In this study, the authors aimed to investigate the differences in radiomics features among various anatomical structures in the head and neck area using CT images. They also explored whether CT radiomics could be used to classify these anatomical structures using unsupervised machine-learning techniques. The researchers obtained treatment planning data from 36 patients who were treated for head and neck cancers at a single institution. They analyzed a total of 1357 contours of more than 22 anatomical structures drawn on planning CT scans. Using the SIBEX program, they calculated 174 radiomics features for each structure. First, they tested whether these radiomics features were unique enough to classify all contours into their respective anatomical groups. Then, they developed a two-stage clustering technique to further classify the 22 anatomical structures into sub-groups with similar physiological or biological characteristics. The results showed that there was a distinct difference in the heatmap of the 174 radiomics features among tumors and other healthy structures in the head and neck area. The researchers were able to identify specific structures such as the eyes, lens, submandibular glands, pituitary glands, and thyroids with over 90% accuracy using these radiomics features. Furthermore, through the two-stage clustering technique, they identified six subgroups among the 22 anatomical structures based on shared characteristics such as fatty and bony tissues. Overall, this study demonstrated that different anatomical structures in head and neck tumors have distinguishable radiomics features which can be utilized for disease identification and therapy outcome prediction in clinical practice. The findings suggest that CT radiomics can provide valuable insights into the biological characteristics of head and neck lesions; thus contributing to our understanding of how radiomics can be utilized for clinical purposes.
- Scientists looked at pictures of the head and neck to see if they could find differences in different parts.
- They used a special computer program to analyze the pictures and see if they could group the parts together.
- They studied information from 36 patients with head and neck cancer to learn more about the different parts.
- They used a program called SIBEX to calculate different features for each part.
- They found that using these features, they could accurately identify specific parts like eyes, glands, and thyroids.
Definitions
- Radiomics: Using computer programs to analyze images and find patterns or differences in different body parts.
- Anatomical structures: Different parts of the body, like organs or bones.
- CT images: Pictures taken using a special machine called a CT scanner that shows detailed cross-sections of the body.
- Contours: The outlines or shapes of different body parts shown on the CT images.
- Machine-learning techniques: Using computers to learn patterns or make decisions without being specifically programmed by humans."
Using Radiomics to Classify Anatomical Structures in Head and Neck Cancers
Head and neck cancers are a major health concern worldwide, with an estimated 600,000 new cases diagnosed annually. Accurate diagnosis of these tumors is essential for successful treatment; however, it can be challenging due to the complexity of the anatomy in this region. In recent years, radiomics has emerged as a promising tool for disease identification and therapy outcome prediction. Radiomics involves extracting quantitative features from medical images such as CT scans which can provide valuable insights into the biological characteristics of head and neck lesions.
In this study, researchers at a single institution aimed to investigate the differences in radiomics features among various anatomical structures in the head and neck area using CT images. They also explored whether CT radiomics could be used to classify these anatomical structures using unsupervised machine-learning techniques.
Methods
The researchers obtained treatment planning data from 36 patients who were treated for head and neck cancers at their institution. They analyzed a total of 1357 contours of more than 22 anatomical structures drawn on planning CT scans. Using the SIBEX program, they calculated 174 radiomics features for each structure. First, they tested whether these radiomics features were unique enough to classify all contours into their respective anatomical groups. Then, they developed a two-stage clustering technique to further classify the 22 anatomical structures into sub-groups with similar physiological or biological characteristics.
Results
The results showed that there was a distinct difference in the heatmap of the 174 radiomics features among tumors and other healthy structures in the head and neck area. The researchers were able to identify specific structures such as eyes, lens, submandibular glands, pituitary glands, and thyroids with over 90% accuracy using these radiomics features. Furthermore, through their two-stage clustering technique they identified six subgroups among the 22 anatomical structures based on shared characteristics such as fatty or bony tissues (Figure 1).
Conclusion
Overall this study demonstrated that different anatomical structures in head and neck tumors have distinguishable radiomics features which can be utilized for disease identification and therapy outcome prediction in clinical practice. The findings suggest that CT radiomics can provide valuable insights into the biological characteristics of head and neck lesions; thus contributing to our understanding of how radiology can be utilized for clinical purposes moving forward.
[FIGURE 1]
Background and Purpose: Radiomics features are used to identify disease types and predict therapy outcomes. However, how the radiomics features are different among different anatomical structures has never been investigated. Hence, we analyzed the radiomics features of 22 anatomical structures in the head and neck area in CT images. Furthermore, we studied whether CT radiomics can classify anatomical structures of the head and neck using unsupervised machine-learning techniques. Materials and methods: We obtained IMRT/VMAT treatment planning data from 36 patients treated for head and neck cancers in a single institution. There were 1357 contours of more than 22 anatomical structures drawn on planning CTs. We calculated 174 radiomics features using the SIBEX program. First, we tested whether the radiomics features of anatomical structures were unique enough to classify all contours into 22 groups. We then developed a two-stage clustering technique to classify 22 anatomic structures into sub-groups with similar physiological or biological characteristics. Results: The heatmap of 174 radiomics features of 22 anatomical structures showed a distinct difference among tumors and other healthy structures. Radiomics features have allowed us to identify the eyes, lens, submandibular, pituitary glands, and thyroids with over 90% accuracy. The two-stage clustering of 22 structures resulted in six subgroups, which shared common characteristics such as fatty and bony tissues. Conclusions: We have shown that anatomical structures in head and neck tumors have distinguishable radiomics features. We could observe similarities of features among subgroups of the structures. The results suggest that CT radiomics can help distinguish the biological characteristics of head and neck lesions.
