Accurate tumor segmentation in medical imaging is crucial for effective diagnosis and treatment planning in cancer care. This research compares the performance of the classical Active Contour Model (ACM) with that of the Medical Segment Anything Model (MedSAM) for tumor segmentation across datasets, including ultrasound, mammograms, and digital breast tomosynthesis (DBT). The findings revealed that MedSAM significantly outperforms ACM in both ultrasound and mammogram applications, owing to its transformer-based architecture, which adeptly handles noise and the diverse appearances of tumors. However, both methods demonstrated comparable performance in DBT. The findings advocate integrating MedSAM into clinical workflows for 2D breast imaging, while highlighting specific challenges in 3D modalities that necessitate further specialized algorithmic development.