ArticleEuropean radiology2024
An [
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed, 7 citations in OpenAlex.
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Cord serum metabolic signatures associated with neonatal anthropometry and neurodevelopment through age two in twins.iScience · 2026Article
- Prediction of Lung Cancer Metastasis Using Machine Learning Models Based on Clinical Laboratory Data.Cancer reports (Hoboken, N.J.) · 2025Article
- Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.BMC cancer · 2025Article
- A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer.Cancer medicine · 2024Article
- Hematological indicator-based machine learning models for preoperative prediction of lymph node metastasis in cervical cancer.Frontiers in oncology · 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
12 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesTo develop an [
methodsA total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [
resultsA prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively.
conclusionThe [ CLINICAL RELEVANCE STATEMENT: A machine learning model of KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [
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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.