ArticleFrontiers in oncology2022
Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Pooled it
- Predicting the risk of bone erosion in rheumatoid arthritis using a SHAP-based interpretable machine learning model.Clinical rheumatology · 2026Article
- Development and internal validation of a multimodal MRI-FNAC radiomics-pathomics model for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Gland surgery · 2026Article
- Predicting knowledge of cervical cancer screening among reproductive-age women in sub-saharan africa using machine learning algorithms.Scientific reports · 2026Article
- Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.Scientific reports · 2026Article
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- Noninvasive immune-inflammatory profiling by ultrasound radiomics for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma.Frontiers in immunology · 2026Article
- Dual-modality ultrasound radiomics model for classifying diabetic peripheral neuropathy in type 2 diabetes: a multicenter prospective study.Frontiers in endocrinology · 2026Article
- The Effect of Ultrasound Image Pre-Processing on Radiomics Feature Quality: A Study on Shoulder Ultrasound.Journal of imaging informatics in medicine · 2025Article
- A deep learning-clinical combined model with SHapley Additive exPlanations (SHAP) method for assessing the tumor spread through air spaces in lung adenocarcinoma: a multicohort retrospective study.Quantitative imaging in medicine and surgery · 2025Article
- Radiomics and deep learning model based on X-ray imaging for the assisted diagnosis of early Legg-Calvé-Perthes disease.BMC musculoskeletal disorders · 2025Article
- Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes.Diagnostics (Basel, Switzerland) · 2025Article
- Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms.European journal of medical research · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Perspective: Global Burden of Iodine Deficiency: Insights and Projections to 2050 Using XGBoost and SHAP.Advances in nutrition (Bethesda, Md.) · 2025Article
- Machine learning based prediction models for the prognosis of COVID-19 patients with DKA.Scientific reports · 2025Article
- CT radiomics analysis facilitates preoperative risk stratification of central lymph node metastasis in papillary thyroid cancer: a multicenter study.Frontiers in oncology · 2025Article
- Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models.Scientific reports · 2024Article
- Prediction of Post-Treatment Visual Acuity in Age-Related Macular Degeneration Patients With an Interpretable Machine Learning Method.Translational vision science & technology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: A radiomics-based explainable eXtreme Gradient Boosting (XGBoost) model was developed to predict central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC), including positive and negative effects. Methods: A total of 587 PTC patients admitted at Binzhou Medical University Hospital from 2017 to 2021 were analyzed retrospectively. The patients were randomized into the training and test cohorts with an 8:2 ratio. Radiomics features were extracted from ultrasound images of the primary PTC lesions. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select CCLNM positively-related features and radiomics scores were constructed. Clinical features, ultrasound features, and radiomics score were screened out by the Boruta algorithm, and the XGBoost model was constructed from these characteristics. SHapley Additive exPlanations (SHAP) was used for individualized and visualized interpretation. SHAP addressed the cognitive opacity of machine learning models. Results: Eleven radiomics features were used to calculate the radiomics score. Five critical elements were used to build the XGBoost model: capsular invasion, radiomics score, diameter, age, and calcification. The area under the curve was 91.53% and 90.88% in the training and test cohorts, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, radiomics score, diameter, and calcification) and negative (i.e., age) impacts. The XGBoost model outperformed the radiologist, increasing the AUC by 44%. Conclusions: The radiomics-based XGBoost model predicted CCLNM in PTC patients. Visual interpretation using SHAP made the model an effective tool for preoperative guidance of clinical procedures, including positive and negative impacts.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.