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Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

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ARTICLE DOWNLOAD

Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

10$

Jing Li, Di Dong, Mengjie Fang, Rui Wang, Jie Tian, Hailiang Li & Jianbo Gao 

Abstract

Objectives

To build a dual-energy CT (DECT)–based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

Materials and methods

Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28–81 years; 157 men [mean age, 60 years; range, 28–81 years] and 47 women [mean age, 54 years; range, 28–79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell’s concordance index (C-index) based on patients’ outcomes.

Results

The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002).

Conclusion

The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients’ prognosis.

Key Points

• This study investigated the value of deep learning dual-energy CT–based radiomics in predicting lymph node metastasis in gastric cancer.

• The dual-energy CT–based radiomics nomogram outweighed the single-energy model and the clinical model.

• The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.

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Year 2020
Language English
Format PDF
DOI 10.1007/s00330-019-06621-x