defined using sex-specific guideline-directed criteria), we trained a classifier that takes as input apical 2-chamber and parasternal long axis videos, and provides as output a binary label denoting whether a patient has an enlarged LV. Then, we evaluated the trained model using a holdout testing dataset of retrospective TTE data (2,087 patients) as well as data from a prospective cohort of 428 patients from whom TTE and POCUS were collected simultaneously. Results: For the retrospective TTE testing dataset, the AUC was 0.851 (95% confidence interval (CI) of 0.830-871). For the prospective cohort, the model performed comparably for both TTE and POCUS video clips (TTE: AUC of 0.865, 95% CI of 0.823-0.906; POCUS: AUC of 0.840, 95% CI of 0.797-0.883). Conclusions: The current deep learning model performed comparably well for both TTE and POCUS echocardiograms. We regard these results as a proof-of-concept of the potential utility of this model in helping point-of-care clinicians make decisions regarding whether patients should receive further evaluation for LV enlargement and associated conditions.

Background: Recently, our team developed an end-to-end artificial intelligence (AI) framework that can successfully estimate left ventricular ejection fraction (LVEF) from echocardiogram videos. However, this framework requires specific views (A2C and PLAX), and cannot estimate LVEF for studies from which these views are missing. Goal: To develop a deep learning model to estimate LVEF from any type and number of echocardiogram video(s). Methods: We built a deep learning model for B-mode echocardiogram videos of the left ventricle (A2C, A3C, A4C, or PLAX views), using retrospective transthoracic echocardiography (TTE) data. The model was evaluated with two test datasets: one with random valid (i.e., showing the left ventricle) views, and one with preselected views (both having 433 patients). Later, the model was tested with a prospective patient cohort that had both TTE and point-of-care ultrasound (POCUS) data collected simultaneously (393 patients). Results: On the retrospective TTE test set, we observed excellent performance (five random valid views per study: RMSE of 6.87% and Pearson correlation (PC) of 0.82 on LVEF estimation, and an AUC of 0.96 (95% CI: of 0.94-0.98) on low LVEF classification (<=40%); preselected views: RMSE of 6.75%, PC of 0.83, and an AUC of 0.96 (95% CI: 0.94-0.98)). For both TTE and POCUS data from the prospective cohort, we also observed strong performance (TTE: RMSE of 5.62%, PC of 0.87, and an AUC of 0.97; POCUS: RMSE of 6.12%, PC of 0.84, and an AUC of 0.96). For the prospective test sets as well as for the retrospective TTE test set with preselected views, results reflect the estimated LVEF average of A2C, A3C, A4C, and PLAX videos from each patient. Conclusions: For both TTE and POCUS data, the current model showed strong performance which was comparable to that of our previously established view-specific model. Alongside this comparable performance, the current model surpasses our previous model in terms of flexibility, as it isn't dependent on view classification and allows a broader selection in terms of the number and types of videos per study or patient. This innovative approach could prove particularly beneficial in point-of-care situations in which it may not be feasible to obtain high-quality, view-specific videos.

