AIMC Topic: Bone Neoplasms

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Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a Model-Averaged Neural Network.

Bone
Macrometastases in bone are preceded by bone marrow invasion of disseminated tumor cells. This study combined functional imaging parameters from FDG-PET/CT and MRI in a rat model of breast cancer bone metastases to a Model-averaged Neural Network (av...

F-FDG PET/CT-Guided Real-Time Automated Robotic Arm-Assisted Needle Navigation for Percutaneous Biopsy of Hypermetabolic Bone Lesions: Diagnostic Performance and Clinical Impact.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this study is to establish the feasibility, safety, diagnostic performance, and clinical impact of real-time intraprocedural F-FDG PET/CT-guided automated robotic arm-assisted biopsy of hypermetabolic marrow or bone lesions.

Robotic Patterning a Superhydrophobic Surface for Collective Cell Migration Screening.

Tissue engineering. Part C, Methods
Collective cell migration, in which cells migrate as a group, is fundamental in many biological and pathological processes. There is increasing interest in studying the collective cell migration in high throughput. Cell scratching, insertion blocker,...

Assessing microscope image focus quality with deep learning.

BMC bioinformatics
BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with hig...

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

Contrast media & molecular imaging
The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of doze...

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

Journal of computational biology : a journal of computational molecular cell biology
Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a ...

A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy.

International journal of molecular medicine
In this study, gene expression profiles of osteosarcoma (OS) were analyzed to identify critical genes associated with metastasis. Five gene expression datasets were screened and downloaded from Gene Expression Omnibus (GEO). Following assessment by M...

Expert System for Bone Scan Interpretation Improves Progression Assessment in Bone Metastatic Prostate Cancer.

Advances in therapy
INTRODUCTION: The bone scan index (BSI) was introduced as a quantitative tool for tumor involvement in bone of patients with metastatic prostate cancer (mPCa). The computer-aided diagnosis device for BSI analysis EXINIbone seems to represent technica...

Patterns of Recurrence After Postprostatectomy Fossa Radiation Therapy Identified by C-11 Choline Positron Emission Tomography/Computed Tomography.

International journal of radiation oncology, biology, physics
PURPOSE: To evaluate C-11 choline positron emission tomography/computed tomography (CholPET) in staging and determining patterns of recurrence in prostate cancer patients with rising prostate-specific antigen levels after prostatectomy radiation ther...

Diagnostic performance of bone scintigraphy analyzed by three artificial neural network systems.

Annals of nuclear medicine
OBJECTIVE: The accuracy of bone scintigraphy analyzed by computer-assisted diagnosis (CAD) software involving multiple artificial neural network (ANN) systems has not been well established.