AIMC Topic: Stromal Cells

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Automated tumor stroma ratio assessment in colorectal cancer using hybrid deep learning approach.

Scientific reports
The Tumor-Stroma Ratio (TSR) is a critical prognostic factor in colorectal cancer (CRC), offering insights into tumor microenvironment interactions. However, traditional TSR assessment methods are subjective and labor-intensive. This study is among t...

Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.

Scientific reports
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a minority of patients even survives advanced disease for months or years. Thus, there is a clinical ...

Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

Journal of biomedical optics
SIGNIFICANCE: Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessita...

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study.

Laboratory investigation; a journal of technical methods and pathology
Tumor-stroma ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathologic relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parame...

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma.

The American journal of pathology
Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has not been used in routine clinical practice because of reproducibility issues. The current study develo...

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.

The American journal of pathology
Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the ...

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types.

EBioMedicine
BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma frac...