ArticleBMC medical imaging2026
Preliminary machine-learning model with clinical, US, and CEUS features for the diagnosis of thyroid follicular-patterned lesions.
Article in BMC medical imaging, 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
9 authors.
Funding
Abstract
backgroundThough follicular thyroid carcinoma can be confirmed postoperatively by the histological findings of capsular or vascular invasion, preoperative diagnosis of follicular-patterned lesions has long been a diagnostic challenge. This study seeks to establish a machine-learning (ML) model based on clinical, US, and CEUS features for the differential diagnosis of thyroid follicular-patterned lesions (TFPLs).
methodsPatients with surgical pathologically confirmed TFPLs who underwent preoperative US and CEUS between January 2013 to April 2023 were enrolled in this retrospective study. We utilized five ML algorithms (logistic regression, random forest [RF], k-nearest neighbor [KNN], support vector machine [SVM], and elastic net [EN]) to construct an optimized model via US, CEUS and clinical data for the differential diagnosis of TFPLs. Model performance was evaluated with sensitivity, area under the precision-recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC).
results114 patients were finally included. The sensitivities of the five ML algorithms (Logistic, RF, KNN, SVM, EN) were 0.93, 0.97, 0.93, 0.97, 0.93 for the training set, and 0.40, 1.00, 0.80, 1.00, 0.60 for the test set. The AUPRCs were 0.91, 0.97, 0.99, 0.91, 0.91 for the training set, and 0.65, 0.71, 0.38, 0.57, 0.65 for the test set. The AUROCs were 0.95, 0.98, 0.99, 0.89, 0.93 for the training set. When applied to the test set, the RF model had a significantly higher AUROC value (0.92; 95% CI: 0.88, 0.96) than other ML algorithms (0.91, 0.89, 0.91, 0.91, P < .05) with significant features including peripheral halo sign, thyroglobulin, rim enhancement, peak intensity and wash-out time.
conclusionsOur ML model integrating clinical, US, and CEUS features achieved high sensitivity for preoperative differentiation of TFPLs, potentially guiding surgical planning-a step toward clinical use that requires CEUS standardization and external validation.
trial registrationThis study was registered at www.chictr.org.cn (no. ChiCTR2200066254, date: November 29, 2022).
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.