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Updated: Jun 30, 2026
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Self-supervised 3D deep learning on preoperative contrast-enhanced computed tomography for predicting high pathologic
Qian Li1,2, Yongxin Li2,3, Jing Bai4
1Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Background:
Accurate preoperative identification of high nodal burden (pathologic N2 or N3; hereafter N2+) is important in esophageal squamous cell carcinoma, but contrast-enhanced computed tomography criteria based mainly on nodal size and morphology have limited sensitivity.
Methods:
In this retrospective multicohort study, 1,060 consecutive patients with esophageal squamous cell carcinoma who underwent preoperative contrast-enhanced computed tomography and curative-intent esophagectomy with lymphadenectomy were enrolled from two centers. Center A contributed a development cohort (n = 612; train/validation/internal test, 428/92/92) and a temporally held-out cohort (n = 238), and Center B contributed an external test cohort (n = 210). A three-dimensional residual convolutional neural network encoder was pretrained on 3,200 unlabeled chest computed tomography examinations using masked-volume reconstruction and then fine-tuned on tumor-centered volumes comprising the primary tumor plus a 5-mm margin.
Results:
The self-supervised model achieved area under the receiver operating characteristic curve values of 0.881 (95% confidence interval, 0.793-0.955) in the internal test cohort, 0.860 (95% confidence interval, 0.810-0.903) in the temporal cohort, and 0.860 (95% confidence interval, 0.810-0.906) in the external cohort. In the external cohort, sensitivity and specificity at the main operating point were 0.581 and 0.845 for the self-supervised model versus 0.339 and 0.784 for the guideline-inspired comparator. Calibration also improved with self-supervised pretraining (Brier score, 0.148; expected calibration error, 0.053).
Conclusion:
A contrast-enhanced computed tomography-only self-supervised three-dimensional model predicted high pathologic nodal burden in esophageal squamous cell carcinoma with robust temporal and external validation and showed numerically higher performance than a transparent computed tomography-only comparator. Calibrated risk estimates may help prioritize additional nodal workup when staging resources are limited or routine computed tomography findings are equivocal.
