Accurate tumor staging in lung cancer is crucial for prognosis and treatment planning and is governed by explicit anatomical criteria under fixed guidelines. However, most existing deep learning approaches treat this spatially structured clinical decision as an uninterpretable image classification problem. Tumor stage depends on predetermined quantitative criteria, including the tumor's dimensions and its proximity to adjacent anatomical structures, and small variations can alter the staging outcome. To address this gap, we propose AnatomicalNets, a medically grounded, multi-stage pipeline that reformulates tumor staging as a measurement and rule-based inference problem rather than a learned mapping. We employ three dedicated encoder-decoder networks to precisely segment the lung parenchyma, tumor, and mediastinum. The diaphragm boundary is estimated via a lung-contour heuristic, while the tumor's largest dimension and its proximity to adjacent structures are computed through a contour-based distance estimation method. These features are passed through a deterministic decision module following the international association for the study of lung cancer guidelines. Evaluated on the Lung-PET-CT-Dx dataset, AnatomicalNets achieves an overall classification accuracy of 91.36%. We report the per-stage F1-scores of 0.93 (T1), 0.89 (T2), 0.96 (T3), and 0.90 (T4), a critical evaluation aspect often omitted in prior literature. We highlight that the representational bottleneck in prior work lies in feature design rather than classifier capacity. This work establishes a transparent and reliable staging paradigm that bridges the gap between deep learning performance and clinical interpretability.

翻译：肺癌的准确肿瘤分期对于预后判断和治疗方案制定至关重要，且需遵循既定指南下的明确解剖学标准。然而，现有深度学习方法大多将这一具有空间结构的临床决策视为不可解释的图像分类问题。肿瘤分期取决于预定的定量标准，包括肿瘤尺寸及其与邻近解剖结构的距离，细微变化即可改变分期结果。为弥补这一差距，我们提出了AnatomicalNets——一种基于医学原理的多阶段管线，将肿瘤分期重构为基于测量与规则推理的问题，而非学习型映射。我们采用三个专用编码器-解码器网络分别精确分割肺实质、肿瘤和纵隔。通过肺轮廓启发式方法估计膈肌边界，并基于轮廓的距离估计方法计算肿瘤最大尺寸及其与邻近结构的距离。这些特征通过遵循国际肺癌研究协会指南的确定性决策模块进行处理。在Lung-PET-CT-Dx数据集上的评估显示，AnatomicalNets实现了91.36%的整体分类准确率。我们报告了各分期的F1分数：T1为0.93，T2为0.89，T3为0.96，T4为0.90——这是先前文献中常被忽略的关键评估维度。我们指出，先前工作的表征瓶颈在于特征设计而非分类器能力。本研究建立了透明可靠的分期范式，弥合了深度学习性能与临床可解释性之间的鸿沟。