Introduction: Transcatheter aortic valve replacement (TAVR) is a well-recognized treatment for severe aortic stenosis. One potentially life-threatening complication of TAVR is infective endocarditis (IE). There is limited contemporary data on the clinical characteristics and outcomes of patients undergoing TAVR who develop in-hospital IE. Aim: Describe the clinical characteristics and outcomes of patients undergoing TAVR who develop in-hospital IE. Methods: The National Inpatient Sample Database was queried from 2016 to 2020 for relevant ICD-10 procedural and diagnostic codes. Baseline characteristics and in-hospital outcomes of adult (≥18 years) patients undergoing TAVR who developed in-hospital IE were compared to that of patients who did not develop IE. Multivariate logistic-regression analyses adjusting for age, race, gender, and statistically significant comorbidities between cohort's were performed. A p-value <0.05 was considered significant. Results: A total of 314,250 hospitalizations for patients undergoing TAVR were identified. Of them, 1065 (0.3%) developed in-hospital IE. Overall, mean age of IE patients was 70.3 ± 15.0. Patients were predominantly (62%) males and white (79.5%) ( Table 1) . IE patients had a higher risk of in-hospital all-cause mortality (adjusted odds ratio [95% CI]: 2.07 [1.50-2.86]), septic shock (3.73 [2.71-5.13]), acute respiratory failure (2.59 [2.19-3.06]), ischemic stroke (4.4 [3.48-5.51]), acute kidney injury (3.05 [2.61-3.57]), cardiac arrest (2.82 [1.83-4.37] and cardiogenic shock 3.70 [2.98-4.57]). All p values were <0.01. Conclusion: Patients undergoing TAVR who develop in-hospital IE had higher in-hospital complications when compared to those of patients who did not develop IE. Although the rate of in-hospital IE was low, the number of patients identified in the NIS database was sizable and the syndrome deserves investigation due to its associated complications.

Mucin-1 (MUC1) is a highly relevant antigen for cancer vaccination due to its overexpression and hypo-glycosylation in a high percentage of carcinomas. To enhance the immune response to MUC1, our group has developed C3-liposomes that encapsulate the MUC1 antigen along with immunostimulatory compounds for direct delivery to antigen-presenting cells (APCs). C3-liposomes bind complement C3, which interacts with C3-receptors on APCs, resulting in liposomal uptake and the delivery of tumor antigens to APCs in a manner that mimics pathogenic uptake. In this study, MUC1 and Toll-like receptor (TLR) agonists were encapsulated in C3-liposomes to provoke an immune response in transgenic mice tolerant to MUC1. The immune response to the C3-bound MUC1 liposomal vaccine was assessed by ELISA, ELISpot, and flow cytometry. Co-administering TLR 7/8 agonists with MUC1 encapsulated in C3-liposomes resulted in a significant antibody response compared to non-encapsulated MUC1. This antibody response was significantly higher in females than in males. The co-encapsulation of three TLR agonists with MUC1 in C3-liposomes significantly increased antibody responses and eliminated sex-based differences. Furthermore, this immunization strategy resulted in a significantly increased T cell-response compared to other treatment groups. In conclusion, the co-delivery of MUC1 and TLR agonists via C3-liposomes greatly enhances the immune response to MUC1, highlighting its potential for antigen-specific cancer immunotherapy.

Abstract Despite major strides in cancer research and therapy, these advances have not been equitable across race and ethnicity. Historically marginalized groups (HMG) are more likely to have inadequate preventive screening, increased delays in diagnosis, and poor representation in clinical trials. Notably, Black, Hispanic, and Indigenous people represent 30% of the population but only 9% of oncology clinical trial participants. As a result, HMGs lack equitable access to novel therapies, contradicting the principle of distributive justice, as enshrined in the Belmont report, which demands the equitable selection of subjects in research involving human subjects. The lack of clinical trial diversity also leads to low generalizability and potentially harmful medical practices. Specifically, patients with brain cancer face unique barriers to clinical trial enrollment and completion due to disease-specific neurologic and treatment-induced conditions. Collectively, the intersection of these disease-specific conditions with social determinants of health fosters a lack of diversity in clinical trials. To ameliorate this disparity in neuro-oncology clinical trial participation, we present interventions focused on improving engagement of HMGs. Proposals range from inclusive trial design, decreasing barriers to care, expanding trial eligibility, access to tumor profiling for personalized medical trials, setting reasonable metrics and goals for accrual, working with patient community stakeholders, diversifying the neuro-oncology workforce, and development of tools to overcome biases with options to incentivize equity. The diversification of participation amongst neuro-oncology clinical trials is imperative. Equitable access and inclusion of HMG patients with brain tumors will not only enhance research discoveries but will also improve patient care.