A Chinese research team reported that machine learning analysis of tumor sub-regions on plain, non-contrast CT scans can accurately distinguish benign from malignant sacral tumors before surgery. The approach leverages sub-region characterization rather than relying solely on standard radiomic summaries, aiming to improve preoperative diagnostic confidence when biopsy and surgical decision-making hinge on classification. If validated in broader cohorts, the model could reduce diagnostic uncertainty and streamline surgical planning for sacral lesions. The study adds to growing evidence that interpretable imaging AI can target specific anatomical tasks with clinically relevant performance.