Citations

Abstract Background: Medium vessel occlusion (MeVO) strokes, particularly affecting the M2 segment of the middle cerebral artery, represent a critical proportion of acute ischemic strokes, posing significant challenges in management and outcome prediction. The efficacy of mechanical thrombectomy (MT) in MeVO stroke may warrant reliable predictors of functional outcomes. This study aimed to investigate the prognostic value of follow-up infarct volume (FIV) for predicting 90-day functional outcomes in MeVO stroke patients undergoing MT. Methods: This multicenter, retrospective cohort study analyzed data from the Multicenter Analysis of primary Distal medium vessel occlusions: effect of Mechanical Thrombectomy (MAD-MT) registry, covering patients with acute ischemic stroke due to M2 segment occlusion treated with MT. We examined the relationship between 90-day functional outcomes, measured by the modified Rankin Scale (mRS), and follow-up infarct volume (FIV), assessed through CT or MRI within 12–36 h post-MT. Results: Among 130 participants, specific FIV thresholds were identified with high specificity and sensitivity for predicting outcomes. A FIV ⩽5 ml was highly specific for predicting favorable and excellent outcomes. The optimal cut-off for both prognostications was identified at ⩽15 ml by the Youden Index, with significant reductions in the likelihood of favorable outcomes observed above a 40 ml threshold. Receiver Operator Curve (ROC) analyses confirmed FIV as a superior predictor of functional outcomes compared to traditional recanalization scores, such as final modified thrombolysis in cerebral infarction score (mTICI). Multivariable analysis further highlighted the inverse relationship between FIV and positive functional outcomes. Conclusions: FIV within 36 h post-MT serves as a potent predictor of 90-day functional outcomes in patients with M2 segment MeVO strokes. Establishing FIV thresholds may aid in the prognostication of stroke outcomes, suggesting a role for FIV in guiding post intervention treatment decisions and informing clinical practice. Future research should focus on validating these findings across diverse patient populations and exploring the integration of FIV measurements with other clinical and imaging markers to enhance outcome prediction accuracy.

Melanoma is the deadliest form of skin cancer. The median age at diagnosis is 66. While most patients are treated with immunotherapy, the use of targeted therapy is a valid alternative for patients whose tumors harbor a BRAF or c-KIT driver mutation. These agents, while effective, come with a variety of side effects which limit their use, especially in older patients. We sought to assess the efficacy and toxicity of these agents in older melanoma patients. Melanoma patients over 65 treated with BRAF/MEK or c-KIT inhibitors were retrospectively identified, and their data were analyzed for treatment efficacy and toxicity. All data were compared using the Chi-square test for categorical comparisons and the Kruskal–Wallis method for median comparisons. One hundred and sixteen patients were identified. One hundred and six patients were treated with BRAF/MEK inhibitors. The assessed response rate (RR) was 83% and was comparable across different subgroups, including advanced line patients and those with a more aggressive disease. The median progression free survival (PFS) was 7.9 months, and the median overall survival (OS) was 15.7 months. Twenty-seven percent experienced grade 3–4 toxicity leading to a 24% treatment discontinuation rate. Another 10 patients were treated with the c-KIT inhibitor imatinib, for whom the assessed RR was 55%. The median PFS was 4.3 months, and the median OS was 22.6 months. Forty percent needed dose reductions, yet none had to stop treatment due to adverse effects. The use of targeted therapy in older patients is effective yet challenging due to toxicity. Deploying mitigation strategies can help maximizing their usefulness.

AbstractBackgroundThis study investigates the impact of margin status after colorectal liver metastasis (CLM) resection on outcomes of patients after neoadjuvant treatment versus those who underwent upfront resection.MethodsAn international collaborative database of CLM patients who underwent surgical resection was used. Proportional hazard regression models were created for single and multivariable models to assess the relationship between independent measures and median overall survival (mOS).ResultsR1 was associated with worse OS in the neoadjuvant group (mOS: 51.8 m for R0 vs. 26.0 m for R1; HR: 2.18). In the patients who underwent upfront surgery, R1 was not associated with OS. (mOS: 46.7 m for R0 vs. 42.6 m for R1). When patients with R1 in each group were stratified by adjuvant treatment, there was no significant difference in the neoadjuvant group, while in the upfront surgery group with R1, adjuvant treatment was associated with significant improvement in OS (mOS: 42.6 m for adjuvant vs. 25.0 m for no adjuvant treatment; HR: 0.21).ConclusionR1 is associated with worse outcomes in the patients who receive neoadjuvant treatment with no significant improvement with the addition of adjuvant therapy, likely representing an aggressive tumor biology. R1 did not impact OS in patients with upfront surgery who received postoperative chemotherapy.

