AIMC Topic: Bone Neoplasms
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Machine learning-based selection of immune cell markers in osteosarcoma: prognostic determination and validation of CLK1 in disease progression.
Frontiers in immunology
Dec 17, 2024
INTRODUCTION: Osteosarcoma (OS) is a malignancy of the bone that mainly afflicts younger individuals. Despite existing treatment approaches, patients with metastatic or recurrent disease generally face poor prognoses. A greater understanding of the t...
Male
Osteosarcoma
DNA Copy Number Variations
Bone Neoplasms
Disease Progression
Protein Serine-Threonine Kinases
Protein-Tyrosine Kinases
Female
Cell Line, Tumor
Humans
Gene Expression Regulation, Neoplastic
Tumor Microenvironment
Machine Learning
Cell Proliferation
Biomarkers, Tumor
Prognosis
Lymphocytes, Tumor-Infiltrating
Automated Bone Cancer Detection Using Deep Learning on X-Ray Images.
Surgical innovation
Dec 16, 2024
In recent days, bone cancer is a life-threatening health issue that can lead to death. However, physicians use CT-scan, X-rays, or MRI images to recognize bone cancer, but still require techniques to increase precision and reduce human labor. These m...
BMSMM-Net: A Bone Metastasis Segmentation Framework Based on Mamba and Multiperspective Extraction.
Academic radiology
Nov 30, 2024
RATIONALE AND OBJECTIVES: Metastatic bone tumors significantly reduce patients' quality of life and expedite cancer spread. Traditional diagnostic methods rely on time-consuming manual annotations by radiologists, which are prone to subjectivity. Emp...
Automated detection of bone lesions using CT and MRI: a systematic review.
La Radiologia medica
Nov 6, 2024
PURPOSE: The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications.
Assessment of Artificial Intelligence Chatbot Responses to Common Patient Questions on Bone Sarcoma.
Journal of surgical oncology
Oct 29, 2024
BACKGROUND AND OBJECTIVES: The potential impacts of artificial intelligence (AI) chatbots on care for patients with bone sarcoma is poorly understood. Elucidating potential risks and benefits would allow surgeons to define appropriate roles for these...
Optimized deep learning networks for accurate identification of cancer cells in bone marrow.
Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for disting...
as a Novel Biomarker for Colon Cancer Bone Metastasis with Machine Learning and Immunohistochemistry Validation.
Cancer biotherapy & radiopharmaceuticals
Oct 18, 2024
Bone metastasis (BM) is a serious clinical symptom of advanced colorectal cancer. However, there is a lack of effective biomarkers for early diagnosis and treatment. RNA-seq data from public databases (GSE49355, GSE101607) were collected and normal...
Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?
European journal of radiology
Sep 17, 2024
BACKGROUND AND PURPOSE: Computed tomography (CT) and biopsy may be insufficient for preoperative evaluation of the grade and outcome of patients with chondrosarcoma. The aim of this study was to develop and validate a CT-based deep learning radiomics...
Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial.
Journal of surgical oncology
Sep 11, 2024
BACKGROUND: Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoper...
Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy.
BMC medical imaging
Sep 9, 2024
BACKGROUND: To evaluate the clinical performance of two deep learning methods, one utilizing real clinical pairs and the other utilizing simulated datasets, in enhancing image quality for two-dimensional (2D) fast whole-body scintigraphy (WBS).
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