Predicting variability in pediatric cochlear implant outcomes through synchronous brain activation patterns: Insights from fNIRS.

Objectives: This study examined the relationship between cortical activation and auditory performance in pediatric cochlear implant (CI) users compared to normal-hearing (NH) controls using functional near-infrared spectroscopy (fNIRS). The aim was to identify neural predictors of CI outcome and to investigate post-implantation cortical plasticity.

Design: Eighteen pediatric CI users and 17 NH controls performed simultaneously non-speech discrimination and sentence recognition tasks while undergoing fNIRS recording.

Enhanced Malignancy Prediction of Small Lung Nodules in Different Populations Using Transfer Learning on Low-Dose Computed Tomography.

Predicting malignancy in small lung nodules (SLNs) across diverse populations is challenging due to significant demographic and clinical variations. This study investigates whether transfer learning (TL) can improve malignancy prediction for SLNs using low-dose computed tomography across datasets from different countries. We collected two datasets: an Asian dataset (669 SLNs from Cathay General Hospital, CGH, Taiwan) and an American dataset (600 SLNs from the National Lung Screening Trial, NLST, America).

Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging.

Mild traumatic brain injury (mTBI), known as concussion, accounts for more than 85% of brain injuries globally. Specifically, uncomplicated mTBI showing negative findings in routine clinical imaging in the acute phase hinders early and appropriate care in these patients. It has been acknowledged that different impact parameters may affect and even accelerate the progress of subsequent neuropsychological symptoms following mTBI.

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions.

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

Background: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.

Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats.

Background: This study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q).

Methods: Eighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI.

Territory-Related Functional Connectivity Changes Associated with Verbal Memory Decline in Patients with Unilateral Asymptomatic Internal Carotid Stenosis.

Background And Purpose: Verbal memory decline is a common complaint of patients with severe asymptomatic stenosis of the internal carotid artery (aICS). Previous publications explored the associations between verbal memory decline and altered functional connectivity (FC) after aICS. Patients with severe aICS may show reduced perfusion in the ipsilateral territory and redistribution of cerebral blood flow to compensate for the deficient regions, including expansion of the posterior and contralateral ICA territories via the circle of Willis.

Prefrontal activity and heart rate variability during cognitive tasks may show different changes in young and older adults with and without mild cognitive impairment.

Background: Age-related decline in cognitive function is often linked to changed prefrontal cortex (PFC) activity and heart rate variability (HRV). Mild cognitive impairment (MCI), a transitional stage between normal aging and dementia, might have further degeneration beyond aging. This study aimed to investigate the differences between young and older adults with or without MCI in cognitive functions, task-induced PFC activation and HRV changes.

Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

Background: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep learning-based workflow to provide reliable automatic segmentation of the atria, pericardium, and EAT for future applications in the management of AF.

Methods: This study enrolled 157 patients with AF who underwent first-time catheter ablation between January 2015 and December 2017 at Taipei Veterans General Hospital.

Feasibility of Auto-Quantified Epicardial Adipose Tissue in Predicting Atrial Fibrillation Recurrence After Catheter Ablation.

Background: The aim of this study was to build an auto-segmented artificial intelligence model of the atria and epicardial adipose tissue (EAT) on computed tomography (CT) images, and examine the prognostic significance of auto-quantified left atrium (LA) and EAT volumes for AF.

Methods and results: This retrospective study included 334 patients with AF who were referred for catheter ablation (CA) between 2015 and 2017. Atria and EAT volumes were auto-quantified using a pre-trained 3-dimensional (3D) U-Net model from pre-ablation CT images.

Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.

Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard.

Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors.

Background And Objective: Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors.

Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients.

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced.

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

Background: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional studies when conventional centralized learning (CL) is used.

Purpose: To explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns.

Magnetic resonance radiomics-derived sphericity correlates with seizure in brain arteriovenous malformations.

Objectives: Angioarchitectural analysis of brain arteriovenous malformations (BAVMs) is qualitative and subject to interpretation. This study quantified the morphology of and signal changes in the nidal and perinidal areas by using MR radiomics and compared the performance of MR radiomics and angioarchitectural analysis in detecting epileptic BAVMs.

Materials And Methods: From 2010 to 2020, a total of 111 patients with supratentorial BAVMs were retrospectively included and grouped in accordance with the initial presentation of seizure.

Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis.

In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies.

Right arcuate fasciculus as outcome predictor after low-frequency repetitive transcranial magnetic stimulation in nonfluent aphasic stroke.

Background And Purpose: A reliable neuroimaging biomarker to predict language improvement after neuromodulation in post-stroke aphasia is lacking. It is hypothesized that aphasic patients with stroke injuries in the left primary language circuits but with sufficient right arcuate fasciculus (AF) integrity might respond to low-frequency repetitive transcranial magnetic stimulation (LF-rTMS), leading to language improvement. This study aimed to assess the microstructural indices of the right AF before LF-rTMS treatment and further correlate with language improvement after the treatment.

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status.

The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate patient management and outcome improvement. However, the disease progression is influenced by multiple factors, such as patient characteristics and treatment strategies, and hence satisfactory performance of OS prediction remains challenging. Accordingly, we proposed a deep learning approach based on comprehensive predictors, including clinical, imaging, and genetic information, to accomplish reliable and personalized OS prediction in patients with BMs after receiving GKRS.

Effects of bihemispheric transcranial direct current stimulation on motor recovery in subacute stroke patients: a double-blind, randomized sham-controlled trial.

Background: Bihemispheric transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) can simultaneously modulate bilateral corticospinal excitability and interhemispheric interaction. However, how tDCS affects subacute stroke recovery remains unclear. We investigated the effects of bihemispheric tDCS on motor recovery in subacute stroke patients.

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

Background: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies.

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.

Background: Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery.

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images.

Background And Objective: GKRS is an effective treatment for smaller intracranial tumors with a high control rate and low risk of complications. Target delineation in medical MR images is essential in the planning of GKRS and follow-up. A deep learning-based algorithm can effectively segment the targets from medical images and has been widely explored.

Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography.

Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images.