AIMC Topic: Osteosarcoma
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Microfluidics-based label-free SERS profiling of exosomes with machine learning for osteosarcoma diagnosis.
Talanta
Nov 1, 2025
Osteosarcoma (OS) calls for early diagnosis to significantly improve patient survival rates. Exosomes hold significant potential as noninvasive biomarkers for the early diagnosis of cancer. Here, we design a microfluidic device to purify and analyze ...
Exosomal Gene Biomarkers in Osteosarcoma: Mifepristone as a Targeted Therapeutic Revealed by Multi-Omics Analysis.
FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Jul 15, 2025
Osteosarcoma (OS) is an aggressive bone cancer that mainly occurs in children and adolescents. OS patients are mainly treated with neoadjuvant chemotherapy and surgical resection. This treatment is effective for early osteosarcoma. However, the effec...
Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis.
Clinical imaging
Jul 1, 2025
BACKGROUND AND AIMS: Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-...
Multimodal Diagnostic Approach for Osteosarcoma and Bone Callus Using Hyperspectral Imaging and Deep Learning.
Journal of biophotonics
Jun 1, 2025
Distinguishing osteosarcoma from bone callus remains a clinical challenge due to their morphological similarities. This study proposes J-CAN, a multimodal deep learning framework integrating hyperspectral imaging (HSI) and H&E-stained pathology for r...
Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.
Scientific reports
May 19, 2025
Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment...
Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy.
Clinical and experimental medicine
May 9, 2025
While neoadjuvant chemotherapy combined with surgical resection has improved the prognosis for patients with osteosarcoma, its impact on metastatic and recurrent cases remains limited. Immunotherapy is emerging as a promising alternative. However, th...
Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
May 1, 2025
OBJECTIVE: The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to p...
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...
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 (...
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...
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