AIMC Topic: Fluorouracil

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A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

Journal of cancer research and clinical oncology Mar 24, 2022
PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has be...
Humans Artificial Intelligence Deep Learning Antineoplastic Combined Chemotherapy Protocols Neoplasm Staging Fluorouracil Chemotherapy, Adjuvant Prospective Studies Prognosis Retrospective Studies Colorectal Neoplasms Biomarkers, Tumor
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Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine.

Computational and mathematical methods in medicine Feb 12, 2022
Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is difficult to treat because of its unobvious onset, low resection rate, and rapid deterioration. There...
Humans Algorithms Stomach Neoplasms Fluorouracil Computational Biology Xenograft Model Antitumor Assays Animals Medicine, Chinese Traditional Spleen Deep Learning Mice, Nude Phytotherapy Cell Line, Tumor Antineoplastic Agents, Phytogenic Mice Drugs, Chinese Herbal
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Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Nature communications Feb 10, 2022
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-base...
Fluorouracil Chemotherapy, Adjuvant CD8 Antigens B7-H1 Antigen Gene Expression Regulation, Neoplastic RNA, Long Noncoding Neoplasm Recurrence, Local Machine Learning Risk Factors Humans Colorectal Neoplasms Biomarkers, Tumor Gene Expression Profiling
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Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.

Cardiovascular toxicology Nov 18, 2021
Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare machine learning algorithms to predict cardiotoxicity risk among nationally representative CRC patients...
Male Middle Aged Risk Assessment Time Factors Treatment Outcome Female Risk Factors Aged Colorectal Neoplasms Antimetabolites, Antineoplastic Cardiotoxicity Predictive Value of Tests Fluorouracil SEER Program Heart Diseases Capecitabine Humans Machine Learning Decision Support Techniques
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Evaluation of external contamination on the vial surfaces of some hazardous drugs that commonly used in Chinese hospitals and comparison between environmental contamination generated during robotic compounding by IV: Dispensing robot vs. manual compounding in biological safety cabinet.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners Jun 24, 2021
OBJECTIVES: The aims of the study were to evaluate the external contamination of hazardous drug vials used in Chinese hospitals and to compare environmental contamination generated by a robotic intelligent dispensing system (WEINAS) and a manual comp...
Robotics Robotic Surgical Procedures Fluorouracil Tandem Mass Spectrometry Drug Compounding Cyclophosphamide Equipment Contamination Humans Antineoplastic Agents Hospitals China Occupational Exposure Environmental Monitoring
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Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Nature communications Oct 30, 2020
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...
Fluorouracil Urinary Bladder Organoids Biomarkers, Tumor Antineoplastic Agents Gene Regulatory Networks Humans Transcriptome Gene Expression Regulation, Neoplastic Machine Learning Cell Line, Tumor Drug Development Colorectal Neoplasms Protein Interaction Maps Urinary Bladder Neoplasms Cisplatin
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Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BMC bioinformatics Sep 30, 2020
BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression da...
Humans Machine Learning Neoplasms Antineoplastic Agents ROC Curve Gene Expression Regulation, Neoplastic Deoxycytidine Databases, Genetic Gemcitabine Cluster Analysis Area Under Curve Fluorouracil
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An Application of Machine Learning in Pharmacovigilance: Estimating Likely Patient Genotype From Phenotypical Manifestations of Fluoropyrimidine Toxicity.

Clinical pharmacology and therapeutics Feb 27, 2020
Dihydropyrimidine dehydrogenase (DPD)-deficient patients might only become aware of their genotype after exposure to dihydropyrimidines, if testing is performed. Case reports to pharmacovigilance databases might only contain phenotypical manifestatio...
Fluorouracil Pharmacovigilance Antimetabolites, Antineoplastic Dihydropyrimidine Dehydrogenase Deficiency Humans Machine Learning Phenotype Databases, Factual Genotype
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FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms.

Cancer medicine Jan 1, 2020
Early identification of metastatic or recurrent colorectal cancer (CRC) patients who will be sensitive to FOLFOX (5-FU, leucovorin and oxaliplatin) therapy is very important. We performed microarray meta-analysis to identify differentially expressed ...
Oligonucleotide Array Sequence Analysis Intracellular Signaling Peptides and Proteins Response Evaluation Criteria in Solid Tumors Leucovorin Humans Machine Learning Prognosis Colorectal Neoplasms Biomarkers, Tumor Fluorouracil Gene Expression Profiling Protein Kinases Gene Expression Regulation, Neoplastic Datasets as Topic Antineoplastic Combined Chemotherapy Protocols Organoplatinum Compounds Neoplasm Recurrence, Local Cell Cycle Proteins
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Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.

Cancer medicine Dec 20, 2019
BACKGROUND: To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.
Induction Chemotherapy China Chemoradiotherapy Image Processing, Computer-Assisted Nasopharyngeal Neoplasms Adult Antineoplastic Combined Chemotherapy Protocols Endemic Diseases Male Neoplasm Staging Nasopharynx Humans Middle Aged Cisplatin Risk Assessment Prognosis Fluorouracil Deep Learning Retrospective Studies Progression-Free Survival Female Radiotherapy, Intensity-Modulated Kaplan-Meier Estimate
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