Related Experiment Video
Updated: Jan 28, 2026
High-frequency Ultrasound Imaging of Mouse Cervical Lymph Nodes
Published on: July 25, 2015
Spatially aware radiomics integrating anatomical knowledge to improve lymph node malignancy prediction in head and
Liyuan Chen1, Sepeadeh Radpour1, Michael Dohopolski1
1Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA.
Background:
Radiomics holds the potential to improve the diagnostic evaluation of equivocal lymph nodes in head and neck cancer (HNC). While conventional radiomics models utilize features such as intensity, geometry, and texture of individual lymph node, they often neglect key spatial and anatomical characteristics tied to lymphatic dissemination patterns.
Purpose:
In this study, we propose a novel spatially aware radiomics model that integrates anatomical knowledge and clinical factors to enhance lymph node malignancy prediction.
Methods:
A total of 1389 lymph nodes (1119 benign and 270 malignant), contoured on CT scans from 192 HNC patients were included. Two models were developed: a baseline model using conventional radiomics features and an enhanced model incorporating five additional spatial and anatomical features, such as primary tumor type, lymph node level, the laterality of the primary tumor, the laterality of the lymph node, and the distance from the lymph node to the primary tumor. Sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve (AUC) criteria were used to evaluate the model performance.
Results:
The proposed spatially aware radiomics model significantly outperformed the baseline model. The baseline model achieved SEN = 0.915, SPE = 0.756, ACC = 0.787, PPV = 0.475, NPV = 0.974, and AUC = 0.931. The enhanced model achieved SEN = 0.919, SPE = 0.845, ACC = 0.860, PPV = 0.589, NPV = 0.977, and AUC = 0.953. Statistical testing confirmed a significant improvement in both accuracy (p = 3.71 × 10-20) and AUC (p = 1.13 × 10-4).
Conclusions:
This study demonstrates that incorporating lymphatic anatomy and clinical context into radiomics models significantly improves predictive performance. The proposed approach enhances interpretability, aligns with clinical workflows, and holds promises for personalized radiation therapy planning.
More Related Videos
05:52Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
Published on: August 19, 2021
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
01:26Arteries of the Head and Neck
The internal carotid arteries supply blood to the anterior portion of the cerebrum. They enter the...
01:19Veins of Head and Neck
On the other hand, the vertebral veins, unlike their arterial counterparts, are not primarily responsible for brain drainage. Instead, they drain the cervical vertebrae, spinal cord, and some small...
01:23Detailed Structure and Function of Lymph Nodes
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...
01:21Self-Awareness and Its Effects
01:06Altered States of Awareness
The ingestion of substances like stimulants or hallucinogens leads to chemical alterations in the brain...
01:15Subconsciousness and No Awareness
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...