AIMC Topic: Bone Neoplasms

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Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

Cancer medicine
OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC).

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

BioMed research international
OBJECTIVES: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the c...

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm.

Scientific reports
Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accur...

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

Scientific reports
SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis syste...

Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study.

European journal of radiology
PURPOSE: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesio...

Sarcoma classification by DNA methylation profiling.

Nature communications
Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sa...

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Scientific reports
Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpretin...

Effectiveness of Radiofrequency Ablation in the Treatment of Painful Osseous Metastases: A Correlation Meta-Analysis with Machine Learning Cluster Identification.

Journal of vascular and interventional radiology : JVIR
A systematic review and meta-analysis of pain response after radiofrequency (RF) ablation over time for osseous metastases was conducted in 2019. Analysis used a random-effects model with GOSH plots and meta-regression. Fourteen studies comprising 42...

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

Journal of cancer research and clinical oncology
PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient ...