Related Experiment Video
Updated: May 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Integrating neuron-specific enolase and venous-phase enhanced computed tomography radiomics for improved survival
Haoru Wang1, Wenhong Ye2, Ting Li3
1Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China.
Background:
High-risk neuroblastoma is associated with a poor prognosis, making it crucial to identify patients within this group who face an even higher risk of adverse outcomes.
Objective:
To determine if integrating clinical indicators and venous-phase enhanced computed tomography radiomics features could improve the prediction of overall survival in high-risk neuroblastoma.
Materials And Methods:
We retrospectively included high-risk neuroblastoma patients treated at a primary institution, randomly stratifying them into a training set (70%) and a test set (30%). Univariate and multivariate Cox regression analyses were used to identify independent clinical risk factors. We then extracted radiomics features from venous-phase enhanced computed tomography images. Clinical risk factors, radiomics score, and combined model were evaluated in the training, test, and external validation sets.
Results:
The training, test, and validation sets included 70, 30, and 40 patients, respectively. Neuron-specific enolase was identified as the independent clinical risk factor, with concordance indices of 0.616, 0.627, and 0.595 in the training, test, and validation sets, respectively. The radiomics score achieved concordance indices of 0.699, 0.690, and 0.684 in the training, test, and validation sets, respectively. The combined model showed concordance indices of 0.730, 0.707, and 0.690 in the training, test, and validation sets, respectively. The combined model predicted 5-year overall survival with an area under the receiver operating characteristic curve of 0.780 in the training set, 0.742 in the test set, and 0.710 in the validation set.
Conclusion:
Combining neuron-specific enolase and venous-phase enhanced computed tomography radiomics improves survival prediction in high-risk neuroblastoma.
More Related Videos
09:53Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
Published on: August 16, 2020
12:23Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound
Published on: August 14, 2012