AI Medical Compendium Journal:
Cellular oncology (Dordrecht, Netherlands)

Showing 1 to 4 of 4 articles

Exploring tumor microenvironment interactions and apoptosis pathways in NSCLC through spatial transcriptomics and machine learning.

Cellular oncology (Dordrecht, Netherlands) Dec 19, 2024
BACKGROUND: The most common type of lung cancer is non-small cell lung cancer (NSCLC), accounting for 85% of all cases. Programmed cell death (PCD), an important regulatory mechanism for cell survival and homeostasis, has become increasingly prominen...
Humans Tumor Microenvironment Lung Neoplasms Machine Learning Prognosis Gene Expression Regulation, Neoplastic Transcriptome Cell Line, Tumor Gene Expression Profiling Signal Transduction Cell Proliferation Carcinoma, Non-Small-Cell Lung Apoptosis
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OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data.

Cellular oncology (Dordrecht, Netherlands) May 28, 2024
PURPOSE: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based m...
Mice, Inbred C57BL Cell Survival Antibodies, Monoclonal, Humanized Carcinoma, Pancreatic Ductal Coculture Techniques Kinetics Pancreatic Neoplasms Deep Learning Mice Time Factors Leukocytes, Mononuclear Antineoplastic Agents Animals Humans Caproates
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Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Cellular oncology (Dordrecht, Netherlands) Apr 15, 2019
PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we a...
Prognosis Middle Aged Female Humans Algorithms Multivariate Analysis Proportional Hazards Models Triple Negative Breast Neoplasms Mitosis Disease-Free Survival Deep Learning
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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.

Cellular oncology (Dordrecht, Netherlands) Mar 1, 2019
PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis....
Deep Learning Pathology, Clinical Neoplasm Staging Aged Diagnosis, Computer-Assisted Middle Aged Prognosis Multivariate Analysis Stromal Cells Female Kaplan-Meier Estimate Male Humans Rectal Neoplasms
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