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Prediction Of Lymph Node Metastasis In Advanced Gastric Adenocarcinoma Based On Dual-energy Ct Radiomics: Focus On The Features Of Lymph Nodes With A Short Axis Diameter ≥6 Mm.

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Prediction Of Lymph Node Metastasis In Advanced Gastric Adenocarcinoma Based On Dual-energy Ct Radiomics: Focus On The Features Of Lymph Nodes With A Short Axis Diameter ≥6 Mm.

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Prediction of lymph node metastasis in advanced gastric adenocarcinoma based on dual-energy CT radiomics: focus on

Yang You¹, Yan Wang¹, Xianbo Yu²

¹Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.

Frontiers in Oncology

|March 18, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Keywords:

CT advanced gastric cancer dual-energy lymph node metastases radiomics

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Dual-energy CT (DECT) radiomics effectively predicts lymph node metastasis (LNM) in advanced gastric adenocarcinoma (GAC). DECT radiomics and combined models significantly outperform traditional clinical models for LNM prediction in lymph nodes (LNs) ≥6 mm.

Area of Science:

Radiology
Oncology
Medical Imaging

Background:

Advanced gastric adenocarcinoma (GAC) poses challenges in predicting lymph node metastasis (LNM).
Accurate assessment of lymph node (LN) status is crucial for treatment planning in GAC.
Dual-energy CT (DECT) offers advanced imaging capabilities for characterizing LNs.

Purpose of the Study:

To evaluate the diagnostic performance of DECT radiomics features in predicting LNM in advanced GAC.
To compare radiomics-based models with traditional clinical models for LNM prediction.
To assess the clinical utility of DECT radiomics for LNs with a short-axis diameter ≥6 mm.

Main Methods:

Retrospective analysis of GAC patients undergoing radical gastrectomy and LN dissection.

Extraction of radiomics features from DECT images (120 kV linear fusion and iodine maps).

Development of clinical, radiomics, and combined models using logistic regression and random forest, validated with ROC curves and DCA.

Main Results:

Radiomics and combined models demonstrated superior performance in predicting LNM compared to the clinical model in both training and validation sets (AUCs ranging from 0.970 to 0.986 vs. 0.772 to 0.820).
LN shape and radiomics score were significant predictors in the combined model.
Decision curve analysis indicated superior clinical benefits for the radiomics and combined models.

Conclusions:

DECT radiomics features provide high diagnostic performance for predicting LNM in LNs (≥6 mm) of advanced GAC.
Combined models integrating radiomics and traditional features offer robust prediction of LNM.
DECT radiomics is a valuable tool for improving the accuracy of LN staging in GAC.