Middle Meningeal artery Embolization For Chronic Subdural Hematomas With Concurrent antithrombotics

BACKGROUND: Chronic subdural hematoma (CSDH) is an increasingly prevalent disease in the aging population. Patients with CSDH frequently suffer from concurrent vascular disease or develop secondary thrombotic complications requiring antithrombotic treatment. OBJECTIVE: to determine the safety and impact of early reinitiation of antithrombotics after middle meningeal artery embolization for chronic subdural hematoma. METHODS: This is a single-institution, retrospective study of patients who underwent middle meningeal artery (MMA) embolizations for CSDH. Patient with or without antithrombotic initiation within 5 days postembolization were compared. Primary outcome was the rate of recurrence within 60 days. Secondary outcomes included rate of reoperation, reduction in CSDH thickness, and midline shift. RESULTS: Fifty-seven patients met inclusion criteria. The median age was 66 years (IQR 58-76) with 21.1% females. Sixty-six embolizations were performed. The median length to follow-up was 20 days (IQR 14-44). Nineteen patients (33.3%) had rapid reinitiation of antithrombotics (5 antiplatelet, 11 anticoagulation, and 3 both). Baseline characteristics between the no antithrombotic (no-AT) and the AT groups were similar. The recurrence rate was higher in the AT group (no-AT vs AT, 9.3 vs 30.4%, P = .03). Mean absolute reduction in CSDH thickness and midline shift was similar between groups. Rate of reoperation did not differ (4.7 vs 8.7%, P = .61). CONCLUSION: Rapid reinitiation of AT after MMA embolization for CSDH leads to higher rates of recurrence with similar rates of reoperation. Care must be taken when initiating antithrombotics after treatment of CSDH with MMA embolization

Middle Meningeal artery Embolization of Septated Chronic Subdural Hematomas

INTRODUCTION: Middle meningeal artery embolization (MMAE) has emerged as a promising new treatment for patients with chronic subdural hematomas (cSDH). Its efficacy, however, upon the subtype with a high rate of recurrence-septated cSDH-remains undetermined. METHODS: From our prospective registry of patients with cSDH treated with MMAE, we classified patients based on the presence or absence of septations. The primary outcome was the rate of recurrence of cSDH. Secondary outcomes included a reduction in cSDH thickness, midline shift, and rate of reoperation. RESULTS: Among 80 patients with 99 cSDHs, the median age was 68 years (IQR 59-77) with 20% females. Twenty-eight cSDHs (35%) had septations identified on imaging. Surgical evacuation with burr holes was performed in 45% and craniotomy in 18.8%. Baseline characteristics between no-septations (no-SEP) and septations (SEP) groups were similar except for median age (SEP vs no-SEP, 72.5 vs. 65.5, p CONCLUSION: MMAE appears to be equal to potentially more effective in preventing the recurrence of cSDH in septated lesions. These findings may aid in patient selection

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology

Abstract 1122‐000174: Stroke Risk of Carotid Artery Stenting Using Balloon‐Guide Catheter Versus Distal Embolic Protection Devices

Introduction: Prevention of distal embolization during carotid artery stenting (CAS) is a key element of procedural technique and is standardly performed using distal protection devices (DPDs). Data in support of DPDs, however, are limited. Here, we present our experience of proximal occlusion using a balloon guide catheter (BGC) during CAS as the primary method of distal embolic protection. Methods: We conducted a retrospective review of patients undergoing CAS at our healthcare system between January of 2018 to March of 2021. Procedures were categorized by embolic protection strategy: DPD or BGC (with or without DPD). Emergent cases were defined as patients receiving CAS within <24 hours of presenting with an ischemic stroke or TIA ipsilateral to the carotid disease side. Severe stenosis was defined as 70–99% per NASCET criteria. The primary outcome was rate of procedural ischemic stroke between the DPD and BGC groups, and was defined as acute focal neurological deficit lasting for ≥ 24 hours following CAS related to an embolic event during the procedure. Results: A total of 126 CAS procedures were performed during the study period. 91 cases were performed under proximal BGC protection (of which 24 also included DPD usage) and 35 CAS cases via DPD as a primary mean for embolic protection. The median age for the cohort was 68 [IQR 62‐76], 37% females, 31% (n = 39) cases were treated emergently, and elective cases were 69% (n = 87). Baseline characteristics were similar in both groups except for hyperlipidemia (BGC vs DPD, 71.4% vs 42.9%; p = 0.003) and history of smoking (BGC vs DPD, 56% vs 34.4%; p = 0.029). Severe carotid stenosis was present in 80.2% BGC group and 77.1% in DPD (p = 0.573). Post‐stenting balloon angioplasty was more frequent in the BGC group as compared with DPD (54% vs. 26%, BGC vs. DPD, p = 0.005). Procedural embolic stroke rates were low in both groups, and not significantly different (1.1% vs. 2.9%, BGC vs. DPD, p = 0.48). Conclusions: CAS with BGC as the primary means of distal embolic protection showed comparable, low rates of procedural embolic ischemic events compared to those with DPD. These findings suggest BGC embolic strategies may be a viable alternative to DPD usage

Abstract Number ‐ 143: Middle Meningeal Artery Embolization of Septated Chronic Subdural Hematomas

Introduction Chronic Subdural Hematoma (cSDH) is projected to be the most common neurosurgical disease in the US by the end of the decade. MMA embolization is a promising new treatment; however, its efficacy in patients with complex, septated cSDH remains uncertain. Methods From our prospectively maintained registry of patients with cSDH treated with MMA embolization (with or without concurrent surgical drainage), we identified patients with and without septations. Septations were defined as hyperdense septa between the inner and outer membranes on a lower‐density background. The primary outcome was recurrence of cSDH, which was defined as any radiographic evidence of increase in thickness and/or new acute hemorrhage. Secondary outcomes included reduction in cSDH thickness, midline shift and rate of reoperation. Results Among 84 patients with 100 cSDHs, median age was 70 [IQR 59‐77] with 26.2% females. 35 CSDHs (35%) had membranes identified on imaging. Evacuation with burr holes was performed in 45.2% and craniotomy in 16.7% of the total cohort. Baseline characteristics between the patients with no septations (no SEP) and those with septatations (SEP) were similar except for median age (no SEP vs SEP, 66 vs 74, p = 0.006). Recurrence rate was lower in the SEP group (no SEP vs SEP, 21.5 vs 2.9%, p = 0.017) even when adjusting for clinically relevant factors (OR 0.07, p = 0.017). Despite larger baseline thickness in the SEP groups, the mean absolute reduction in thickness was more pronounced (no SEP vs SEP, ‐4.6 vs ‐8.0 mm, p = 0.016) with similar midline shift change. Rate of reoperation did not differ (6.2 vs 2.9%, p = 0.65). Recurrence free survival was significantly improved in patients with septations even after adjustment for age and evacuation strategy (Figure 1, p = 0.04). Conclusions MMAE in traditionally higher risk septated cSDHs appears to be highly effective with an even larger reduction in volume and lower risk of recurrence than non‐septated hematomas. These findings support the mechanistic theory of MMAE as a devascularization procedure of membrane neovasculature and may aid in improved patient selection

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Abstract Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions’ correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

International audienceDeep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions’ correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology