ArticleJournal of cancer research and clinical oncology2023
Nomogram to predict the risk of biochemical recurrence and structural recurrence in patients with stage cN1 papillary thyroid carcinoma.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for postoperative recurrence in papillary thyroid carcinoma: a systematic review and critical appraisal.Frontiers in endocrinology · 2026Pooled it
- Development and validation of a machine learning-based hierarchical classification model for predicting the early therapeutic response to initialAnnals of nuclear medicine · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
introductionAlthough papillary thyroid carcinoma (PTC) is thought to be the least aggressive thyroid cancer, it has a significant recurrence rate. Therefore, we aimed to develop a nomogram to estimate the probability of biochemical recurrence (BIR) and structural recurrence (STR) in patients with stage cN1 PTC.
methodsWe studied the relationship between the characteristics of patients with stage N1a PTC and the risk of recurrence by analysing the data of 617 inpatients (training cohort) and 102 outpatients (validation cohort) in our hospital. We used the least absolute shrinkage and selection operator regression model to identify prognostic indicators to construct nomograms to predict the risk of BIR and STR.
resultsThere were 94 (15.24%) BIR cases in the training cohort and 36 (35.29%) in the validation cohort. There were 31 (5.02%) STR cases in the training cohort and 23 (22.55%) cases in the validation cohort. The variables included in the BIR nomogram were sex, age at diagnosis, tumour size, extrathyroidal infiltration, and lymph node ratio (LNR). While the variables included in the STR nomogram were tumour size, extrathyroidal infiltration, BRAF state, metastatic lymph nodes, and LNR. Both the prediction models demonstrated good discrimination ability. The results showed the calibration curve of the nomogram was near the optimum diagonal line, and the decision curve analysis showed a noticeably better benefit.
conclusionThe LNR may be a valid prognostic indicator for patients with stage cN1 PTC. The nomograms could help clinicians identify high-risk patients and choose the best postsurgical therapy and monitoring.
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