ArticleCancers2024
Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06527027 (Comparison of the LN-RADS, RECIST 1.1 and Node-RADS Classification in the Assessment of Lymph Nodes in MRI and CT in Relation to Histopathological Results - a Prospective, Randomised Study), which is not on this map. Cited by 7 papers, 3 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.
Comparison of the LN-RADS, RECIST 1.1 and Node-RADS Classification in the Assessment of Lymph Nodes in MRI and CT in Relation to Histopathological Results - a Prospective, Randomised Study
Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic accuracy of node-RADS for the detection of lymph node invasion: a systematic review and meta-analysis.European radiology · 2025Pooled it
- Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- PET Imaging in Head and Neck Squamous Cell Carcinoma: from Staging to Response Assessment.Current oncology reports · 2026Review
- Article
- Artificial intelligence for diagnosing bladder pathophysiology: An updated review and future prospects.Bladder (San Francisco, Calif.) · 2025Review
- Ultrasound-based Node-RADS: Introducing a new Scoring System for Ultrasound-based Classification of Lymphadenopathy.Iranian journal of otorhinolaryngology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.
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.