The search for reliable prognostic markers in oral squamous cell carcinoma (OSCC) remains a critical need. Tumor-infiltrating lymphocytes (TILs), particularly T lymphocytes, play a pivotal role in the immune response against tumors and are strongly correlated with favorable prognoses. Computational pathology has proven highly effective for histopathological image analysis, automating tasks such as cell detection, classification, and segmentation. In the present study, we developed a StarDist-based model to automatically detect T lymphocytes in hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) of OSCC, bypassing the need for traditional immunohistochemistry (IHC). Using QuPath, we generated training datasets from annotated slides, employing IHC as the ground truth. Our model was validated on Cancer Genome Atlas-derived OSCC images, and survival analyses demonstrated that higher TIL densities correlated with improved patient outcomes. This work introduces an efficient, AI-powered workflow for automated immune profiling in OSCC, offering a reproducible and scalable approach for diagnostic and prognostic applications.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.32074/1591-951X-1069 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Nextflow4MS-DIAL: A Reproducible Nextflow-Based Workflow for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.
J Am Soc Mass Spectrom
January 2025
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida 32611, United States.
Reproducibility in untargeted metabolomics data processing remains a significant challenge due to software limitations and the complex series of steps required. To address these issues, we developed Nextflow4MS-DIAL, a reproducible workflow for liquid chromatography-mass spectrometry (LC-MS) metabolomics data processing, validated with publicly available data from MetaboLights (MTBLS733). Nextflow4MS-DIAL automates LC-MS data processing to minimize human errors from manual data handling.
View Article and Find Full Text PDF3D CT to 2D X-ray image registration for improved visualization of tibial vessels in endovascular procedures.
Int J Comput Assist Radiol Surg
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Purpose: During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.
Methods: X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment.
Auto-RapTAC: A Versatile and Sustainable Platform for the Automated Rapid Synthesis and Evaluation of PROTAC.
J Med Chem
January 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
The tedious synthesis and limited throughput biological evaluation remain a great challenge for discovering new proteolysis targeting chimera (PROTAC). To rapidly identify potential PROTAC lead compounds, we report a platform named Auto-RapTAC. Based on the modular characteristic of the PROTAC molecule, a streamlined workflow that integrates lab automation with "click chemistry" joint building-block libraries was constructed.
View Article and Find Full Text PDFDeep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision.
Surg Endosc
January 2025
Surgery Department, Meander Medical Centre, Maatweg, Amersfoort, 3818 TZ, Utrecht, The Netherlands.
Background: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are not included in the pre-operative difficulty assessment. In this work, we propose an automated workflow for pelvimetry based on pre-operative magnetic resonance imaging (MRI) volumes.
View Article and Find Full Text PDFAn Automated Workflow to Discover the Structure-Stability Relations for Radiation Hard Molecular Semiconductors.
J Am Chem Soc
January 2025
Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 7, 91058 Erlangen, Germany.
Emerging photovoltaics for outer space applications are one of the many examples where radiation hard molecular semiconductors are essential. However, due to a lack of general design principles, their resilience against extra-terrestrial high-energy radiation can currently not be predicted. In this work, the discovery of radiation hard materials is accelerated by combining the strengths of high-throughput, lab automation and machine learning.
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!