Abstract Background Inflammation and insufficient physical inactivity contribute to individual-level risk of disease recurrence and death in stage III colon cancer. The extent to which increased inflammatory risk can be offset by sufficient physical activity remains unknown. Methods This cohort study was nested within the Cancer and Leukemia Group B (now part of the Alliance for Clinical Trials in Oncology) and Southwest Oncology Group randomized trial. Inflammatory burden was quantified by high-sensitivity C-reactive protein, interleukin-6, and soluble tumor necrosis factor-α receptor 2 after recovery from tumor resection. Physical activity was measured during and after postoperative chemotherapy. The primary endpoint was disease-free survival. Results The 3-year disease-free survival rate was 88.4% among patients with low inflammation and sufficient physical activity (referent group for all comparisons), 84.9% with low inflammation and insufficient physical activity (absolute risk difference = −3.5 percentage points, 95% confidence interval [CI] = −11.3 to 4.3; P = .38), 78.0% with intermediate inflammation and insufficient physical activity (absolute risk difference = −10.4 percentage points, 95% CI = −17.4 to −3.3; P = .007), and 79.7% with high inflammation and insufficient physical activity (absolute risk difference = −8.7 percentage points, 95% CI = −15.7 to −1.6; P = .022). In contrast, the 3-year disease-free survival rate was 87.3% among patients with intermediate inflammation and sufficient physical activity (absolute risk difference = −1.1 percentage points, 95% CI = −7.5 to 5.3; P = .74) and 84.4% with high inflammation and sufficient physical activity (absolute risk difference = −4.0 percentage points, 95% CI = −12.3 to 4.3; P = .34). Conclusion In this observational study of stage III colon cancer patients, physical activity was associated with improved disease-free survival despite high inflammation. Patients with intermediate or high inflammation who were physically active had disease-free survival rates that were not statistically significantly different from those with low inflammation.

Pediatric genetic and metabolic liver diseases comprise a broad spectrum of conditions and represent the second most common indication for liver transplantation following biliary atresia. The decision to transplant can be challenging and requires consideration of several factors, including hepatic involvement, extrahepatic manifestations, and anticipated posttransplant outcomes. This review examines pediatric genetic and metabolic liver diseases, their pathophysiology, clinical presentation, and the role of liver transplantation.

Abstract We conducted this systematic review and meta-analysis to clarify the trend of precocious puberty (PP) incidence after the COVID-19 outbreak and explore potential contributing factors, such as age at presentation and body mass index (BMI) SD score (SDS). Children visiting pediatric endocrinology clinics for the first time for suspected PP were included. We searched databases until February 28, 2023, for studies reporting various indicators of PP incidence before and during the pandemic. Total numbers of events and observations were recorded. A meta-analysis was performed to compare the odds of PP, BMI SDS, and age at presentation between the 2 periods. The dose-response relationships between time points (by number of years away from the pandemic) and PP risk were explored. In summary, a total of 32 studies including 24 200 participants were recruited. The COVID-19 pandemic was associated with the increasing odds of PP among children referred for a suspicious condition (odds ratio = 1.96; 95% CI, 1.56-2.47; I2 = 54%; P < .001). Sensitivity analysis confirmed the robustness of the findings. The BMI SDS did not vary between the 2 periods, whereas age at presentation was lower after the pandemic. Precocious puberty incidence increased more rapidly during the pandemic period than during the prepandemic period. Trial registration: International Prospective Register of Systematic Reviews (PROSPERO; identifier: CRD42023402212)

AbstractBackgroundAdaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real‐time or online ART workflow. In response to this challenge, approaches to auto‐segmentation involving deformable image registration, atlas‐based segmentation, and deep learning‐based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model.PurposeTo address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto‐segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)‐IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation.MethodsThe PHL‐IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL‐IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients.ResultsImplementing the PHL‐IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.810.05 with the general model, 0.83 for the continual model, 0.83 for the conventional IDOL model to an average of 0.87 with the PHL‐IDOL model. Similarly, the Hausdorff distance decreased from 3.06 with the general model, 2.84 for the continual model, 2.79 for the conventional IDOL model and 2.36 for the PHL‐IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL‐IDOL model.ConclusionThe PHL‐IDOL framework applied to the auto‐segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient‐specific prior information in a task central to online ART workflows.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a highperformance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.