ArticleLangenbeck's archives of surgery2024
Construction of a nomogram based on clinicopathologic features to predict the likelihood of No. 253 lymph node metastasis in rectal cancer patients.
Article in Langenbeck's archives of surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Development and validation of a model for predicting central lymph node metastasis in colorectal cancer based on CT textural features and clinical indicators.Translational cancer research · 2026Article
- Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo explore the high-risk factors for rectal cancer No.253 lymph node metastasis (LNM) and to construct a risk nomogram for the individualized prediction of No.253 LNM.
methodsThis was a retrospective analysis of 425 patients with rectal cancer who underwent laparoscopic-assisted radical surgery. Independent risk factors for rectal cancer No.253 LNM was identified using multivariate logistic regression analysis, and a risk prediction nomogram was constructed based on the independent risk factors. In addition, the performance of the model was evaluated by discrimination, calibration, and clinical benefit.
resultsMultivariate logistic regression analysis showed that No.253 lymphadenectasis on CT (OR 10.697, P < 0.001), preoperative T4-stage (OR 4.431, P = 0.001), undifferentiation (OR 3.753, P = 0.004), and preoperative Ca199 level > 27 U/ml (OR 2.628, P = 0.037) were independent risk factors for No.253 LNM. A nomogram was constructed based on the above four factors. The calibration curve of the nomogram was closer to the ideal diagonal, indicating that the nomogram had a better fitting ability. The area under the ROC curve (AUC) was 0.865, which indicated that the nomogram had high discriminative ability. In addition, decision curve analysis (DCA) showed that the model could show better clinical benefit when the threshold probability was between 1% and 50%.
conclusionPreoperative No.253 lymphadenectasis on CT, preoperative T4-stage, undifferentiation, and elevated preoperative Ca199 level were found to be independent risk factors for the No.253 LNM. A predictive model based on these risk factors can help surgeons make rational clinical decisions.
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