ArticleAbdominal radiology (New York)2026
Machine learning of multiparametric MRI radiomics preoperatively distinguishes testicular seminoma from non-seminoma: a multicenter study.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Progress in the research of artificial intelligence in andrology: a narrative review.Translational andrology and urology · 2026Review
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.
Funding
Abstract
RATIONALE AND
objectivesThis study aims to develop an optimal model for distinguishing seminoma from non-seminoma testicular tumors using machine learning classifiers based on multiparametric MRI radiomics. MATERIALS AND
methodsThis multi-institutional study enrolled a total of 188 patients, including 83 with seminoma and 105 with non-seminoma. The cohort from Institution 1 (n = 137) served as the training and validation set, whereas the independent cohort from Institution 2 (n = 51) was designated as the test set. Manual segmentation of tumor regions of interest (ROIs) was performed by experienced researchers on DWI, ADC, T2WI, and CE-T1WI sequences. A comprehensive radiomics workflow was implemented, encompassing data standardization, dimensionality reduction, feature selection, and classification using six distinct machine learning classifiers. Predictive models were developed by integrating radiomics features extracted from individual sequences and multiple sequence combinations with machine learning algorithms. In parallel, clinical models were established through univariate and multivariate logistic regression to identify significant predictive factors. A combined model incorporating both radiomics signatures and clinical characteristics was subsequently developed. The discriminatory performance of all models was evaluated by comparing area under the curve (AUC) values using DeLong's test.
resultsModel 19 (DWI+T2WI+ADC+CE-T1W+Clinical/SVM) achieved the highest AUC values, reaching 0.934, 0.922, and 0.846 on the training, validation, and external test sets respectively. In terms of clinical-radiological characteristics, alpha-fetoprotein (AFP) and cystic necrosis are significant predictors.
conclusionA combined model utilising DWI, ADC, T2WI, and contrast-enhanced T1WI imaging, alongside AFP and cystic necrosis, demonstrates high precision, adaptability, and robustness in distinguishing seminoma from non-seminoma testicular tumors.
Indexed as
Identifiers
42240645What 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.