AI Article Synopsis
- The study investigates the effectiveness of Deep Learning Radiomics Nomograms (DLRN) for predicting IDH genotype in glioma patients.
- A total of 402 patients were divided into training and validation groups to develop a model that combines deep learning, radiomics, and clinical data for accurate classification.
- The DLRN showed high performance with an AUC of 0.98, indicating its potential to improve patient management and targeted therapy based on IDH mutation status.
Article Abstract
Purpose: To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.
Methods: A total of 402 glioma patients from two independent centers were retrospectively included, and the data from center I was randomly divided into a training cohort (n = 239) and an internal validation cohort (n = 103) on a 7:3 basis. Center II served as an independent external validation cohort (n = 60). We developed a DLRN for IDH classification of gliomas based on T2 images. This hybrid model integrates deep learning features, radiomics features, and clinical features most relevant to IDH genotypes and finally classifies them using multivariate logistic regression analysis. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the performance of the model and applied the DLRN score to the survival analysis of some of the follow-up glioma patients.
Results: The proposed model had an area under the curve (AUC) of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients.
Conclusions: Deep learning radiomics nomograms performed well in non-invasively predicting IDH mutation status in gliomas, assisting stratified management and targeted therapy for glioma patients.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.mri.2024.110314 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue.
Quant Plant Biol
December 2024
Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany.
Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana.
View Article and Find Full Text PDFAssessment of human emotional reactions to visual stimuli "deep-dreamed" by artificial neural networks.
Front Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
Decorrelative network architecture for robust electrocardiogram classification.
Patterns (N Y)
December 2024
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
To achieve adequate trust in patient-critical medical tasks, artificial intelligence must be able to recognize instances where they cannot operate confidently. Ensemble methods are deployed to estimate uncertainty, but models in an ensemble often share the same vulnerabilities to adversarial attacks. We propose an ensemble approach based on feature decorrelation and Fourier partitioning for teaching networks diverse features, reducing the chance of perturbation-based fooling.
View Article and Find Full Text PDFDeep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT.
Infect Drug Resist
January 2025
Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People's Republic of China.
Background: Early differentiation between spinal tuberculosis (STB) and acute osteoporotic vertebral compression fracture (OVCF) is crucial for determining the appropriate clinical management and treatment pathway, thereby significantly impacting patient outcomes.
Objective: To evaluate the efficacy of deep learning (DL) models using reconstructed sagittal CT images in the differentiation of early STB from acute OVCF, with the aim of enhancing diagnostic precision, reducing reliance on MRI and biopsies, and minimizing the risks of misdiagnosis.
Methods: Data were collected from 373 patients, with 302 patients recruited from a university-affiliated hospital serving as the training and internal validation sets, and an additional 71 patients from another university-affiliated hospital serving as the external validation set.
Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence.
Clin Med Insights Oncol
January 2025
Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada.
Despite the expanding therapeutic options available to cancer patients, therapeutic resistance, disease recurrence, and metastasis persist as hallmark challenges in the treatment of cancer. The rise to prominence of generative artificial intelligence (GenAI) in many realms of human activities is compelling the consideration of its capabilities as a potential lever to advance the development of effective cancer treatments. This article presents a hypothetical case study on the application of generative pre-trained transformers (GPTs) to the treatment of metastatic prostate cancer (mPC).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!