AIMC Topic: Spinal Neoplasms

Clear Filters Showing 41 to 45 of 45 articles

Optimizing workflows for metastatic central nervous system disease: a systematic review and proposed guidelines.

Journal of neuro-oncology
INTRODUCTION: Brain and spine metastases are a major cause of morbidity and mortality in patients with malignancy. Aside from advancements in medical, surgical, and radiation therapies, patient workflow optimization augments care. Here, we present th...

Zero-shot segmentation of spinal vertebrae with metastatic lesions: an analysis of Meta's Segment Anything Model 2 and factors affecting learning free segmentation.

Neurosurgical focus
OBJECTIVE: Accurate vertebral segmentation is an important step in imaging analysis pipelines for diagnosis and subsequent treatment of spinal metastases. Segmenting these metastases is especially challenging given their radiological heterogeneity. C...

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

The Lancet. Digital health
BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic lev...

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation.

Neurosurgery
BACKGROUND: Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality.

Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.

Neurosurgery
BACKGROUND: Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care.