# Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

arXiv: https://arxiv.org/abs/2008.11870 · HTML: https://mlsanity.com/paper/2008.11870/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 15 of 15)

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- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
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- [Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification](https://mlsanity.com/paper/2307.15271/) (2023)
- [Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer](https://mlsanity.com/paper/2404.03819/) (2024)
- [Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT](https://mlsanity.com/paper/2110.05280/) (2021)
- [Mediastinal Lymph Node Detection and Segmentation Using Deep Learning](https://mlsanity.com/paper/2212.11956/) (2022)
- [A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma](https://mlsanity.com/paper/2403.07092/) (2024)
- [Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment](https://mlsanity.com/paper/2404.17235/) (2024)
- [EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2506.06830/) (2025)
- [Integrating features from lymph node stations for metastatic lymph node detection](https://mlsanity.com/paper/2301.03202/) (2023)
- [From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans](https://mlsanity.com/paper/2503.07933/) (2025)

## Abstract (arXiv)

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTVLN). Determining and delineating the spread of GTVLN is essential in defining the corresponding resection and irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. In this work, we propose an effective distance-based gating approach to simulate and simplify the high-level reasoning protocols conducted by radiation oncologists, in a divide-and-conquer manner. GTVLN is divided into two subgroups of tumor-proximal and tumor-distal, respectively, by means of binary or soft distance gating. This is motivated by the observation that each category can have distinct though overlapping distributions of appearance, size and other LN characteristics. A novel multi-branch detection-by-segmentation network is trained with each branch specializing on learning one GTVLN category features, and outputs from multi-branch are fused in inference. The proposed method is evaluated on an in-house dataset of 141 esophageal cancer patients with both PET and CT imaging modalities. Our results validate significant improvements on the mean recall from 72.5% to 78.2%, as compared to previous state-of-the-art work. The highest achieved GTVLN recall of 82.5% at 20% precision is clinically relevant and valuable since human observers tend to have low sensitivity (around 80% for the most experienced radiation oncologists, as reported by literature).