The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer
Source : https://qims.amegroups.com/article/view/100581/html
The eighth edition of tumor node metastasis (TNM) classification is currently the most authoritative and generalized staging system for gastric cancer ( 1, 2). According to this staging system, T4b stage gastric cancer is defined as the tumor infiltrating the serosa and invading adjacent structures and organs; accounting for 6-27% of all gastric cancers.
Conclusions: CE-CT-based radiomics nomogram offers good accuracy and stability in differentiating preoperative cT4 stage gastric cancer patients into pT4b and non-pT4b stages, which has a great clinical relevance for selecting the course of treatment for cT4 stage gastric cancer patients.
• Conclusions: “CE-CT-based radiomics nomogram offers good accuracy and stability in differentiating preoperative cT4 stage gastric cancer patients into pT4b and non-pT4b stages, which has a great clinical relevance for selecting the course of treatment for cT4 stage gastric cancer patients.”
• Chinese researchers assessed the predictive potential of radiomics models based on contrast-enhanced computed tomography (CE-CT) images to identify pT4b stage patients among cT4 stage gastric cancer patients.
• The investigators developed a nomogram that combined the clinical features and Rad-score to identify pT4b stage patients among preoperative cT4b stage gastric cancer patients.
• “The nomogram in this study provides a personalized reference potential for preoperative prediction of the pathological status of the tumor,” the researchers wrote. “The AJCC gastric cancer guidelines (1) recommend that cT4b stage patients should discuss the treatment options with the multi-disciplinary treatment (MDT), which achieved a personalized diagnosis and treatment plan, fill in the shortcomings of the disciplines. Therefore, when faced with decision-making difficulties such as deciding whether the tumor can be completely resected, the results from this study may provide some useful reference for decision-making.”
• Limitations of the current study include its retrospective nature and the use of machine-learning algorithms without advanced dimensionality reduction techniques to create models. Additionally, the segmentation of tumors with ill-defined boundaries is controversial.