Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer

Abstract

Background: This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients.

Methods: A total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n = 80) and a testing cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station LNs based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.

Results: Two radiomic signatures, reflecting phenotypes of the tumor and LNs respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the training cohort (AUC 0.915; 95% confidence interval [CI] 0.832-0.998) and testing cohort (AUC 0.908; 95% CI 0.814-1.000). The decision curve also indicated its potential clinical usefulness.

Conclusions: The nomogram received favorable predictive accuracy in predicting No.3 LNM in T1-2 GC, and the nomogram showed positive role in predicting LNM in No.4 LNs. The nomogram may be used to predict LNM in T1-2 GC and could assist the choice of therapy.

Keywords: Lymph nodes; Nomogram; Stomach cancer.

Fig. 1

Radiomic workflow in this study

Fig. 2

Performance of nomogram, signatures and CT-reported metastasis LN status in training and testing cohorts. AUC area under the curve. The black line represents the result of the nomogram. The blue line represents the result of the radiomic signature-1. The green line represents the result of the radiomic signature-2. The red line represents the result of the CT-reported metastasis LN status. Abbreviations RS1 Radiomic signature 1, RS2 radiomic signature 2

Fig. 3

Nomogram and calibration curves. a Nomogram for the prediction of lymph node metastasis. b Calibration curves of the nomogram in the training cohort and c testing cohort. Calibration curves depict the calibration of the nomogram in terms of agreement between the predicted risk of lymph node metastasis and observed outcomes. The 45-degree blue dotted lines represent perfect prediction, and the pink lines represent the predictive performance of the nomogram. The closer the dotted line fit to the ideal line, the better the predictive accuracy of the nomogram. LN lymph node, CT computed tomography. Abbreviation RS1 Radiomic signature 1, RS2 radiomic signature 2

Fig. 4

The result of comparative experiments in the CT-reported LN metastasis-negative subgroup. The panel shows the ROC curve analysis for the nomogram in the CT-LNM0 subgroup. Abbreviations CT computed tomography, LN lymph node, ROC receiver operator characteristic, AUC area under the curve

Fig. 5

Decision curve analysis for the nomogram. The y-axis represents the net benefit, and the pink line represents the nomogram. The blue line represents the hypothesis that all patients had lymph node (LN) metastases, and the black line represents the hypothesis that no patients had LN metastases. The x-axis represents the threshold probability. The threshold probability is where the expected benefit of treatment is equal to the expected benefit of avoiding treatment. The decision curves in the training cohort showed that if the threshold probability is between 0 and 0.85, using the nomogram to predict LN metastases adds more benefit than treating either all or no patients