  **Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal
for diagnosis and treatment. The challenges lie in the irregular shapes,
blurred boundaries of tumors, and the inefficiency of existing methods.
  **Purpose:** We conducted a study to introduce a model, utilizing
human-guided knowledge and unique modules, to address the challenges of 3D
tumor segmentation.
  **Methods:** We developed the PropNet framework, propagating radiologists'
knowledge from 2D annotations to the entire 3D space. This model consists of a
proposing stage for coarse segmentation and a refining stage for improved
segmentation, using two-way branches for enhanced performance and an up-down
strategy for efficiency.
  **Results:** With 98 patient scans for training and 30 for validation, our
method achieves a significant agreement with manual annotation (Dice of 0.803)
and improves efficiency. The performance is comparable in different scenarios
and with various radiologists' annotations (Dice between 0.785 and 0.803).
Moreover, the model shows improved prognostic prediction performance (C-index
of 0.620 vs. 0.576) on an independent validation set of 42 patients with
advanced gastric cancer.
  **Conclusions:** Our model generates accurate tumor segmentation efficiently
and stably, improving prognostic performance and reducing high-throughput image
reading workload. This model can accelerate the quantitative analysis of
gastric tumors and enhance downstream task performance.
