Dong, D;
Fang, M-J;
Tang, L;
Shan, X-H;
Gao, J-B;
Giganti, F;
Wang, R-P;
... Tian, J; + view all
(2020)
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multi-center study.
Annals of Oncology
10.1016/j.annonc.2020.04.003.
(In press).
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Abstract
BACKGROUND: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough. PATIENTS AND METHODS: We enrolled 730 LAGC patients from 5 centers in China and 1 center in Italy, and divided them into 1 primary cohort, 3 external validation cohorts, and 1 international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multi-phase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis. RESULTS: The DLRN showed good discrimination of the number of LNM on all cohorts (overall C-indexes: 0.821, 95% CI: 0.785-0.858 in the primary cohort; 0.797, 95% CI: 0.771-0.823 in the external validation cohorts; and 0.822, 95% CI: 0.756-0.887 in the international validation cohort). The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (p<0.05). Besides, DLRN is significantly associated with the overall survival of LAGC patients (n=271). CONCLUSION: A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.
| Type: | Article |
|---|---|
| Title: | Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multi-center study. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.annonc.2020.04.003 |
| Publisher version: | https://doi.org/10.1016/j.annonc.2020.04.003 |
| Language: | English |
| Additional information: | © 2020 The Author(s). Published by Elsevier Ltd on behalf of European Society for Medical Oncology. Under a Creative Commons license |
| Keywords: | Deep learning, Locally advanced gastric cancer, Lymph node metastasis, Radiomic nomogram |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10096530 |
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