AIMC Topic: Osteosarcoma

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Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings.

Clinical cancer research : an official journal of the American Association for Cancer Research Jan 17, 2025
PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predic...
Deep Learning Convolutional Neural Networks Adolescent Adult Male Bone Neoplasms Kaplan-Meier Estimate Necrosis Humans Neoadjuvant Therapy Osteosarcoma Neural Networks, Computer Female Young Adult Prognosis
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Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.

Journal of cellular and molecular medicine Jan 1, 2025
Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However, identifying hub genes regulating mitophagy and developing mitophagy-based treatments to combat drug resistance remains challenging. Herein, BayeDEM (...
Humans Neoplasms Deep Learning Apoptosis Osteosarcoma Genes, Essential Cell Line, Tumor Gene Expression Regulation, Neoplastic Mitochondria Drug Resistance, Neoplasm Mitophagy Bayes Theorem Cisplatin
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Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Briefings in bioinformatics Nov 22, 2024
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchi...
Unsupervised Machine Learning Bayes Theorem Gene Expression Profiling Transcriptome Osteosarcoma Bone Neoplasms Cluster Analysis Humans Prognosis
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Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Medicine Sep 27, 2024
Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predict...
Proportional Hazards Models Neoplasm Staging SEER Program Adolescent Humans Adult Child Prognosis Osteosarcoma Female Male ROC Curve Machine Learning Bone Neoplasms Clinical Decision-Making Young Adult Middle Aged
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Combined bioinformatics and machine learning methodologies reveal prognosis-related ceRNA network and propose ABCA8, CAT, and CXCL12 as independent protective factors against osteosarcoma.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University Aug 1, 2024
BACKGROUND: Aberrant circular RNA (circRNA) acts as an oncogene or suppressor during neoplasm initiation and development. However, the functions of most circRNAs in osteosarcoma (OS) remain unclear.
RNA, Competitive Endogenous Prognosis ATP-Binding Cassette Transporters Biomarkers, Tumor RNA, Circular Gene Expression Regulation, Neoplastic Computational Biology Protective Factors Gene Regulatory Networks Osteosarcoma RNA, Messenger MicroRNAs Humans Bone Neoplasms Chemokine CXCL12 Machine Learning
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Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Osteosarcoma is the most common type of bone cancer. The primary means of osteosarcoma diagnosis is through evaluating plain x-rays. Using image analysis techniques, features that clinicians use to diagnose osteosarcoma can be quantified and studied ...
Support Vector Machine Sensitivity and Specificity Metabolomics Humans ROC Curve Radiography Algorithms Image Processing, Computer-Assisted X-Rays Osteosarcoma
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