**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.

翻译：**背景：** 胃肿瘤在CT扫描中的精确3D分割对于诊断与治疗至关重要。其挑战在于肿瘤形态不规则、边界模糊以及现有方法的低效性。**目的：** 我们开展了一项研究，引入一个融合人类引导知识与独特模块的模型，以应对3D肿瘤分割的挑战。**方法：** 我们开发了PropNet框架，将放射科医师的2D标注知识传播至整个3D空间。该模型包含一个用于粗分割的提议阶段和一个用于优化分割的细化阶段，通过双向分支提升性能，并采用上下策略提高效率。**结果：** 利用98例患者扫描数据进行训练及30例进行验证，我们的方法实现了与人工标注高度一致的结果（Dice系数为0.803），并提升了效率。在不同场景及不同放射科医师的标注下，模型表现稳定（Dice系数介于0.785至0.803之间）。此外，在包含42例晚期胃癌患者的独立验证集上，模型改善了预后预测性能（C-index：0.620 vs. 0.576）。**结论：** 我们的模型能够高效稳定地生成精确的肿瘤分割结果，提升预后性能并减少高通量图像阅片工作量。该模型可加速胃肿瘤的定量分析，并增强下游任务性能。