AIMC Topic: Karnofsky Performance Status

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Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

PloS one Nov 11, 2024
BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality an...
Middle Aged Deep Learning Magnetic Resonance Imaging Brain Neoplasms Male Postoperative Period Isocitrate Dehydrogenase Karnofsky Performance Status Multimodal Imaging Glioblastoma Prognosis Retrospective Studies Aged Adult Female Humans
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The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.

Cancer medicine Aug 28, 2021
This retrospective study has been conducted to validate the performance of deep learning-based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the ...
Glioblastoma Bayes Theorem Sex Factors Age Factors Predictive Value of Tests DNA Repair Enzymes Tumor Suppressor Proteins Proportional Hazards Models Survival Analysis DNA Modification Methylases Adult Brain Neoplasms Middle Aged Deep Learning Male Female Isocitrate Dehydrogenase Aged Humans Karnofsky Performance Status Chemoradiotherapy Retrospective Studies Radiotherapy
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A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters.

Cancer medicine Mar 24, 2021
PURPOSE: Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high-grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians...
Prognosis Female Brain Neoplasms Magnetic Resonance Imaging Male Area Under Curve Treatment Outcome Aged Proportional Hazards Models Middle Aged Humans Glioma Karnofsky Performance Status Combined Modality Therapy Models, Theoretical Machine Learning Support Vector Machine
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Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model.

International journal of radiation oncology, biology, physics May 22, 2020
PURPOSE: To determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival (BMETS) to predict survival using 27 prognostic ...
Life Expectancy Karnofsky Performance Status Bone Neoplasms Female Area Under Curve Spinal Neoplasms Reproducibility of Results Time Factors Analgesics, Opioid Palliative Care Algorithms Middle Aged Male Pelvic Bones Humans Blood Cell Count Prognosis Steroids Machine Learning Proportional Hazards Models Kaplan-Meier Estimate
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