AI Article Synopsis
- Cervical cancer is the fourth most common cancer in women globally, and this study introduces a deep learning framework to predict molecular subtypes in HPV-positive cervical squamous cell carcinoma using histology slides.
- The research analyzed three cohorts with 545 patients, demonstrating that the Digital-CMS scores can effectively predict both disease-specific and disease-free survival outcomes.
- Furthermore, the study found significant differences in the tumor microenvironment between subtypes, highlighting potential biomarkers and therapeutic targets that could guide clinical applications for better patient management.
Article Abstract
Cervical cancer remains the fourth most common cancer among women worldwide. This study proposes an end-to-end deep learning framework to predict consensus molecular subtypes (CMS) in HPV-positive cervical squamous cell carcinoma (CSCC) from H&E-stained histology slides. Analysing three CSCC cohorts (n = 545), we show our Digital-CMS scores significantly stratify patients by both disease-specific (TCGA p = 0.0022, Oslo p = 0.0495) and disease-free (TCGA p = 0.0495, Oslo p = 0.0282) survival. In addition, our extensive tumour microenvironment analysis reveals differences between the two CMS subtypes, with CMS-C1 tumours exhibit increased lymphocyte presence, while CMS-C2 tumours show high nuclear pleomorphism, elevated neutrophil-to-lymphocyte ratio, and higher malignancy, correlating with poor prognosis. This study introduces a potentially clinically advantageous Digital-CMS score derived from digitised WSIs of routine H&E-stained tissue sections, offers new insights into TME differences impacting patient prognosis and potential therapeutic targets, and identifies histological patterns serving as potential surrogate markers of the CMS subtypes for clinical application.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1038/s41698-024-00778-5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A real-time approach for surgical activity recognition and prediction based on transformer models in robot-assisted surgery.
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.
Bioinformatics
January 2025
Biocomputing Group, University of Bologna, Italy.
Motivation: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.
Results: In this work we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations.
Development of a model for measuring sagittal plane parameters in 10-18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology.
J Orthop Surg Res
January 2025
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
View Article and Find Full Text PDFUniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.
BMC Bioinformatics
January 2025
College of Artificial Intelligence, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu, China.
Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models.
View Article and Find Full Text PDFImproving 3D deep learning segmentation with biophysically motivated cell synthesis.
Commun Biol
January 2025
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
Biomedical research increasingly relies on three-dimensional (3D) cell culture models and artificial-intelligence-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets.
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!