  Pancreatic cancer remains one of the leading causes of cancer-related
mortality worldwide. Precise segmentation of pancreatic tumors from medical
images is a bottleneck for effective clinical decision-making. However,
achieving a high accuracy is often limited by the small size and availability
of real patient data for training deep learning models. Recent approaches have
employed synthetic data generation to augment training datasets. While
promising, these methods may not yet meet the performance benchmarks required
for real-world clinical use. This study critically evaluates the limitations of
existing generative-AI based frameworks for pancreatic tumor segmentation. We
conduct a series of experiments to investigate the impact of synthetic
\textit{tumor size} and \textit{boundary definition} precision on model
performance. Our findings demonstrate that: (1) strategically selecting a
combination of synthetic tumor sizes is crucial for optimal segmentation
outcomes, and (2) generating synthetic tumors with precise boundaries
significantly improves model accuracy. These insights highlight the importance
of utilizing refined synthetic data augmentation for enhancing the clinical
utility of segmentation models in pancreatic cancer decision making including
diagnosis, prognosis, and treatment plans. Our code will be available at
https://github.com/lkpengcs/SynTumorAnalyzer.
