ArticleBMC cancer2026
Radiological staging clinical decision support model for rectal cancer lymph node metastasis detection on MRI.
Article in BMC cancer, 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
4 authors.
Funding
Abstract
backgroundAccurate staging of lymph node metastasis (LNM) is crucial for personalising rectal cancer treatment. Lymph nodes (LNs) are the most common sites of rectal cancer metastasis, and malignant LNs are typically treated with neo-adjuvant radiotherapy or chemoradiotherapy (CRT) to reduce the chance of recurrence and distant metastasis after surgery. Radiological staging criteria, based on LN size, shape, and texture, are known to be subjective, and radiologists have an average diagnostic performance of 73% sensitivity and 74% specificity.
methodsThis study develops a fully automatic and end-to-end pre-operative radiological staging model for rectal cancer LNM using artificial intelligence (AI) methods. The model combines automatic detection of lymph nodes on Magnetic Resonance Imaging (MRI) with multiple instance learning patient-level lymph node staging. Models were trained and evaluated on an in-house dataset provided by Leeds Teaching Hospitals NHS Trust (LTHT) including 458 patients with pre-operative MRI scans, patient clinical data, and post-operative pathological TNM staging. Accurate detection of the lymph nodes on MRI is achieved using nnU-Net, and the classification results compare different 3D feature encoders, investigating a trade-off between performance and interpretability.
resultsThe results demonstrate state-of-the-art performance with cross-validated metrics of 0.828 AUC, 86.6% sensitivity, and 72.4% specificity. Clinical validation study results show that the AI staging model performance exceeded three expert radiologists in predicting post-operative pathology with a 9% higher F1 score.
conclusionsThis study provides strong evidence that an end-to-end AI model can significantly improve pre-operative staging of rectal cancer lymph node metastasis. The model outperformed expert radiologists and shows clear potential to enhance clinical decision making and improve patient outcomes.
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.