Abstract #1534

Developing a radiomics model to predict tumor consistency of pituitary adenomas using multicenter MRI data

Jushen Wu¹, Pengcheng Wang¹, Jiayu Xiao², Gabriel Zada³, Jonathan Chen⁴, Eddie Briseno⁵, and Zhaoyang Fan⁶

¹Department of Biomedical Engineering, University of Southern California, los angeles, CA, United States, ²Department of Radiology, University of Southern California, los angeles, CA, United States, ³Deaprtment of Neurological Surgery, University of Southern California, los angeles, CA, United States, ⁴Diamond bar high school, los angeles, CA, United States, ⁵Keck school of medicine, University of Southern California, los angeles, CA, United States, ⁶Deaprtment of Radiology Oncology, University of Southern California, los angeles, CA, United States

Synopsis

Keywords: Diagnosis/Prediction, Radiomics

Motivation: Knowing the consistency of pituitary adenomas pre-surgery is crucial, yet current radiomic studies lack emphasis on multi-center patient data and more precise prediction.

Goal(s): This study developed radiomic models to classify adenomas from multi-center patients into three consistency levels.

Approach: Following image preprocessing, a novel feature engineering method called Feature Gradient was applied to select optimal feature subsets for the SVM classification models.

Results: Across seven SVM classifiers, the average AUC and accuracy scores on the testing set were 0.68 and 0.66, respectively, and three-sequences radiomic model achieved AUC and accuracy scores of 0.79 and 0.8.

Impact: This research underscores the importance of applying radiomics to diverse, generalized patient data, advancing its potential for real-world clinical use and demonstrating its adaptability to varied patient and imaging conditions in practical medical scenarios.

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