ArticleOncology letters2019
A nomogram to predict vascular invasion before resection of colorectal cancer.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography.Surgical endoscopy · 2023Article
- Nomogram for predicting occurrence of synchronous liver metastasis in colorectal cancer: a single-center retrospective study based on pathological factors.World journal of surgical oncology · 2022Article
- A Nomogram to Predict Critical Weight Loss in Patients with Nasopharyngeal Carcinoma During (Chemo) Radiotherapy.Clinical Medicine Insights. Oncology · 2022Article
- Development and Validation of a Nomogram for Predicting the Unresolved Risk of Parents of Adolescents With Psychiatric Diagnoses.Frontiers in psychiatry · 2022Article
- Nomogram based on clinical characteristics for preoperative prediction of perineural invasion in gastric cancer.The Journal of international medical research · 2020Article
- Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma.Evolutionary bioinformatics online · 2020Article
- Article
- A Novel Nomogram for Individually Predicting of Vascular Invasion in Gastric Cancer.Technology in cancer research & treatmentArticle
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. In the present study, a predictive nomogram for VI in patients with CRC was constructed. The prediction model consisted of 664 eligible patients with CRC, who were divided into a training set (n=468) and a validation set (n=196). Data were collected between August 2013 and April 2018. The feature selection model was established using the least absolute shrinkage and selection operator regression model. Multivariable logistic regression analysis was used to construct the predictive nomogram. The performance of the nomogram was evaluated by calibration, discrimination and clinical usefulness. Differentiation, computed tomography (CT)-based on N stage (CT N stage), hemameba and tumor distance from the anus (cm) were integrated into the nomogram. The nomogram exhibited good discrimination, with an area under the curve (AUC) of 0.731 and good calibration. Application of the nomogram in the validation cohort showed acceptable discrimination, with an AUC of 0.710 and good calibration. Decision curve analysis revealed that the nomogram was clinically useful. These findings suggests, to the best of our knowledge, that this may be the first nomogram for individual preoperative prediction of VI in patients with CRC, which may promote preoperative optimization strategies for this selected group of patients.
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