ArticleFrontiers in medicine2026
Development and validation of a machine learning model for predicting high-risk distant metastatic recurrence in differentiated thyroid cancer.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Distant metastatic recurrence significantly impacts the prognosis of patients with differentiated thyroid cancer (DTC). Current risk stratification systems have limited accuracy in predicting high-risk distant metastatic recurrence. Objective: This study aimed to develop and validate a machine learning model for predicting high-risk distant metastatic recurrence in DTC patients. Methods: We retrospectively analyzed 1,245 DTC patients treated between January 2020 and December 2024. Patients were randomly divided into training ( Results: During a median follow-up of 72 months, 126 patients (10.1%) developed distant metastatic recurrence. LASSO regression identified eight predictors: age, tumor size, extrathyroidal extension, lymph node metastasis, BRAF V600E mutation, postoperative stimulated thyroglobulin (sTg) level, radioactive iodine dose, and TNM stage. The XGBoost model demonstrated the best performance, with an AUC of 0.88 (95% CI, 0.83-0.93) in the validation set. Patients were stratified into low-risk (recurrence rate: 1.7%), intermediate-risk (14.4%), and high-risk (64.1%) groups with significantly different distant metastasis-free survival ( Conclusion: We developed and validated an XGBoost-based machine learning model that accurately predicts high-risk distant metastatic recurrence in DTC patients. This model may help clinicians identify patients who could benefit from more aggressive treatment and intensive follow-up, enabling personalized management strategies.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.