Abstract #1545

Predictive Power of Combined Inflammatory Markers and MRI Features for Glioma Prognosis Using Machine Learning

Yaohua Liu¹, Yuting Wang¹, Junle Zhu², Jun Qian², Shuang Qin¹, Yi Hong¹, Saifei Sun¹, Feng Chen², Qin Zhang³, Qian Caixia Fu⁴, Peijun Wang¹, and Qi Lv¹

¹Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China, ²Department of Neurosurgery, Tongji Hospital, Tongji University School of Medicine, Xincun road No. 389, Shanghai, China, ³Department of Clinical Research Center, Tongji Hospital, Tongji University School of Medicine, Xincun road No. 389, Shanghai, China, ⁴MR Application development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China

Synopsis

Keywords: Diagnosis/Prediction, Brain, glioma

Motivation: Exploring the differential ability of MR combined with inflammatory markers for glioma grading.

Goal(s): Predicting the prognosis of glioma by integrating inflammatory markers and MRI characteristics through machine learning models.

Approach: A total of 179 patients diagnosed with glioma were included in the analysis. Key MRI-derived features were examined alongside inflammatory markers. We evaluated various machine learning classifiers, using metrics such as AUC, accuracy, and F1 score.

Results: The RF model exhibited superior predictive performance, achieving an AUC of 0.90 and an F1 score of 0.997, highlighting its efficacy in accurately distinguishing between high-grade and low-grade gliomas.

Impact: 1.Key MRI-derived features and inflammatory markers were both used to train a model.
2.The models showed superior predictive performance.
3.The models can be used for distinguishing between high-grade and low-grade gliomas.

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