ArticleThe British journal of radiology2022
The efficacy of
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- [Cancers · 2026Article
- Preoperative prediction of thymoma risk classification with machine learning-based computed tomography radiomics features.World journal of surgical oncology · 2026Article
- A clinically applicable and generalizable deep learning model for anterior mediastinal tumors in CT images across multiple institutions.Scientific reports · 2026Article
- Fusion of machine learning models using fuzzy comprehensive evaluation for thymoma risk prediction: a multicenter analysis.Updates in surgery · 2025Article
- Prediction of pathological risk subtypes of thymic anterior mediastinal cysts and thymic epithelial tumors based on CT radiomics and deep learning methods: a retrospective study.European journal of medical research · 2025Article
- The Value of Baseline [Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning Models Derived from [Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Radiomics in thymic epithelial tumors: a scoping review of current status and advances.BMC cancer · 2025Article
- The Role of [Molecular diagnosis & therapy · 2025Article
- Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors.Frontiers in oncology · 2025Article
- Incremental diagnostic value of tumor habitat radiomics for risk stratification in thymic epithelial tumors.Frontiers in oncology · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.BMC cancer · 2024Article
- Re-evaluation and operative indications after induction therapy for thymic epithelial tumors.Mediastinum (Hong Kong, China) · 2024Review
- Clinical application ofJapanese journal of radiology · 2024Review
- Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.European radiology · 2023Article
- Is PET/CT Able to Predict Histology in Thymic Epithelial Tumours? A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (
methodsThis retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic
resultsSUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The best-performing algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than three SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each;
conclusionADVANCES IN KNOWLEDGE: Machine-learning approach using
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.