AIMC Topic: Spinal Neoplasms

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A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 29, 2025
BACKGROUND AND PURPOSE: The most common adverse event following spine stereotactic body radiotherapy (SBRT) is vertebral compression fracture (VCF). There is interest in the development of patient-specific tools that can predict those at high risk of...
Machine Learning Retrospective Studies Female Risk Factors Aged Aged, 80 and over Male Middle Aged Humans Spinal Neoplasms Radiosurgery Spinal Fractures Fractures, Compression
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Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images.

Medical physics Apr 21, 2025
BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.
Palliative Care Humans Radiotherapy, Conformal Deep Learning Spinal Neoplasms Time Factors Cone-Beam Computed Tomography Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted
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External validation of a machine learning prediction model for massive blood loss during surgery for spinal metastases: a multi-institutional study using 880 patients.

The spine journal : official journal of the North American Spine Society Mar 27, 2025
BACKGROUND CONTEXT: A machine learning (ML) model was recently developed to predict massive intraoperative blood loss (>2,500 mL) during posterior decompressive surgery for spinal metastasis that performed well on external validation within the same ...
Aged Male Middle Aged Blood Loss, Surgical Spinal Neoplasms Decompression, Surgical Humans Machine Learning Retrospective Studies Female
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Predicting quality of life of patients after treatment for spinal metastatic disease: development and internal evaluation.

The spine journal : official journal of the North American Spine Society Mar 26, 2025
BACKGROUND CONTEXT: When treating spinal metastases in a palliative setting, maintaining or enhancing quality of life (QoL) is the primary therapeutic objective. Clinicians tailor their treatment strategy by weighing the QoL benefits against expected...
Cohort Studies Netherlands Spinal Neoplasms Prospective Studies Adult Humans Male Female Machine Learning Middle Aged Aged Quality of Life
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Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.

Cancer imaging : the official publication of the International Cancer Imaging Society Mar 13, 2025
BACKGROUND: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning.
Deep Learning Magnetic Resonance Imaging Spinal Neoplasms Aged Pelvic Neoplasms Humans Sacrum Adult Retrospective Studies Male Female Middle Aged
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Direct-to-Treatment Adaptive Radiation Therapy: Live Planning of Spine Metastases Using Novel Cone Beam Computed Tomography.

International journal of radiation oncology, biology, physics Mar 9, 2025
PURPOSE: Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out using a synthetic CT (sCT) created through deformable registration between the patient-specific fan-beam CT, fan-beam computed tomography (FBCT), and...
Spinal Neoplasms Radiotherapy Dosage Phantoms, Imaging Radiotherapy, Image-Guided Humans Cone-Beam Computed Tomography Organs at Risk Radiotherapy Planning, Computer-Assisted
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Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.

Academic radiology Feb 28, 2025
RATIONALE AND OBJECTIVES: To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three a...
Middle Aged Contrast Media Magnetic Resonance Imaging Models, Statistical Aged Adult Sensitivity and Specificity Male Signal-To-Noise Ratio Diffusion Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Artifacts Spinal Neoplasms Image Enhancement Humans Algorithms Computer Simulation Female Reproducibility of Results
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External validation of 12 existing survival prediction models for patients with spinal metastases.

The spine journal : official journal of the North American Spine Society Jan 31, 2025
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making.
Humans Prospective Studies Spinal Neoplasms Aged Adult Male Prognosis Middle Aged Retrospective Studies Female
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Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

Journal of magnetic resonance imaging : JMRI Jan 27, 2025
BACKGROUND: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
Magnetic Resonance Imaging Spine Image Interpretation, Computer-Assisted Spinal Neoplasms Humans Male Artificial Intelligence Middle Aged Female Retrospective Studies Deep Learning Reproducibility of Results ROC Curve Aged Adult
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Comparative diagnostic accuracy of ChatGPT-4 and machine learning in differentiating spinal tuberculosis and spinal tumors.

The spine journal : official journal of the North American Spine Society Jan 11, 2025
BACKGROUND: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. The application of AI-driven large language models (LLMs) shows great potential for improving the accu...
Adult Sensitivity and Specificity Tuberculosis, Spinal Retrospective Studies Humans Generative Artificial Intelligence Male Female Middle Aged Magnetic Resonance Imaging Machine Learning Aged Diagnosis, Differential Algorithms Young Adult Spinal Neoplasms
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