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Abstract #3113

Automated Sacral Tumor Localization and Multi-Class Identification of Six Tumor Types in NCCT with an innovative CL-MedImageNet Fusion Model

Fei Zheng1, Ping Yin1, Wenjia Zhang1, and Nan Hong1
1Peking University people’ hospital, Beijing, China

Synopsis

Keywords: Bone/Skeletal, Bone, Deep learning · CNN · Sacral tumors · Classification

Motivation: Accurate preoperative identification of tumor types is essential for personalized treatment.

Goal(s): To develop a fully automated hybrid approach to predict sacral tumor types from preoperative NCCT images.

Approach: We built an automated hybrid model integrating two deep CNN models (Model 1 and Model 2) through a fully automated pipeline.

Results: The CL-MedImageNet model achieved the best results, with macro average AUC of 0.891, 0.883, and 0.874 for the validation set, internal test set, and external test set, respectively. The CL-MedImageNet model also surpassed the radiologist’s performance.

Impact: Our model reliably predicts sacral tumor types using a fully automated NCCT process, improving individualized treatment planning with its high reproducibility and generalizability.

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