AIMC Topic: Spinal Neoplasms
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Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.
Acta radiologica (Stockholm, Sweden : 1987)
Oct 1, 2024
BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinically significant to concentrate on patients who have malignant tumors and differentiate between benign...
Developmental and Validation of Machine Learning Model for Prediction Complication After Cervical Spine Metastases Surgery.
Clinical spine surgery
Aug 29, 2024
STUDY DESIGN: This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery.
A systematic review of deep learning-based spinal bone lesion detection in medical images.
Acta radiologica (Stockholm, Sweden : 1987)
Jul 21, 2024
Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for e...
Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.
International journal of surgery (London, England)
May 1, 2024
BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to a...
A New Deep Learning Algorithm for Detecting Spinal Metastases on Computed Tomography Images.
Spine
Dec 12, 2023
STUDY DESIGN: Retrospective diagnostic study.
Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Oct 5, 2023
PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).
Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma.
World neurosurgery
Aug 14, 2023
OBJECTIVE: Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on a Cox regression model to predict the survival of patients with spinal chordoma. We aimed to develop a more effectiv...
CORR Insights®: Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm.
Clinical orthopaedics and related research
Jul 25, 2023
Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm.
Clinical orthopaedics and related research
Jun 12, 2023
BACKGROUND: The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA) was developed to predict the survival of patients with spinal metastasis. The algorithm was successfully tested in five international institutions using 1101 patie...
Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods.
World neurosurgery
Apr 13, 2023
OBJECTIVE: To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods.
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