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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