ArticleTechnology in cancer research & treatment
Predictive Value of a Prognostic Model Based on Lymphocyte-to-Monocyte Ratio Before Radioiodine Therapy for Recurrence of Papillary Thyroid Carcinoma.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Pre-RAI Monocyte-to-Lymphocyte Ratio Predicts Early and Higher Recurrence Risk in Intermediate-Risk DTC, While PNI and NRI Show No Prognostic Value.Clinical endocrinology · 2026Article
- Explainable multi-modal machine learning for predicting occult pulmonary metastases in differentiated thyroid cancer: a SHAP-based approach prior to radioactive iodine scans.Frontiers in medical technology · 2025Article
- Association between blood inflammatory indicators and prognosis of papillary thyroid carcinoma: a narrative review.Gland surgery · 2024Review
- Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.BMC cancer · 2024Article
- A novel signature to predict thyroid cancer prognosis and immune landscape using immune-related LncRNA pairs.BMC medical genomics · 2022Article
- Association of Neutrophil-to-Lymphocyte Ratio and Lymphocyte-to-Monocyte Ratio with Clinicopathological Features and Short-Term Outcome in Well-Differentiated Thyroid Cancer.Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, IndiaArticle
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to investigate the predictive value of a prognostic model based on the lymphocyte-to-monocyte ratio (LMR) before radioiodine treatment for the recurrence of papillary thyroid carcinoma (PTC).
methodsClinicopathological data of 441 patients with papillary thyroid cancer were collected retrospectively. The Receiver operating characteristic (ROC) was used to determine the optimal cut-off value for predicting PTC recurrence by LMR before radioiodine treatment. Recurrence was the endpoint of the study, and survival was estimated by the Kaplan-Meier method, and any differences in survival were evaluated with a stratified log-rank test. Univariate and multifactorial analyses were performed using Cox proportional-hazards models to identify risk factors associated with PTC recurrence.
resultsThe ROC curve showed that the best cut-off value of LMR before radioiodine treatment to predict recurrence in patients with PTC was 6.61, with a sensitivity of 54.1%, a specificity of 73%, and an area under the curve of 0.628. The recurrence rate was significantly higher in the low LMR group (16%) than in the high LMR group (5%) (
conclusionsThe LMR-based prognostic model before radioactive iodine treatment is valuable for early prediction of PTC recurrence and it can be used in clinical practice as a supplement to risk stratification and applied in combination to help screen out patients with poorer prognosis early.
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